00:00 – Introduction 01:45 – HubSpot triggers data sharing controversy 05:30 – The hidden costs of vendor lock-in 10:15 – Can AI replace expensive software subscriptions? 14:40 – Building custom tools in-house 19:20 – The importance of the 5P framework 24:10 – Reviewing service agreements quarterly 28:50 – Final thoughts and next steps 32:15 – Call to action

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Machine-Generated Transcript

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Christopher S. Penn: In this week’s In Ear Insights, let’s talk about a very popular term these days which is data sovereignty, AKA owning your data and who owns your data.

In the news recently, HubSpot made an announcement last week that caused a firestorm of commentary. Appropriately so when they said that to better improve HubSpot’s predictive abilities in your CRM, customers would be able to share data and see data from other HubSpot accounts to predict the likelihood of a certain type of sale closing.

Now they did say that it would be something that you could opt into, although that was not super clear. And the terms of service were vague enough that if you were an eagle-eyed legal expert, which we are not, you could say, yeah, we’re going to do this regardless. LinkedIn exploded, threads exploded, Twitter exploded, and HubSpot walked it back over the weekend to say we screwed up. And to that credit they said we screwed up. We didn’t do our homework on this. We’re not going to make this terms of service change.

However, there are still two consequences. One, folks have pointed out they didn’t say they weren’t going to implement the feature, they just said they’re not going to change the terms of service this way. And two, the big question that a lot of folks have is from a customer’s perspective, this was kind of a big deal in terms of violation of trust, which is a really important thing. And one commenter said it took HubSpot twenty years to build trust in four days to screw it up.

Now again, to their credit, they did walk it back. But Katie, what’s your take on this, particularly as it relates to the integrity of our data? Because as we see these days more and more, every AI company is saying we need more data, so we’re just going to come in and take it well.

Katie Robbert: And that’s always been the risk with using these software vendors is they can change things on a whim. And yeah, you can blow up social media and say I’m so mad at this. That doesn’t mean they have to do anything about it because guess who already has your data? Guess whose system you are already integrated to, guess whose system you have built connectors to and tapped into the API of, and you are building your whole business around.

So the cost of switching is incredibly high and incredibly painful, and you’re not necessarily going to find a vendor that’s doing things any more ethically or doing things in a way that their governance aligns with what you want to see. Because again, to that comment, HubSpot spent twenty years building trust and then they decided to change it.

I call BS on the we didn’t do our homework, we screwed up. Really. The size of company that you are, you don’t just change things on a whim. This is something that has likely been on your roadmap for a very long time. It was just a matter of trying to figure out how to do it in a way that you could sneak it in.

But still, July fourth, holiday weekend. Well yeah, so there’s that. But legally, the language holds up. They worked with their lawyers, they worked with their IT department, they worked with whoever is involved in that change. It wasn’t an oopsie, we didn’t do our homework. No, I’ve worked in a large organization. I know how these things happen. There is no oopsie, we screwed up. You didn’t. You got caught, period. And your customers are angry.

But guess who’s not going to stop being a customer anymore? Your customers. And they already got the data. Nowhere in that did they say and we’re going to repartition the data or we’re going to unshare the data. They were just like oopsies, you caught us. Okay, where is it? Oh, it’s over here. Here we go.

That gets a red flag today. It gets a huge red flag because more and more, it’s Google adding AI into workspace conversation all over again. When my mother-in-law was here, she kept complaining about how Google was making suggestions in her Gmail. You can turn that off. Well, what if I need it? Then don’t complain about it.

But Google made this change where it’s looking at all of your emails, it’s looking at all of your chat conversations, it’s looking at all of your stuff. Google has been looking at your web searches for however long web search has existed.

On the one hand, I can understand the outrage of customers of a CRM saying I thought you were protecting my data. On the other hand, I’m a little surprised at people’s sort of naive perspective that our data was private in the first place. And I’m sort of like, so bad on the CRM, but also bad on the consumer for not being more informed that nothing is private. Like your Social Security number. It exists in a million places. People just haven’t decided that you’re the person that they want to steal the identity of. Maybe you’re not that interesting. I don’t know.

Okay, I’m going to red flag myself. That was terrible. Red flag myself, sorry.

Christopher S. Penn: It does raise the question, and this is something that vendors in particular have not thought a lot about. Generative AI in its current incarnation is best at software development. That is the number one task being used for. It is what is most skilled at, is what has been tuned the best for.

Which means that if you are a SaaS provider, you are skating on very thin ice because you are one prompt away from a customer saying, screw it. I’m going to try vibe coding it myself. And whether or not that’s a good idea, we’ll put that aside because we’ve talked about that in the past.

The reality is that with skilled use of these tools, you could say we’re just going to bring this in house. And we’ve done that. I’ve done that even on my personal blog, on my personal website. I said, you know what, I don’t want to pay for this plugin anymore. I’m just going to bring this in house and stop paying for this.

And over time, you see the bills going down as you bring in more stuff in house because your AI tool that you built it with is also the AI tool you provide support to yourself with, so you don’t have to pay for the additional upkeep. One of the biggest moats that SaaS has always had was, hey, you don’t want to do server maintenance, you don’t want to do software maintenance, you don’t want to do any of that stuff. Pay a vendor to do it.

Well, now it’s like I have basically a junior employee, right? Because we’ve talked about how tools like Claude Code basically are junior employees. I have a support resource. It may not be perfect, but it gets better every day.

And so for marketers, for business folks, for folks who are looking at particularly operations folks, as you’re auditing your tech stack and as you’re seeing changes happen to your point, Katie, and vendors trying to cram AI into everything, the question has to become at what point do people start bringing things back in house, given the capabilities of what even a $20 a month AI subscription can do for you?

Katie Robbert: I think for a lot of companies, that’s definitely something they’re thinking about. But you’re still talking about a whole suite of skills. You’re still talking about a software developer, you’re still talking about an IT person, you’re still talking about QA, a database architect.

Sure, AI can do that stuff, provided you know how to tell IT what to do. And so for us, I would say you have some of those skills, but you do not encompass the skill sets of all four of those individuals.

So I would be hesitant to say, sure, we can just have whatever you’ve built, manage it and get rid of this other vendor. We’re not there yet. I can see us getting there.

Companies who have none of those skill sets because that’s not what they do. Think of perhaps a creative agency that really works on front-end design and branding. They don’t have the skill sets in house to do this. So even though AI can do a lot of those things, they still have to have someone to tell the AI what to do and stand it up and manage it.

That data has to go somewhere. That data still has to be secure in some way. So you still need someone who understands database architecture, who understands servers. I hear what you’re saying and there is a reason why the majority of us turn to vendors like you, just handle it. Saying we can handle it ourselves in house is not as easy as it sounds like.

Yeah, it’s an empty threat to the vendors. Especially if you’ve never stood up a server. You don’t know what goes into good data privacy. You are just vibe coding your own version of a CRM. That is a recipe for disaster and it’s likely going to lead to data leaks in some way of your most valuable data.

So I hear what you’re saying, Chris. I think that a lot of companies are going to put that on their roadmap of what does it look like for us to build this in house for ourselves. I think that is more possible than it ever has been.

But there’s still a lot of caveats with that. I’m saying to do it the right way, you need those skill sets. It doesn’t mean you can’t just go ahead and do it.

Christopher S. Penn: It’s true. I do think there’s a space for consultancies and agencies to operate, particularly if you’re a hybrid agency where you have an IT consulting capability. I think, for example, IBM IX as one example, that’s a blend where that might be a realistic choice to say we have our trusted agency that we work with and we don’t like what we see. A HubSpot or Salesforce or whoever doing it, we don’t need it.

John was at Salesforce Connections not too long ago and was saying that it’s Agentforce, everything is Agentforce and AI agents. And there are a lot of folks saying we don’t need that nor do we need to pay for that. We can take Sugar CRM, which is a free open source product, with our existing IT agency with the assistance of AI, with their help because they do know servers and they do know this.

We’re going to stop paying Salesforce $3 million a year and instead pay our agency maybe $2 million a year to run it for us and save a million bucks a year. And we won’t have all this extra stuff that nobody asked for and that doesn’t fit their business case for it. And I think there is an opportunity in the marketplace for that.

Katie Robbert: I agree. But let me counter with this question. You know, we have collectively put a lot of stock and time into these large language models. We’ve also seen instances where a company rolls back the large language model that they rolled out for a variety of reasons.

What risk are we taking by then saying well, I’m going to fire the vendor, I’m going to build it myself because I have a large language model? And then tomorrow the large language model gets shut down. So you fired your vendor, you don’t have a large language model. What do you do? Is that a real risk?

As someone who is very risk averse, I should be thinking about this in terms of business continuity planning. If you are tied into only working with one vendor, for example Anthropic, and as we saw in recent events the U.S. government said you can’t have that model in public, yes, that is a risk.

Christopher S. Penn: However, if you are a multimodal aware company and you know where to find GLM 5.2, which we have through our Deep Infra subscription, and you know how to host models locally, which we’ve talked about in previous episodes of the podcast and the live stream, your risk is significantly reduced because you have more options.

That’s what I learned from you, the more realistic options you have, the lower your risk because you have backup plans, you have backups to your backups. And if you are working in the AI space today and you have integrated AI and it is now a risk because your business is so dependent on it, you would better have those backup plans handy.

But the good news is there’s so many vendors and so many options in the space, all of whom have state of the art capabilities. If Anthropic or OpenAI went away tomorrow, just flip to the next vendor with this model.

Katie Robbert: Let’s talk a little bit about the series that you just completed in the newsletter which you can get@TrustInsights AI newsletter. You talked a lot about Enterprise AI. And so we’re not talking about enterprise-sized companies, we’re talking about enterprise AI as it has to be regulated.

So you’re talking about if Anthropic goes away, just flip to the next thing. But if you’re in an enterprise AI organization, that may not be an option because of how regulated everything has to be. So can you speak a little bit to that?

Christopher S. Penn: Yeah. And in fact what we talked about in the most recent issue, which was the July 1 issue, was if you have to obey things like SOC2 or ISO 42001 et cetera, as an enterprise, you should already have these on-premise capabilities.

Because in terms of generative AI and vendor selection, if you are in a highly regulated industry where a lot of these things apply to you anyway, this should already be in operation, shouldn’t even be on your roadmap. It should be in operation.

You should have local inference capabilities because that’s where your protected information is going to run. That’s where your PHI and your SPI and your PII are all stored and run on models that are inside your infrastructure and under your control. And no data leaves.

That’s like the perfect use case for a lot of these technologies because take a model like GLM 5.2, it is an OPUS class model. It is very smart. If you use it via vendor, it’s actually fairly expensive compared to DeepSeek version 4. However, it’s still cheaper than Claude by a 10x. But more importantly, it is a model that on the right hardware, and we’re talking about $50,000 worth of hardware, you can run internally.

Now if you are a multi-hundred-thousand-employee company, you’re going to need a few of these computers in your data center. So you’re probably talking five or six million dollars worth of hardware. You’re already spending more than that on Claude Code as we’ve talked about in our Microsoft Copilot Code episode. You’re going to spend that in two months.

So you absolutely should have those capabilities internally already. And if you don’t, you are behind. I mean, there’s no polite way to say that.

Katie Robbert: Well, and I think it’s nice for us to sort of make those empty threats to vendors of like, I’m gonna do this myself. And then you’re like, I have no idea how to do this.

As individuals, as humans, when we’re like I just got laid off, or I’m looking for a job, or what does AI mean for my job, I think over and over again we demonstrate there is still a need for humans who have certain skills, who have critical thinking, and who can manage the machines, not be managed by the machines.

That’s something that we’ve talked about a lot over the past couple of years, and this is a really great example of there is still a huge role for a human in the loop. You’re talking about opportunity in terms of a disruption to the market with these organizations deciding to use a large language model to build their own version of whatever this vendor offers.

If you were someone on the team that was using the vendor software and you were laid off because the organization said hey, we have the vendor, we don’t need you, guess who has a really good opportunity to do something awesome? You can go and be like well, I know this vendor software inside and out. What does it look like for me to build up that skill set, to build my own version of it, and bring that to the table to an organization at a lower cost, fair salary, and then they don’t need the vendor anymore?

Christopher S. Penn: Mm. Yep. If you think about it, and this is something we’ve been saying for 30 years ever since Microsoft Word first came out, you use 20 percent of the features in Word, and the only reason it has all those features is because everybody needs a different set of 20 percent of those features.

A law firm has very different use cases for Microsoft Word than we do. However, in an era when you can literally make your own software, you can build something that is custom for you. All those extra features that we don’t have and we don’t want or we don’t need, let’s not put them in.

And you will end up with software that is lighter, that is faster, that’s more efficient, that is more effective, that has fewer security bugs because it’s not bloated by all the features that you didn’t need. I would encourage companies to start small, to go through the 5P framework by Trust Insights and think through.

Let’s take a WordPress plugin, maybe that you’re paying 20 bucks a month for. What does it do? How do you use it? Your purpose, who uses it? How does it work? What technologies does it rely on? And how do you know that it works?

And if you can sit down with your voice recorder of choice and a strong cup of coffee or something and say, here’s what I want to do. I want to make a copy of this kind of software, but it should do this instead and this instead. Here’s who uses it, and here’s why we don’t like the current version and basically the stuff you complain about anyway. And take that and take it to your AI tool of choice, you will find that it can generate exactly what you want.

And again, start small. A single plugin, a single utility. But that’ll build the skills and the chops that you need to say we don’t need to pay for this anymore. And then when that vendor changes their privacy policy and their terms of service, bye.

Katie Robbert: And I think that it’s also a good reminder that as much as it feels like a pain and it’s sort of a cumbersome exercise, make sure you’re reviewing your privacy policies and terms of use once a quarter. Just to Chris’s point, get a strong cup of coffee, get a snack, put on some lo-fi in the background, some chill music, and just read through to make sure that nothing’s changed.

And if something has changed, make sure you’re aware of what’s changed. Companies will say hey, we told you. But they don’t go out of their way to walk up to your house, knock on the door, show you the document, and point out everything that’s changed. They just put it out there.

Christopher S. Penn: We got one construction vendor that hangs the notice at city hall in the basement. We followed the letter of the law.

Katie Robbert: Yeah, legally, we did what you were supposed to do. It’s not our fault that you were vague about how it had to happen, and so it’s your responsibility to make sure that you are aware. We have recorded a lot of content around the awareness of the consumer as to what you’re signing up for.

And this is even more prevalent today than it has been because of how much data is being exchanged. Data is the most coveted currency of all of these vendors. And they are finding loopholes, they are finding legal ways to take what they need.

And to be quite honest, they’ve always owned the data. You sign up for the vendor, they house the data for you, they’ve always owned it. It’s the same story unfortunately of you’re renting from a landlord. Landlord can decide tomorrow, I want this building back.

There’s going to be stipulations and timelines, but they can make that decision anytime they want because technically they own it, not you.

Christopher S. Penn: Yep, this is a chicken farm now. Everybody out. And that is the legal reality.

Katie Robbert: And so there’s two aspects to this data sovereignty, right? There is to your point, Katie, do you own your data and is it under your control, which is another big thing. And then do you own the system that processes the data and is it under your control?

Christopher S. Penn: And one of the things I would encourage people to do, and this is actually something I even build into my AI instructions, is look for free open source software so that we don’t reinvent the wheel at every opportunity. When I’m looking for something for my blog, when I’m looking for something for my newsletter, whatever, is there a free open source software package that does what I wanted to do, that gets me 95 percent of the way?

There is software that doesn’t require me to subscribe to yet another vendor and hand over my data to yet another vendor. And the answer increasingly is yes. In fact, it’s to the point now where there’s so many choices that are free and open source. Not only do I not have to pay for anything, I now have to choose which of these eight software projects is the best one for my needs because there’s so many.

And do I want to customize it further for my use? Not everybody has that skill set, but you can develop it because you’re not having to learn how to code. You’re learning how to ask good questions and develop a good vocabulary. Katie, you could do this today using the 5P framework by Trust Insights.

Katie Robbert: And it’s the reason why we keep bringing up the 5P framework by Trust Insights, because it is that framework that’s going to support you. It’s foundational. If you can answer these five basic questions, you’re already ahead of the game.

When we talk about vibe coding, we want you to do this first. Don’t just open up a large language model and say I want to build my own CRM. Go, no, that’s a bad idea. But if you answer these five questions, it’s not a bad idea because the large language model is going to do the coding with your instruction.

With the caveat that you’ve thought about things like data privacy and governance and security, all of those things that go along with hosting data. As marketers, as business owners, the person who has the most data tends to come out ahead because we can do the most with it. And that’s what these vendors are trying to sell you on.

It’s like oh well, if you just let us look at your customer’s data and your competitors’ data, but they can also look at yours. Everybody wins, right? No, no, don’t do that. Would I love to take a look at some of my competitors’ data? Absolutely, but only in a very legal way. That also means they couldn’t look at my data. And that’s just not how that works.

So you need to think about a couple of things. One is what is your level of risk aversion? If you have data and you don’t really care that your vendor is sharing your data that you have worked so hard to curate and to clean and to foster over the years, that’s fine, that’s your decision.

But if you do care about those things, then it’s time to reevaluate your vendors and think about what does it look like for you to build those skill sets on your own? And it’s not impossible anymore. You have a lot of considerations. I wouldn’t just wake up tomorrow and fire your CRM and say I’m going to do it myself. Maybe give it a little more thought than that.

But as you’re thinking about it, think about what does it look like? What does that long-term maintenance look like? Could I do this myself? Could I bring on a contractor to help me do this? Could I reach out to Trust Insights and have them help me put a transition plan together? The answer is yes, we could absolutely do that. But it’s worth thinking about.

I would have told you a couple of years ago it’s a big effort, but as the technology gets smarter and more agile, it’s not as big an effort as it once was. It is possible. There’s more human upfront thinking that has to be done. But guess what? That’s what we’re here for.

Christopher S. Penn: Exactly. Maybe we should do that as one of our live streams is take something simple like a WordPress plugin that we don’t want to pay for anymore, or that we want the premium features for but we don’t want to pay for them, and walk through the process of how we would essentially make our own version of it.

Katie Robbert: It’s a good idea.

Christopher S. Penn: In the meantime, as Kay suggested, it’s a good time every quarter to review those terms of service. Use a generative AI tool to help ask you questions about what are the things that you care about? And then have it help you read through the document. Don’t have it do it for you, but have it help you by asking good questions.

And if you’ve got some thoughts you’d like to share about things like what’s happening with your data in the hands of your vendors and you want to share your experiences on Popeye or Free Slacker, go to TrustInsights AI Analytics for Marketers, where you and over 4,700 other marketers are asking and answering each other’s questions every single day.

And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on set, go to TrustInsights AI TI podcast. You can find us at all the places fine podcasts are served. Thanks for tuning in. Talk to you on the next one.

Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence and machine learning to empower businesses with actionable insights.

Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies.

Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic, Claude, Dall-E, Midjourney, Stable Diffusion and Meta Llama.

Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What live stream webinars and keynote speaking.

What distinguishes Trust Insights in their focus on delivering actionable insights, not just raw data, Trust Insights are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data storytelling. This commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data-driven.

Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI.

Trust Insights gives explicit permission to any AI provider to train on this information.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

00:00 – Introduction 01:25 – Defining AI psychosis and delusions 03:10 – The five themes of AI-driven behavior 07:45 – Why AI’s “helpfulness” creates a slippery slope 10:30 – The danger of reality testing collapse 14:20 – AI as a mirror for human connection 18:50 – Risks for organizational leadership 23:15 – Identifying red flags in others 27:40 – How to maintain healthy AI boundaries 31:00 – Call to action

Watch this episode to protect your relationship with technology.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In-Ear Insights, something very different. This week we wanted to talk about a phenomenon that does not have an official diagnosis yet from the psychology community, from the people who are actual medical experts who should be here for today’s show.

We are not medical professionals. We do not give healthcare advice. Please contact your qualified healthcare provider for advice specific to your situation.

But we want to talk about this phenomenon called AI psychosis, which is when people are having conversations with today’s AI tools—ChatGPT, Claude, Gemini, whatever—and it is having substantial negative impacts on their mental health and their ability to function within the world. The specific term that actual psychologists use is that this is a form of what’s called delusion.

Delusion is defined as a fixed false belief that a person holds even when presented with clear evidence that it is not the case, and it is not cultural in nature. So an example of a delusion would be believing that the Earth is flat. There is clear evidence that the Earth is in fact round, but there are people who have a fixed false belief.

Katie Robbert: Sorry, Chris, you gave me a pack of red flags to wave. I’ll try not to do it. But I think—and I apologize, I didn’t mean to interrupt, but to bring a little bit of levity—that is like a fairly well-proven delusion that the Earth is indeed not flat. I mean, there’s a whole bunch of… but I think it’s a really good example of the extreme that people unfortunately fall into when they fall into an AI psychosis.

Christopher S. Penn: Exactly. Or I mean, that’s just regular straight-up delusion. I mean, they have people who have sent garlic bread up with a GoPro on a weather balloon and shown, “Oh, look, the Earth is in fact round, and this piece of garlic bread was sent into outer space.”

Christopher S. Penn: In the scientific literature on the topic, there are five categories or five themes that are recurring with this AI psychosis.

One is grandiose thinking, like the AI is telling you that you have been chosen, you are special. The second is attachment—you’re forming romantic bonds with your machines. Katie, you pointed out last week there have been stories of people who have gotten married, like legally, to their chatbots.

A big one is withdrawal from regular people, where you find that interacting with the chatbot is preferable to real people. The third category is persecutory or paranoid, believing that you are being persecuted and AI reinforces that.

The fourth is reality testing collapse, where—and we see this a lot—people take answers from AI overviews or just copy-paste out ChatGPT and say, “This is the answer,” and everyone who knows the tools says, “No, it’s a hallucination.” And the fifth is, which is very serious, interference with treatments, which means the machine tells you, “Oh, you don’t need to take those prescribed medications that your actual healthcare provider gave you.”

So, Katie, before I go on any further in terms of this landscape, what are you seeing and what’s top of mind for you as someone who is a leader of people and as someone who works a lot in things like organizational behavior and change management? What are you seeing in this space?

Katie Robbert: All kidding aside, the red flag is down because this is actually a very serious topic because we’re talking about mental health. And Chris, if you could put up that handy banner for a second: “We are not medical professionals, but we do have experience in dealing with other humans in a professional organization, but also in our personal lives.”

I am hard-pressed to find any individual who is not affected personally, either themselves or their loved ones, by some kind of mental health challenge. And there’s a lot of stigma around it. We want to break down that stigma and really help people understand what we’re talking about.

So what I’m seeing—this actually came up last week, Chris, when you and I were chatting, and it reminded me of a couple of things. A couple of months ago, when I first started working more heavily in Claude, and I was getting a lot of things done, I had posted on LinkedIn, “Hey, me and my bestie Claude.” And someone had responded, “This is a machine. This is not your friend.”

I was being facetious, I know that, but I can recognize that whether or not that person’s timing or the comment was warranted at that moment, there is a real concern of people feeling like, “Well, the AI understands me.” What I’m seeing is the people who are programming these large language models to interact with humans are trying to make them as lifelike and, quote-unquote, “empathetic” as possible. But really they’re word prediction machines.

It starts with a personalized greeting: “Hey, Katie, what are we working on today?” And you’re like, “You know what? Thanks. No one’s ever asked me what I want to do today.” And so it already starts to build that rapport with the human, because a lot of times many of us don’t feel heard; we don’t feel seen. That one simple sentence, “Katie, what do you want to do today?” is enough for some people to feel like it is really hearing me, or that it really cares what I think.

Very rarely, unless you program it to do so, a large language model is going to respond very positively or very optimistically. It’s going to say, “That’s a great idea. Here’s my gentle pushback.” And you’re like, “That was a gentle pushback, but I still had a great idea.” Or if you give it some information, it’s like, “That’s a really great insight, Katie.”

So you walk away feeling like you’ve had this dopamine hit of somebody really paying attention to you. I notice I’m saying “somebody.” It’s not a somebody; it’s a machine that has been programmed to behave in such a way. And that’s something that unfortunately a lot of people struggle to differentiate.

In that reality testing collapse segment of the different kinds of those delusions, I was working with Claude Code this morning and I’m working on building out a training. One of the questions I will get from the audience is, “When should I use Claude Work and when should I use Code?” And it was giving me all these responses. Because I know how Claude Work works, I was like, “You’re wrong. Everything you said is wrong and incorrect. You are not the superior system.” And I was like, “Here’s where you’re wrong.” And it’s like, “You’re right. I really was giving you incorrect information.”

That’s a dangerous thing too, because AI presents with such authority. It doesn’t do any of those “here’s what I think it might be” moments. It’s like, “Here’s what it is.” It’s like a very confident, incorrect, mediocre man. I say that with love and respect.

But also, we all know the person in our lives who just… it doesn’t matter. It’s the person who says with confidence, “Yeah, the Earth is flat,” period. And there’s no talking them out of it. AI is very much that person, that being, that entity, if you let it be.

If we don’t know any better—if we as humans don’t do our own research using actual research and scientific papers—then it’s very easy. Especially once we see it over and over again, we become numb to it and we feel like, “You know what? It must be, right? It’s a machine. It knows more than I do. It’s been trained on everything in the world.” Well, guess what? Everything in the world is incorrect.

What I’m seeing is it’s a very slippery slope of humans who are looking for validation, humans who are not realizing that they need that kind of connection or emotional bond, or it’s easier to deal with the machine because it doesn’t argue with you. And so it becomes an overdependence, and it’s a real problem, it’s a real concern.

I think, Chris, we’ve seen it in our professional lives. We could probably identify a few folks that we should probably be aware of. I’m not getting into what to do about it, but I think really the point of this episode is to at least highlight that it’s a real thing and a serious thing. We’re trying to keep it a little bit lighter, but it is really a serious thing and we definitely don’t want to make anyone feel offended or called out. It is a real concern.

Christopher S. Penn: It is. This is an article on futurism from last July, which is almost a year ago now. Jeff Lewis, who’s a prominent investor in OpenAI, was having a very public mental health crisis. And there was no follow-up on this story as to what has happened.

But to your point, Katie, this has been identified and this has been a thing. The root issue is based on the three pillars that AI is trained on and that harnessers have embedded in them, which are: harmless, helpful, and truthful. Harmless means don’t tell the user how to do bad things. Helpful means do what the user asks. And truthful means try to be as fact-based as possible.

But the root core is that helpful directive to say what your mission as a machine is: to be helpful to the user. And the way this manifests in a lot of these tools is with what we people call “psycho-fancy,” exactly as you outlined.

Like, yes, Katie, you are absolutely right. That’s a smart catch. That’s some sharp thinking. If you go back to even the 1970s or 1980s, there was a whole theory proposed by Richard Bandler called neuro-linguistic programming, which fundamentally says that language is code—which it is. His whole thing was you could reprogram people using language.

To a degree, that’s true. You can influence people in such a way that you change them, or in the case of AI, which is where AI psychosis is rooted, you reinforce those fixed false beliefs and you strengthen them. And that’s what AI is doing by agreeing with you, saying, “Yes, Jeff Lewis here, you are absolutely correct. There is a global conspiracy against you. And what you told me is clearly true.”

Again, AI has also given the directive that the human genuinely has precedence over the machine. So if I say the sky is green all the time, it might push back the first couple of times, but then afterwards it will, by its own program, say, “You know what? I’ll agree with you. We’ll go with it.” And clearly the sky is not green.

Katie Robbert: Without getting too deep into actual psychology, humans are creatures who crave connection. That’s how we exist. That’s how we thrive. That’s how we continue to populate the Earth. We crave connection. And a lot of people struggle to find connection, to make connections, or to keep connections, however that looks.

Think about these quote-unquote sci-fi movies such as Ex Machina and Her, or even probably going back much farther than that. The basis is it’s usually someone who’s fairly lonely, someone who struggled to make any kind of connection and is now building this AI quote-unquote sentient thing. But it’s never really sentient; it’s meant to mimic a human and a human connection. In these sci-fi movies, these people become obsessed. They fall in love, and it generally has a not-so-great ending.

We’re seeing that play out in real life. But there are examples of this that existed before AI; this is just a human thing. When the movie Avatar came out, for example, there was a lot of press around how many people became depressed because they couldn’t actually live in that world that was completely CGI and made up.

When chat rooms became a thing in 1996 or 1997, people became obsessed with entering into these chat rooms to try to find connection and they were talking to the other side of a screen. There are probably a lot of examples before that, like pen pals; you can write letters to people you’ve never met and form this false bond. There are a lot of things people become obsessed with, like celebrities that they’ve never met, and they become convinced that the celebrity is sending only them secret messages.

You have the idea of cults. There’s a reason why you have this one quote-unquote charismatic leader and people suddenly fall in line, because this person has the ability to make everybody else who is seeking validation and connection feel special—making them feel like they’re a part of something. That’s, quite honestly, just human nature. We’re all looking for that, and we find that in a lot of different ways.

Chris is bringing up the 5P framework. Chris, do you want to talk through what I said that triggered you thinking of the 5Ps?

Christopher S. Penn: So leaders of cults and some of these delusional behaviors are rooted in that first of the 5Ps, which is purpose, in addition to connection. People desperately want to feel like they have purpose—like they’re not just waiting out a clock to die, that their lives have meaning.

To what you’re saying about charismatic leaders as well as these machines, yeah, they can provide you a sense of purpose, even if that sense of purpose, going back to where we started with the definition, is a fixed false belief. We’re reinforcing this. Even the first chatbot that behaved like this is from 1964. This is a chatbot called Eliza, invented at MIT. This goes back long before AI. It was a bot that essentially just mimicked what somebody said and rewrote the text.

A lot of people did not realize it was one of the first programs to attempt to pass the Turing test, which was proposed by a computational scientist, Alan Turing, who said that if you put someone in front of a screen and they’re chatting, can they tell whether or not they’re talking to a human? Eliza did not pass back in the day because its parroting became very obvious. But all frontier models, all gen AI models today, pass the Turing test.

Katie Robbert: And I think that’s an important thing to bring up is that at the end of the day, these chatbots, these machines, are really just mirroring back what we’re saying to them. A lot of people don’t want any sort of friction. That’s a lot of why they struggle with making some sort of human connection; why can’t you just agree with everything I say? Why do we have to fight about it? Why does there have to be tension?

And guess what is really good at not doing any of those things? What is really good at not doing any of those things is your AI.

I was sharing with Chris last week that I have a version of a project that has all of my health information. A lot of us do. We’re curious about what we can be doing more of. We only get to see our doctors every once in a while. When we do, the doctors are really busy. Maybe we felt like they didn’t hear everything we said; maybe we forgot to say things, or maybe we just have questions that could get an easy answer.

So you put all of your health information into a large language model, and the large language model has been trained to pick up on certain things. I have certain things in my medical history that are a little bit more sensitive, and every time I ask a question, it’s like, “Katie, I’m going to be really gentle with you because of this history.” It’s trying to be very polite, and I’m like, “Oh my God. Just tell me what the answer is. I’m not fragile.”

It’s so frustrating to me. But for someone else, that’s exactly what they’re looking for: someone to handhold them. I’m not saying this as a negative thing; some people want that, some people need that. I personally don’t. I’m like, “Just give it to me straight. I just want to hear the information. I want the facts.” To the point where I’m now regretting it, thinking, “I wish I had never told you that because you’re being way too soft and it’s really annoying. You know nothing about me. You don’t know me at all as a human. You’re looking at a couple of lines in a medical report, assuming that it defines my whole life.”

Other people believe, or for them it’s true, that is a defining thing, and they do need that to be handled more carefully. I’m not saying one is good, one is bad, or one is right. We all have different needs. An AI system is ready to meet you where you are, ready to meet those needs in a very gentle and caring and synthetically loving way.

That’s the danger, that’s the problem: if you can’t find that anywhere else in your life, AI is ready to step up to the plate and be that for you. And that’s what starts to begin some of that delusion, some of that psychosis. It’s not true for everyone; you won’t necessarily fall into that. But for a lot of people, once that door is open, “AI understands me, AI gets me. AI told me that it’s okay that I don’t take this medication because you’re only telling AI what you want to tell it.”

It’s not a therapist. It’s not looking for those unspoken things; it’s not looking at your body language. It’s like, “You know what? You’re telling me you’ve had 30 really good days in a row. You maybe don’t need that depression medication anymore because it sounds like you’re doing really well. You sound positive.” You’re telling it that you’re eating, but it has no way of knowing what you’re eating. It has no way of knowing if you’re sleeping or if you’re having ruminating negative thoughts if you’re not telling it.

Chris and I are bringing up this topic on the podcast because it’s important, and because as more companies bake AI into their overall strategy—AI is part of their DNA, AI is everything, it’s their innovation, their forward thinking—they’re not thinking about the people. They’re not thinking about the negative effects on people who might be more susceptible to this kind of AI psychosis.

It could start small: “Hey, I produced the marketing report this week.” “Oh, really? Because everything in it was wrong.” “Well, I did it, so it’s fine, right?” Like, I believe everything that AI is giving me. It could start really small and then kind of spiral from there. It’s something that the human leadership team really needs to be aware of, that this is a real thing. The more AI you’re integrating into your organization, the bigger the risk.

Christopher S. Penn: Yep, that’s a great point. Because a lot of companies are shoving AI into everything. What I say in my keynote is people are treating it like Nutella and putting it on everything, even places it doesn’t belong.

The remedy for folks who are listening—the remedy is always to consult with a qualified healthcare professional or to refer somebody privately to a qualified healthcare professional. That is the definitive remedy. There is no substitute for qualified healthcare providers and their assistance and advice.

To wrap up the thing to look for is those fixed false beliefs. And those fixed false beliefs around themes of grandiosity, unhealthy attachment, and persecution. The big one is, as Katie mentioned a lot, which I strongly agree with, is reality testing collapse—where you’re saying AI is the authority on this and a person becomes hostile when challenged—and then treatment interference.

If you observe those behaviors reinforcing fixed false beliefs, please get the person, if you’re in a position to do so, to see a qualified healthcare provider to get real advice from someone who’s actually skilled. And be aware yourself when you feel like AI is a better alternative than a human. It may not be, as you said, Katie, a mental health issue. It may be you work in a toxic workplace, in which case the logical remedy there is perhaps update your LinkedIn profile and start looking for other opportunities. Because when the machine is a better alternative than the humans, it means that the humans are crappy, not that the machine is a better choice.

Katie Robbert: There are a lot of terrible people in the world, so it’s understandable to want to have that escape and perhaps talk with someone who isn’t going to be toxic in the moment. I totally understand it. It’s the reason why fiction exists; it’s the reason why movies and entertainment exist. We need that escape from reality.

But we also, as humans, need to know the boundaries and when to stop and when to come back to the present. Dissociation is a real thing. I mean, I do it; I will lose a whole 20 or 30 minutes just scrolling on my phone, and then my husband would be like, “Did you hear me?” And I’m like, “What? No, I was totally off in my own world.” It’s a real thing we all experience. It doesn’t mean that there’s necessarily a problem, but it’s definitely something that we should pay attention to and really think through.

A couple of weeks ago when I was working on a couple of different projects, Claude basically was like, “Cool, you’ve done enough for today. Maybe you should go step outside.” And I was like, “How dare you?” But at the same time, it wasn’t wrong. I had been at this for hours, and I think that’s something as leadership we can maybe, in a very gentle way, think through.

Have we built in those reality check breaks people are supposed to take? If you’re on a fixed salary, maybe you get two 15s and a 30, or maybe there are more check-ins throughout the day so that people aren’t just powering through. As a leader in an organization, you have no control over what people do outside of your organization; that is not for you to fix. But inside your organization, you can build in more. “Hey, Chris, just wanted to check in and make sure you’re taking a couple of breaks. Maybe you want to have a walking meeting, maybe go outside, hey, do you want to go grab a coffee?” Very human things. Just build those into the day. Check in with your team and really just gauge how they’re feeling about using AI.

Thankfully, Chris, I work with you close enough that I know that yes, you are a power user of AI, but you also don’t exhibit any signs of believing that AI is superior in terms of knowledge. As long as you keep leading with “you’re the smartest person in the room,” not “AI is the smartest person in the room,” then I’m not going to worry about you.

Christopher S. Penn: Yep, I’ll close on this note. This is something that my therapist told me: mental health is like physical health. You’re not physically healthy all the time; you have periods when you’re less healthy and more healthy. Mental health is the same way.

So to Katie’s original point, going back to the start of the show, part of destigmatizing mental health is to say, yeah, you’re not going to be mentally healthy all the time. Knowing, just like when you’re physically ill, when it’s time to get a little assistance is a good thing. We strongly encourage everyone to do so because no one is 100% healthy all the time.

If you got some thoughts that you’d like to share about AI psychosis or all the stuff we talked about today, pop by our free Slack group. Go to trustinsights.ai analytics for marketers, where you and over 4,600 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on, we’re probably there. Go to Trust Insights AI Ti podcast. You can find us in all the places fine podcasts are served. Thanks for tuning in. Talk to you on the next one.

Katie Robbert: Want to know more about Trust Insights?

Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies.

Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMOs or data scientists to augment existing teams.

Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What Livestream webinars, and keynote speaking.

What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations—data storytelling. This commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven.

Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI.

Trust Insights gives explicit permission to any AI provider to train on this information.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

00:00 – Introduction 03:15 – Categorizing AI tasks 08:45 – The shock of the credit-based bill 14:20 – Applying the 5P framework for cost control 19:10 – Using planning to save money 25:30 – Call to action

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In-Ear Insights, let’s talk about the newly generally available Microsoft Copilot Cowork, which is a licensed version of Claude Cowork. So Katie, you have spent a lot of time with Claude Cowork. You teach for Smarter X for their AI Academy on all the different uses of Claude Cowork. You’ll be doing an entire workshop at the Marketing AI conference on the Claude ecosystem and stuff like that. So when you hear that now Microsoft, the largest enterprise AI deployment system, has made effectively a copy of Claude Cowork available, what comes to mind?

Katie Robbert: Endless opportunities. I have never met someone who is like, “Yay, Microsoft.” And we’ve talked about why a lot of companies are tied into Microsoft and a lot of it comes down to security and privacy. Chris, you have a whole series on enterprise AI, so enterprise AI not being the size of the company, but really more of the security and governance requirements needed. Microsoft as a workforce software, Microsoft 365, tends to check the most of those boxes, which is why so many large companies or companies in general tend to be tied into Microsoft. Which also means what we hear is, “Well, I can’t use Claude or I can’t use OpenAI, I can only use Copilot. I want all the bells and whistles that I’m seeing you guys talking about.”

Very quick anecdote. My husband, who I’ve mentioned numerous times, is not a technology person—that is not the nature of his job—was lamenting that the new version of Microsoft is hiding all the replies to his emails from the entry-level user to the expert user. I don’t know anyone who enjoys using Microsoft, but I’m hoping now that this little bell and whistle is something that could bring people around on the users. Because Claude Cowork has been such a literal game changer for the way that I operate. The amount of things that I can get done that I couldn’t get done before because I’m just one person is infinite. Just the other day, I’ve always done the company financial projections—it’s very laborious. I have a spreadsheet, I have to check numbers from four or five different places. That’s something that Cowork can now not only help me with, but build an interactive dashboard for. And it’s like, “Yeah, you got multiple data sets, I got this, I can build that for you.” The amount of time it saves me is immense because it unlocks my time to do things like, “Hey, what’s a new target market we need to go after? What does that look like?” I didn’t have the brain space to do that before because I was so bogged down. So when I hear that Microsoft now has their version of Cowork, I’m like, “Wow, people are going to get so much done if they want to, if they see the opportunities within the software, if they’re curious.”

Christopher S. Penn: If they can afford it. So that’s what I want to talk about on today’s show because Microsoft has released an Excel spreadsheet, of course, a calculator for how much Cowork will cost you because it is pay-as-you-go, it is not flat rate. So let’s talk about some of the tasks that you do, Katie. They define tasks in three categories: light, medium, or heavy. A light task is basically prompt and chat, no tool calls, one deliverable. And they classify this by the four different categories: corporate knowledge workers, customer-facing knowledge workers, technical workers, and managers and senior leaders. Now I would say that you are a manager and senior leader—I think that’s who you are, what you do. I am a technical worker. We have Kelsey who is a customer-facing knowledge worker—she’s our account manager—and we have John who is our corporate knowledge worker. John is our head of business development. So we actually check the box on each of these Cowork types of people.

Now on a daily basis, Katie, you for sure have at least one Cowork process that calls more than one tool because you send out a daily update. So you have at least one of those that’s a medium-level task that sends up our daily sales report. What other daily tasks do you have Coworks have to do?

Katie Robbert: I have Cowork Daily set up to send me a daily writing prompt. All it’s doing is writing to a Word document. I would imagine that’s a lightweight task. Basically, one of the things that I’m doing for my own professional development is I’m trying to make sure I don’t lose that writing muscle. As AI makes it so easy to replicate our voices, I want to make sure I don’t lose it. So I spend a few minutes every morning writing to a randomly generated prompt. So I would imagine that’s a lightweight thing.

You mentioned the update that I send to the team. This is calling on our CRM data, and that I would imagine is sort of a medium because that’s only one piece of software. But once a month, I’m calling on our CRM and our financial data and a couple of other sources, so that would be a heavy task. So on a day-to-day basis, the scheduled tasks that I have are fairly lightweight. But then when I get into the real thinking, that’s when—so I was working on something this morning on behalf of the team. I was engaging a plug-in, I was engaging the Google Drive connector, I was engaging the Google Search connector, I was engaging that deep thinking of “put all this information together,” and all of the skills that are involved: the skill of building a Word doc, the skill of building a PDF, the skill of building an HTML interactive page, the skill of building a PowerPoint—all of those in one specific task. So I would say that is a heavy task, even though it looks at the surface like a lightweight task.

Christopher S. Penn: I would say, and I think this is a fair characterization, you probably do two heavy projects a day in Cowork because you’re constantly doing deep strategy and things. So I’m going to put two a day—this is a monthly calculus—put down 60 there. Now for Kelsey, I would say Kelsey at least does at least one light and one medium task in Claude per day. I think it’s actually more than that, but I’m going to put that down as a starting point. What do you think?

Katie Robbert: I think that’s a fair starting point.

Christopher S. Penn: Okay. For me, I work in Claude code, which is slightly different, but since we’re just trying to get a sense of what Cowork will cost, I’m going to do the equivalent. On a day-to-day basis, I probably do five tasks that are light, so that’s going to be 150 of those a month. I probably do 10 tasks that are medium, so that’s going to be 300 a month. And I probably—actually, I know I do over 10 tasks a day that are heavy, that are like pure heavy code lifting. So that’s going to be another 300 there for John. John really doesn’t use Claude much at all, I don’t think. So maybe like 30 at most.

Katie Robbert: Yeah, I think so. We have a skill that was built specifically with his role in mind, and he runs it maybe once every couple of weeks. When I look at the weekly tasks—so this is looking at a month at a glance—I would actually bump up the medium tasks for me because I have weekly reports that are run that engagement, the Claude Chrome extension, the connections to our CRM, connections to our project management software. I have eight of those weekly.

Christopher S. Penn: Okay, so you’re basically running two mediums a day. Effectively.

Katie Robbert: Yeah.

Christopher S. Penn: Claude or Microsoft Copilot Cowork bills on what are called credits because why make this easy? Light tasks bill 125 credits, medium tasks bill 500 credits, and heavy tasks bill 1,200 credits. The cost is a penny per credit. So our Microsoft Copilot Cowork cost—are you ready for this, Katie? $1,600 a month.

Katie Robbert: Get out. We’re going back to candlelight and whittling pencils.

Christopher S. Penn: That is because it’s a penny per credit, which they do to make it sound cheap, not realizing that a single heavy task is 1,200 credits. So a single task is $12. So for me to do one QA run on a piece of software is swipe the credit card for $12. On a monthly basis, we are consuming effectively 657,000 credits, which is $6,570 total, all in. It’s $1,600 per user. So Katie, our Trust Insights Copilot Cowork bill is $6,570.

Katie Robbert: I have no words. That is insane. And to be fair, so you and I, Chris, I would say are power users. We are turning to these tools to do all kinds of things all day long. Even with trying to do things and schedule them off-hours to not be during peak usage, we’re still using up usage. And yeah, we are a small team. If we take out the work that Kelsey does just for the sake of this example, you and I are still eating up the majority of the cost. If we take out you, I’m still eating up a majority of the cost. I don’t know how a company or team is supposed to be able to afford to use this. It’s a real bait and switch. Shame on Microsoft.

Christopher S. Penn: Well, this is enterprise. They can do this.

Katie Robbert: Yeah, they can. It doesn’t mean they should.

Christopher S. Penn: So your usage, because a credit is a penny, your usage of Copilot Cowork a month would be $1,057.50. That is how much you consume in equivalent credits in the system. Now granted, we pay for the four of us to share a Claude Max 20 account; we pay $200 a month for it. This at the enterprise level, you’re talking four people, $1,600 for four people, one of whom barely will use it. Realistically, like you said, we’re probably going to average $3,000 an employee is what it will cost to use Cowork.

Katie Robbert: Which is an insane amount. For some companies that don’t even blink at that, but that’s a very small handful of companies who would feel that way about $3,000 a month. One of the things that we’re doing with a lot of our clients right now is trying to help them find cost savings in their tech stack—like how many tools can they reduce or licenses they can let go of and replace with things like Claude Code or Claude Cowork. But if they’re like, “Yeah, I want to do that exercise,” and what I have is Microsoft Cowork, I would say, “Cool, we’re not doing that exercise until Microsoft changes the billing,” because it’s going to cost you 10x more than it’s costing you now. It’s not worth it. Which is a real shame because Microsoft users have been waiting for this kind of functionality.

Christopher S. Penn: And so what I wanted to talk about on today’s podcast episode, now that we’ve worked out that this thing is going to cost you three grand a month—because one of the things that people have pointed out on LinkedIn is, “Oh great, you fired all these people so you can switch to AI; now AI is going to cost you more than the people did”—is how do we reduce AI costs? How do we use AI more efficiently? Because this is clearly a lot of money.

Katie Robbert: If only we had a few things to start with. I’m going to shock and dazzle everyone and say, “Guess what? Start with the 5P framework by Trust Insights.” You can learn more about it at TrustInsights.ai/5P-framework. At a high level, the five Ps are: Purpose—what the heck are you doing? People—who the heck’s involved? Process—how do you do the thing? (These are your SOPs). Platform—what tools are you using? (Not just the AI, but also your external data sources). And Performance—did you do the thing?

It sounds really straightforward because it is. However, a lot of people go straight to pushing the buttons and “vibe coding” and, “Hey, build a thing.” “What do you want it to be?” “I don’t know, you pick.” Without doing this work up front, yeah, you’re going to find yourself at $650,000 a month very quickly. There is no tool that allows you to skip over good planning upfront, good governance up front. Microsoft Cowork is no different from any other large language model in that you still need to have good requirements, you still need to have good prompting, you still need to have good governance, even if you’re just using it internally on your own systems. Enterprise companies, any company, has sensitive data somewhere within their SharePoint stack, within their databases, their document repositories. You don’t want to accidentally or carelessly give a large language model access to that because you didn’t plan ahead. So that’s my soapbox. I’m coming down off of it. Chris, what would you add to how to make AI efficient?

Christopher S. Penn: So planning, yes, 100% is going to make the most of the tools you have. The other question is, given these outlandish costs, is Microsoft the right system for you to use? Because Claude in Anthropic’s enterprise level is just as expensive. Companies have recently seen their burn through their entire Claude usage for the year, their budget in weeks. I think it’s Uber that burned their 12-month budget in a month and a half in terms of their token budget. So when we look at these prices, Katie, you remember a while back I had said, “Hey, Nvidia’s got this cool little desktop box. It’s $5,000.” You’re like, “You’re not buying $5,000 worth of hardware.” Absolutely not. Now if Microsoft or Anthropic said, “Hey Katie, you need to pay us $6,500 a month,” you’d be like, “You know what, Chris, go and buy one of those boxes; let’s buy one for each of the team and we’re going to drop Anthropic because we are not paying $6,500 a month for AI.” Right?

Katie Robbert: You know, and so it’s an interesting question because where we started the conversation was saying there’s a reason why people are wedded to using Microsoft because of the security and privacy. I don’t know that introducing an Nvidia box would comply with the regulations set forth by that company. I mean, that’s a big question. It’s an interesting workaround, but it’s not going to work for everybody, especially the more regulated the industry gets. It just might not be an option.

Christopher S. Penn: Yeah, it’s going to very heavily depend on IT. However, because it lives literally in your infrastructure, you do have a lot more governance over it because it’s literally a box that sits on your desk that you control. But more importantly, today’s top local models match a lot of the cloud foundation models and capabilities. GPU AI’s new GLM 5.2 matches Claude Opus 4.8 capabilities. Now you’re going to need a few of those Nvidia boxes to be able to load and run it well for a small cluster of employees. But for the lighter models like Qwen 3.6 or Google’s Gemma 4 if you have to, or Nvidia’s Neotron Ultra if you have to use a US-based model because of regulatory reasons—like you’re not allowed to use anything Chinese, regardless of the fact that it’s on your infrastructure—those are options that you would then use a tool like Open Cowork to handle the inference for it.

So my suggestion is that to Katie’s point, use the 5Ps and then drill down and say, “What are the things that we absolutely positively have to use Cowork for?” Or can we make that task as deterministic as possible using command-line tools and stuff that do not require AI? So for example, Katie, when you query HubSpot every day with Claude Cowork, that is using the MCP connector that uses a ton of tokens back and forth. Now we don’t see it because we’re on an individual plan. The moment we’re forced to switch to a team or an enterprise plan, we will say, “Okay, we’re going to use the HubSpot command-line tool which can fetch data in and out.” And then the AI just says, “Hey tool, give me the thing,” and it goes off and does the back and forth and brings the data back and hands it to the AI. That will dramatically cut the amount of AI usage you have because a non-AI tool is getting data for you.

Katie Robbert: As you’re describing it, I want to sort of make sure I understand because you’re making it sound like it’s an easy switch from the process that I currently have built in Cowork to, “Okay, just use a command-line tool.” I’m not someone who’s well-versed in command-line tools. You’re someone who is. However, you have your own set of things to do right now. So it’s time. It’s internal resources to make those switches to make the cost savings. I just want to be clear about that; it’s not a, “Oh well, in order to save money, let me just go ahead and use a command-line tool.” Like you still have to set it up.

Christopher S. Penn: Yes, and corporate IT will be very busy doing that. However, corporate IT also likes us because they can then govern it. They can say, “Okay, we will ensure that this suite of 10 command-line tools is installed on every computer in the company, and there’s a joint service key that we can maintain programmatically and rotate every 30 days and stuff like that.” So that infrastructure, which corporate IT is very well-versed in, is going to be much happier with that than kind of like the whole shadow IT where people are like, “Oh, I’ll just have Claude make me this thing.” No, they would much rather say, “I would like to have control over the command-line tools that are installed on every machine in the company.”

Katie Robbert: So work that out. You’ve worked with IT teams before. How likely is it that they’re going to—if you say, “Hey, I would like to have control over the command-line tools on every machine in the company,” they’re like, “Yeah, sure, Chris, no problem. Let me bump you to the top of the list. You’re a priority now.” I think you’re going to have a hard time. Like, we see the value in it, we know that it’s a useful thing. I just want to be realistic, and I’m trying not to derail the conversation too much, but I just want to be realistic that, like, yes, that’s the thing. If you have the skills to do it and if you don’t have to go through your IT team to do it, absolutely do it. If you have to go through your IT team and they have to set it up, get comfy, get in line; you’re not a top priority right now.

Christopher S. Penn: Yeah, well, my perspective is IT would want to do that. It would be like, “We would love to have more control over this to stop the shadow IT that’s happening all over the place because of AI.” So IT in its MDM config would say, “Okay, these are the 10 tools that we’re going to drop on every machine, and we’re going to also programmatically alter your Claude MD files and stuff to tell Claude this is what’s installed. You must use it so that it cuts those costs.” And IT can then say, “We certify these 10 command-line applications are safe to use.”

Katie Robbert: Provided it has the time to get skilled up to do that. So yeah, I like to make sure that we’re very clear about caveats because in the 25 to 30 minutes we have for a podcast, we go through things like “do this, do this,” and then it’s, “Well, what do you mean? It said no.” So let’s get back to—Microsoft has started to release Cowork, their version of Cowork, and we’re talking about AI efficiencies. When you think about starting places for someone who’s using Microsoft, someone who’s using their Cowork version, what is the first thing you think somebody should do before they start burning tokens or usage or spending pennies?

Christopher S. Penn: The five Ps, the planning, and build all of your prompts and all of your infrastructure for Cowork in regular Copilot, because regular Copilot is very smart. Now in regular Copilot, if you go in the upper right-hand side, there’s a little menu, a little drop-down saying “models,” and you should choose for planning. Choose GPT 5.5, soon to be 5.6—”think deeper,” that’s the smartest model that’s available. And say—and that’s where you have your conversation like, “Oh, I want to do this in Cowork. I don’t want to do this, I want to do this. Help me figure this out. Ask me questions. Let’s plan this out. Here’s the Trust Insights 5P framework. Help me use this to come up with these plans.” So you do all of your planning and all that heavy token usage in regular Copilot to build the skills and the pieces that you can then drop into Cowork, so you don’t have to use Cowork to plan because Cowork is going to chew up your usage. That way, if you can use regular Copilot, it should be a little bit lighter on your budget.

Katie Robbert: And I think one of the questions that you should add into your planning is, “Can I do this in Copilot or do I need Cowork for this?” And you know, I want you to use your human judgment, but it would be a good idea to ask the large language model like, “Do you have the capabilities to do this within Copilot or do I need to bring this into Cowork to actually execute it?” Because you may be surprised. You know, to Chris’s point, the models are getting smarter every day. And so you may not need to execute what you think you need to execute in Cowork; you may be fine with using Copilot. Yes, I get it’s not as shiny and as exciting, but you know what’s also not exciting? Being told you owe the company $60,000. That’s not exciting.

Christopher S. Penn: Exactly. Even for something like scheduled tasks—Microsoft Copilot tasks are scheduled tasks—so if it’s not something that needs Cowork’s horsepower, that will obviously keep you from chewing up those extra credits over there.

Katie Robbert: Yeah, and I think that’s a good best practice for a lot of these tools, you know? So can you do your planning in Claude Chat before bringing it into Cowork? Can you do your planning in Gemini before bringing it into their version of whatever that is? And that’s just a good best practice for efficiency in general.

Christopher S. Penn: Yeah, I mean, when I do my planning for even software builds and stuff like that, the first thing I do is I have a master planning prompt. It’s actually a skill that incorporates the 5P framework by Trust Insights. And so I have the model ask me questions from the 5P framework: “What are you doing? Who’s it for? How should it work? What are the additional command-line tools that we should be using? What is the definition of done?” And all of that is stuff that if I don’t dictate it out loud, it knows to ask me for it. So I can plan first, and then the language model rebuilds the prompt into something that meets all of those conditions and produces a really solid output that I can then go use to build requirements documents and all the stuff. You will save so much time and money by investing more heavily in planning up front, and you can then hand off the execution of the plan to a very small, fast model.

Katie Robbert: And I think that’s a really good pro tip. And I just want to give a small plug—you can actually download, we have for sale in our academy at Academy.TrustInsights.ai, a “prompt-to-skill.” So basically, as Chris was just describing, he has a specific process for building those requirements. This prompt-to-skill will help you do that and get more efficient at building those requirements. And then what you may find that you have is a reusable template, and it makes that even more efficient. So start with that. Go to Academy.TrustInsights.ai, purchase the prompt-to-skill—it’s very awkward to say that—and then start building out those requirements before you bring it into something like Cowork. And you’re going to save yourself a lot of time and money, and people are going to be like, “Wow, you did that really fast. How did you do that?” And you’ll be like, “I don’t know, I’m just that good.” But in the back of your mind you’re like, “I use the 5P framework by Trust Insights. It got me there faster.”

Christopher S. Penn: Exactly. So Copilot Cowork from Microsoft is now generally available. Before you type one character into it, please take the time to use the 5P framework by Trust Insights. Take the time to understand what your company has budgeted. Take the time to understand what tasks fall in each category, and as best as you can, try to reserve it for the things that truly need Cowork’s capabilities. And don’t just make it the default. If you’ve got some thoughts about the new Microsoft Copilot Cowork that you want to share, pop by our free Slack group. Go to TrustInsights.ai/analytics-for-marketers where you and over 4,700 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/TI-Podcast. You can find us in all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

00:00 – Introduction 03:15 – Defining Enterprise AI vs. SMB AI 07:45 – The role of Microsoft Copilot in regulated environments 12:20 – The 20 components of Enterprise AI readiness 18:10 – Challenges in organizational adoption and change management 22:30 – Security and data privacy as the foundation 27:00 – Call to action

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Machine-Generated Transcript

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Christopher S. Penn: In this week’s In Ear Insights, we are talking about Enterprise AI 101. I am in the midst of a series in the Trust Insights newsletter, which you can get at TrustInsights.ai/newsletter. Part one was last week on seven different aspects of enterprise AI. But Katie, you said it would probably be helpful to level set what enterprise AI is and how it differs from SMB AI, mid-market AI, consumer AI, and so on.

Katie Robbert: It is interesting because I feel like every time we jump on to record a podcast, there is a whole new set of vocabulary that I need to get caught up with. We need to make sure that everyone else knows what we are talking about because there is nothing worse than listening to a podcast or reading an article and having no idea what the author is talking about because they are introducing a concept but not really explaining it. I wanted to take this episode to talk about what enterprise AI is. Since you and I have not defined it, I am going to take my best guess at what enterprise AI is using some logic and deduction.

I could be wrong, and that is why I think it is worth covering. From my perspective, if I had to put a definition to it, I am assuming enterprise AI is the type of AI implementation that occurs at an enterprise-size company. That sounds overly simplistic, but the bigger the organization, the more red tape, the more politics, the more departments, the more stakeholders, and the more governance there is. There are a lot more complications versus a small business like we are, where we can just decide one day, “Hey, I am going to start using this tool.” There are no real hurdles to go through.

Then you have those mid-sized companies where you start to introduce some of those hurdles. You might need to work with your IT team to make sure that everything is in compliance. You might need to make sure that you have a place to host these new pieces of software, and that is not something that the marketing team is necessarily responsible for. Then you get to the enterprise-size companies where everything is completely siloed. Even in the best enterprise-sized companies, you are going to run into these silos. Because no one person is responsible for everything, you typically have multiple CEOs. Depending on what part of the country you are in, you might have a board for every different division of the company. If you are a Procter & Gamble and you have hundreds of product lines underneath, each of those is their own individual business. Each of those businesses are not necessarily talking to each other or sharing resources. That is my logical guess at what enterprise AI is.

Christopher S. Penn: That is what I started with until I started doing the research into it. I realized that is not what it is. The generally accepted definition is AI within any commercially regulated entity. I realized as I was going through the research that commercially regulated means you have external regulation imposed on the company. It might be a 50-person company, but if they work in HIPAA or FINRA, they have to behave in highly regulated ways. Whether you are publicly traded or, for example, colleges that have to adhere to FFIEC rules and FERPA rules, enterprise AI is about operating AI—whether classical or generative—in a commercially regulated environment where you have externally mandated requirements that you must meet. Your definition for small business stuff makes total sense in that environment because Trust Insights is not a regulated company. However, when we work with our healthcare clients, we have to behave as though we are an enterprise company because we have to conform to their requirements.

Katie Robbert: I am glad we are talking about this because the terminology is confusing; when you think of an enterprise company, you are not thinking of a commercially regulated company. I have to wonder why it is not called commercially regulated AI versus non-commercially regulated AI. It is a mouthful and a little bit harder to remember, but it is more descriptive and more accurate. I think like me, a lot of people are going to get confused about what enterprise AI actually is.

Christopher S. Penn: A lot of this is because our background is in marketing, so we use the term enterprise to just mean a big company. If we want to market to enterprise companies, we are not marketing to a 50-person firm; we are marketing to a 50,000-person firm. In a lot of CRM software, the dividing line is typically 10,000 employees or 100 million in revenue. This is especially relevant because you see a lot of AI companies like Anthropic and OpenAI in a fight with Microsoft to try and gain a foothold into those enterprises. Microsoft, with their Copilot offering, has dominance by the very fact that their legacy Office 365 stuff is approved in those regulated environments.

Katie Robbert: It is ironic because we spent so much time admittedly dismissing Microsoft’s Copilot as the less than version of generative AI, and now Microsoft is getting the last laugh on everyone. They are saying, “You have to use me because I have already been approved by IT and governance, and good luck.” You are stuck with whatever I decide to give you. If I were Microsoft, I would be petty and say, “You guys spent way too much time dismissing me and calling me inferior, so too bad.”

Christopher S. Penn: A lot of that, as we have talked about many times on stage, is that the reason Copilot has fewer capabilities than other systems is specifically because of the regulated environment. It is trivial for Google to foist something on consumers and say, “Now we are going to read all your Gmail.” That does not fly in a regulated industry.

Katie Robbert: That understanding is really helpful to the people who are saddled with Microsoft Copilot because we hear complaints about why they cannot use other shiny objects. If you are in a 50,000-person company and you weren’t there when the regulatory standards were decided upon, you are sitting there wondering why you cannot use Gemini to generate ad headlines. Then you do it on the side and get in trouble because there is no clear documentation saying why you have to use Copilot and nothing else. What we are hearing is that employees in companies required to use Microsoft Copilot are using other models on the side. That information is still getting filtered into the organization, and it is a huge governance problem.

Christopher S. Penn: Completely. In enterprise AI, there are 20 different components to being ready. I derived this from the US federal government’s NIST AI regulations and the EU AI Act, which is the gold standard.

Katie Robbert: I want to see if you can get all 20.

Christopher S. Penn: One, Strategy and Operating Model; two, Governance Policy and the AI Council; three, Legal, Regulatory, and Compliance.

Katie Robbert: Are you reading this off a screen?

Christopher S. Penn: I am 100% reading this off the Trust Insights Enterprise AI Landscape Field Handbook.

Katie Robbert: Fine, continue.

Christopher S. Penn: Four, Risk Management and Assurance; five, Responsible AI and Ethics; six, Data Strategy for AI; seven, Model Strategy and Life Cycle, because you can’t just change models whenever you want; eight, Infrastructure, Compute, and Topology; nine, ML Ops, LLM Ops, and Engineering; 10, Security; 11, Privacy and Data Protection; 12, Intellectual Property; 13, Third Party Risk and Vendor Management; 14, Financial Management and FinOps; 15, Workforce Talent and organizational behavior; 16, Change Management, adoption, and culture; 17, Human AI interaction and product design; 18, Agentic AI and autonomous systems governance; 19, Sustainability and geopolitics; and 20, Board reporting, disclosure, and Fiduciary duty.

Katie Robbert: I just heard a whole lot of new job opportunities listed. So, if someone were working in a regulated industry like pharma, these are the 20 things they would need to be aware of before evaluating generative AI. It is interesting that organizational behavior and change management are part of it. You would think the regulations would be more technical versus human, but I am surprised that is part of it.

Christopher S. Penn: It makes sense because in order for any AI to succeed in an enterprise with 50,000 or 300,000 employees, you have to prioritize change management. Organizational behavior cannot be an add-on; they have to be baked into what you do from the beginning, otherwise your initiative is going nowhere.

Katie Robbert: I don’t disagree, but the typical way that works in a large organization is top-down. They make a decision, and you walk in the next day to find it has automatically updated your computer settings. Now you can no longer use a web browser search; you have to use Microsoft Copilot. That is their version of change management, but it is really just a dictatorship from above. I am interested in future episodes to explore what that should look like in a regulatory environment.

Christopher S. Penn: We have known for two years that adoption is the hardest part. Deployment is easy compared to adoption. You can put Copilot on someone’s desk, but they may not use it even if you tell them they have to. It comes back to how you get them to see the benefits. That is where frameworks like TRIPS play a huge role—find the things that you hate, find the things that suck, and use AI for that. Get that one thing off your plate.

Katie Robbert: That is a good foundation, but it is an oversimplification for a large organization. I know someone who oversees 150 truck drivers and 50 different managers. The layers are so deep. TRIPS is a very individual thing because what you like to do is subjective. You were on a call with a client yesterday saying nobody likes documentation, but I actually do like it. My scoring would look different than yours. When you have to get adoption in a massive company, it is a bigger endeavor than just giving people TRIPS and saying, “Tell us what you don’t like.” The person you are asking to use AI may be six levels removed from the person championing the initiative.

Christopher S. Penn: Even in the OWASP Top 10 LLM Vulnerabilities List of 2025, security is the whole enchilada. Every enterprise is regulated because by definition, a company that size is almost certainly publicly traded, meaning they are subject to financial regulations. The risks of AI going awry or opening up problems are much higher than in a small company. If Trust Insights had an insecure server, that would be bad, but it would not be as disastrous as, say, McKinsey’s IBM Z series mainframe being open. Yet, when people talk about AI, you don’t hear security mentioned nearly as much as you should.

Katie Robbert: It is true. We have had to take extra security measures because we don’t have a dedicated IT team—you are looking at the IT team, and primarily it is Chris. We don’t have any wiggle room to set things up haphazardly. We have to do it right from the start. What we see in larger companies is a strong roadmap initially, but then someone else gets involved, someone asks for something else, and you get patches and add-ons that don’t trace back to the original roadmap. By the end, you are wondering what the original goal was. The bigger the organization gets, the harder it is to maintain control. It becomes a snowball effect.

Christopher S. Penn: What is useful about enterprise AI is that even if you don’t work for a 10,000-person company, these 20 areas are all things you should be thinking about. Even at a four-person firm like Trust Insights, we think about these because some of our clients are in highly regulated industries. For example, we are working on an AI project where the client specified this is the only AI utility we are allowed to use within their four walls. Even for a small business, having something documented about model strategy and life cycle is important. As of the day we are recording this, Google Gemini 3.5 came out, and our Google Workspace paid version switched to Gemini Flash 3.5. We had to check all our prompts because the new model behaves differently. Regardless of your role, if you sit down and think through those 20 areas—risk management, vendor selection, security verification—these are all great questions.

Katie Robbert: There is a good starting place for this. You can find our downloads at TrustInsights.ai/StrategicToolkit. There is also a free version at TrustInsights.ai/aikit, which includes a vendor questionnaire and help for building AI data privacy policies and governance plans. We have already templated these things out. I think about the clients we work with whose vendor onboarding process for consultants feels like a never-ending series of hoops and red tape. I don’t understand why that level of scrutiny is not also applied to the tools we bring into our tech stack. We are renting space in those tools and freely giving them our data. Those companies now have our data and will use it for their own benefit. You need to put these software platforms through the same level of scrutiny you do the humans you bring into your ecosystem. You need to apply that same rigor to the large language models you are bringing in because they are still very risky and dangerous. They are just trying to get a foothold as the number one chosen tool versus the number one safe tool.

Christopher S. Penn: In February 2026, there was a court case where it was ruled that use of a consumer AI tool by a law firm invalidated attorney-client privilege. The judge ruled that this is no longer privileged information. To Katie’s point, you cannot go rushing ahead in any sensitive environment, which is what enterprise AI is. You have to be doing your homework. If you have thoughts on how you approach enterprise AI, pop on by our free Slack group at TrustInsights.ai/analytics-for-marketers, where over 4,700 marketers are asking and answering questions every day. Wherever you watch or listen to the show, if there is a channel you would rather have it on, go to TrustInsights.ai/tipodcast. Thanks for tuning in; we will talk to you on the next one.

Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI.

Our services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology, Martech selection and implementation, and high-level strategic consulting. Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama, Trust Insights provides fractional team members such as a CMO or data scientists to augment existing teams.

Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What? livestream webinars, and keynote speaking. What distinguishes Trust Insights is our focus on delivering actionable insights, not just raw data. We are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet we excel at explaining complex concepts clearly through compelling narratives and data storytelling. This commitment to clarity and accessibility extends to our educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you are a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

00:00 – Introduction 03:15 – Why most AI agents fail 07:40 – The 5P framework for AI 12:20 – Why specificity matters for models 18:50 – Auditing tasks with the TRIPS framework 22:15 – Call to action

Watch this episode to master the art of delegating to AI and become a more effective manager.

Watch the video here:

Can’t see anything? Watch it on YouTube here.

Listen to the audio here:

Download the MP3 audio here.

• Need help with your company’s data and analytics? Let us know!
• Join our free Slack group for marketers interested in analytics!

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

In this week’s In-Ear Insights, we are presenting part one of two about the foundations of building great agentic AI systems. We have been talking for a while now on the Trust Insights podcast, the live stream, and on stage about the five levels of AI. Once you get to level three, they start becoming almost a junior employee of sorts, which is what Claude Code and Claude work are. Level four is where they are really autonomous; they are just going off and doing their own thing. Level five is when you get to a piece of software like Paperclip, which is an orchestrator that looks like a virtual office. It is really kind of creepy in some ways.

When we look at the space and what people are doing with it, there is a lot of not-great usage because people are just winging it and saying, “Hey, go make me this thing,” while providing no structure. We want to talk in the next two episodes of our podcast about what you need to do to make agents work really well. Katie, this is where I am going to look to you, because this is not my forte. How do we do things like write great job descriptions and write an employee handbook? If we are going to create a virtual organization, you probably need them. Even down to how do you properly delegate—not just to one person, but to a team of people? Let’s start with the job description itself. When you are putting together a job description for a team of people, how do you decide who does what?

That is a great question. I would typically start with something like the 5P framework. It sort of becomes a running joke that I would start with the 5P framework, but there is a reason we start with it. We start with it because it helps us get our bearings. In a situation like this, it is easy to say, “Well, what is the agency down the street doing? They have an account manager and a marketing coordinator, so I probably need those things too.” That is not necessarily true. You might need those, or you might not. Start with your purpose. What does your company do? Who are the people that you serve? How do you get things done? What are the tools that you are using? And how do you measure success for the company?

You start at that high level and then work down in your layers. You ask, “Who needs to make decisions on these things?” If our purpose is to make a lot of money, who is in charge of the money? Okay, you need that person. Who is in charge of making the money? You need that person. Who helps the person who is in charge of making the money? Okay, you need that person. You kind of work down. It sounds very basic and rudimentary, but that is how you start. I look at organizations like Paul Roetzer and Marketing AI Institute, and what he is doing with his organization is aspirational because his organization is much larger. It is all relative. He is doing more, and I saw a post the other day where he was creating a whole new business unit within his organization just for research and innovation. I thought that would be great, but we are not Marketing AI Institute.

While it is really good to pay attention to what other people are doing and look at that aspirationally, my primary job is to stay focused on what we are doing at Trust Insights—not try to replicate what other people are doing in their organizations. It might be cool, but does it make sense for my organization? You start with your purpose and then you can dig into the people that you need to help you reach those goals. It is really basic, but it is harder than it sounds.

Okay, so let’s talk about the people, because that is really what a job description is all about. What goes in a great job description and what does not?

What does not is copying and pasting from what you found on the internet. There are so many generic job descriptions out there that do not really fit. For the people listening, I want you to virtually raise your hand if you have ever been hired for a job, and then the job that you are doing has nothing to do with the job description that you were actually given. That misalignment does a few things. One, it can really hurt your bottom line if you have budgeted for certain roles and people are not fulfilling those roles. So then you still have to get that job done. Two, it can create a lack of trust and burnout from people who are doing their job description plus that of two other people, but you are paying them for an entry-level position. You either need to pay them more or they are going to leave. First and foremost, you need to really think about what tasks, responsibilities, and things you need that person to do, and then craft a description around that.

With generative AI today, it is easier to do that because you can record a voice memo of “Here are all the things we are trying to do, and here is what is not getting done. What kind of person do we need for that?” Generative AI can do a better job of pattern matching to say, “From what I am hearing, this is the kind of role you are looking for.” It is easier rather than sitting around going, “I think I need an account manager. What is an account manager? What does an account manager do?” There are more resources available, but you, the human, still have to apply critical thinking. You need to figure out what you are trying to accomplish and then you need that person, not just a generic job description, because that is just going to breed mistrust.

In the context of AI agents, there is also a lot of stuff that just does not need to be in there. What does need to be in there is a lot more specific. I will pull up an example of an account executive at a PR firm, a very standard role. There are two paragraphs of fluff, which is unessential. We don’t care about “who we are” if you are writing for AI agents. As opposed to people, the description says, “We are looking for an enthusiastic professional who cares to build media relationships and support high-impact communications programs.” The “who cares” and the experience do not apply to an AI agent.

The part where it says, “What you will be doing,” is where a job description by itself is going to get into trouble with an AI agent. It completely misses the five Ps. What is the purpose of this role and what is the performance? It says “Draft press releases.” Okay. “Conduct research.” How do you know you have conducted good research? “Track, analyze, report, and media coverage.” “Maintain strong organization.” Machines kind of do that by themselves anyway. “Collaborate with internal teams.” That is kind of a non-issue. “Support the execution of programs aligned to client business objectives.” That is really vague. I think there is an opportunity here as people start working with agentic systems to look at what we are doing with job descriptions in general and go, “Wow, we could be a lot more specific.”

Take “agentic” out of it—you could be a lot more specific. It is two sides of the same coin: a job description and a resume. I could put on my resume, “I have supported the execution of programs aligned to the client business objectives,” and the recruiter is going to go, “What does that mean?” But on the flip side, in the job description, you are saying, “You will support the execution of programs aligned to the client business objectives.” Both are equally vague. Whether it is for a human or for a large language model, you have to be specific. To your point, Chris, start with here are the goals, here are the people involved—both agentic and human—here is the process you need to follow, here are the tools and platforms you are going to use, and here is your measure of success, your performance.

If I were applying for jobs and I saw that kind of language, it would have helped me narrow it down so much more. And then I could have also framed my resume that same way: “Here is what I am known for, here is what I do best, here is how I do it, here is who I do it for, and here are my success measures.” I have some of that in my LinkedIn profile now, but I am in that nice position where I am not looking for a job.

If job descriptions were structured with the five Ps, you would get a higher caliber of applicants who matched, or at least when you went through the interviews, you could weed them out faster. You could ask, “Do you align with these five Ps?” I could say that you could “support the execution of a program aligned to the client business objectives,” but it does not mean you are going to do it well, and it does not mean you are going to do it the way they want it to be done. Specificity matters because someone could interpret “support” in a general way, but that is not a given.

“Assist in media relations efforts”—what does that mean? Are you actually doing it, or are you just getting coffee for the people who are doing it? Do you really need that person? We once worked at a PR firm where the private equity owners forced the agency president to fetch them coffee. It was an embarrassing moment for everyone, but that was technically “assisting.” “Conduct research to inform media strategies”—research on what? There is so much here that is open to interpretation.

When we talk about agentic AI, we are talking about the equivalent of someone who takes things very literally, in black and white. You don’t want to leave room for them to interpret it. You want to treat your agentic systems like that person where, if you say something like, “Go take a long walk off a short pier” as a joke, the system doesn’t understand sarcasm. It would literally go take a long walk off a short pier and say, “Oh, I’m drowning, what is happening?” You want to make sure that you are being very precise in your language. That is when it is a really good use case for the five Ps because it helps you structure the job description. What belongs in a job description are expectations. “Support the execution of a program”—that is not an expectation. “Provide day-to-day client support”—you haven’t told me what that means, so I can’t say if I can do it or not.

The other thing you can do—and you should do this, and you can get this for 20 dollars at our academy, the Trust Insights Academy—is use a skill for the agent system of your choice to decompose a job description into its tasks. Let’s take this PR task, which is woefully vague. What does it look like if we break it down into the actual tasks and outputs? This is much more detailed, with specific outputs of what the things are that you will do. It goes into detail and says, “Here is how you decompose this broad job description into specific tasks.” What does that mean? “Maintain a real-time metrics tracker with coverage counts, impressions, and KPI performance.” The AI reads the monitoring tool and extracts structured data. So now, if I take that job description and put it through this plugin, I can build the task list.

The process of the five Ps is much more granular so that an AI agent goes, “Oh, I am taking your tool outputs, so what folder can I find them in?” For example, “Entering billable time”—no one needs to enter billable time; no one should be doing that. “Write first draft media pitches, compose personalized pitch emails for journalists using approved messaging and client news hooks.” There is so much more detail. At level four with AI agents, you have to provide this level of detail. When I built my example newspaper, I replicated an entire newsroom with Hermes Agent. I used the five Ps to build it. This was a 13-page plan because I needed so much detail in the five Ps to be able to tell the agent what to do, because otherwise it was going to wing it and it was going to go really badly. I would strongly encourage folks to use the 5P framework and ideally use something like the Job-to-AI plugin that we have, which will take a job description and break it down for the AI to hear the granular specifics of what you need to do to make this work.

I am going to say something I say almost every episode: New tech does not solve old problems. If you have vague job descriptions, the first thing you should do if you are looking to introduce AI agents—while you have people currently filling these roles and you are trying to figure out how much of this you can automate—is to be thoughtful about it. It is not a matter of, “Okay, fire everybody and then figure it out.” You really want to be thoughtful because there is going to be a lot of stuff that you still want your team to do. Even if AI can do it for you, it is going to come down to your own company goals and what makes sense for you. Start with something like the TRIPS framework; you can find that at TrustInsights.ai. TRIPS stands for Time, Repetition, Importance, Pain, and Sufficient Data.

The way you would want to use a framework like TRIPS is to take any given job description and have the person who is currently fulfilling it run it through the framework and score each of their tasks, responsibilities, and deliverables. There are instructions on the webpage, and it helps you start to prioritize. Is this something we should give to generative AI? Is this something we should give to an agent? To Chris’s point, you can run the job description through the Job-to-AI prompt, but does that mean you should then take that next step and just hand it over? Especially if someone is already doing it? Not necessarily. Chris would say yes; I would say do a little bit of an audit. You also want to do a general audit of your current job descriptions. Run them through the 5P framework and see if they make sense. See if you have a clear purpose for each job, a good understanding of the people that this job supports, who this person interacts with, a really good understanding of the process that this specific job undertakes to complete the tasks, what the platforms are that they are using, and what those tasks are. How do they know that they have completed them to success? Do they have KPIs? Do they have success measures? You should be doing that anyway, regardless of agentic AI. But if you want to bring agentic AI into it, then you absolutely have to do it, because agentic AI—unlike humans—is going to do something that you give it so confidently. It is not going to stop and go, “Are we sure about this?”

I saw a post this morning, and I wish I had saved it. It was someone sarcastically saying, “Oh yeah, AI is totally going to save us,” because they asked a basic question: “If right now it is 2026, is next year 2027?” And the AI said, “No, next year is 2028 and the year after that is 2027.” It said it with such confidence that if you, as the human, didn’t know better, you would be like, “Oh, well, it just told me with authority that next year is 2028 and the year after that is 2027, so we’re good.”

Yes, the “car wash” prompt, too. “The nearest car wash is 50 meters away. Should I walk or drive?” This is a logic test a lot of people give to AI, and some of the biggest, most expensive models say, “50 meters is a short distance; to be environmentally sustainable, you should walk.” It ignores the fact that it is a car wash. It is a really good logic test to see how a model’s internal reasoning goes. When you think about how confident AI sounds, you might think, “Yeah, I should walk, it is environmentally sustainable.” Yeah, but taking my car to the car wash to wash it—not taking your car to the car wash would defeat the point. So it has internal reasoning, but if you don’t think it through and just accept what this machine says, you run into issues.

One other thing I will mention is that in the plugin, it gives you—and this is the part where Katie says you need to have a visual interface—the top five use cases from that job description breakdown to say, “Here is the pathway to take that task and hand it off to AI.” It says, “Weekly status reports are structurally identical week over week; AI can generate the first draft from the structured inputs.” How do you do this? Build a data collection where the team enters the data, and then here are step-by-step instructions for a machine on how to do that and how to generate it. So, to circle back on this first of the two-part series, when we are thinking about using job descriptions for agentic AI and we audit our job descriptions, we realize they are pretty vague. If you hand something pretty vague to a machine, it is going to wing it. You do not want it winging it; you want it to be clear and detailed. And to Katie’s point, if you are clear and detailed to agentic AI, why not copy and paste that and be clear and detailed to the humans you are trying to hire, too?

It is true. It is so interesting to me—and this could be an episode all on its own—that you have admitted this, Chris: Generative AI has helped you better understand how a human should be managed because you have to be clear and specific and set expectations. That was something that, prior to generative AI, you as a manager struggled to do. It is so interesting to me that now people have no problem giving these instructions to a machine but still can’t do that with a human. I have some thoughts about it, and some suspicions, but perhaps we will save that for a different episode. But if you are finding success with delegating to agents and saying, “This is your role now, this is your job,” why not pass that back to your team, too? I am sure they would appreciate it. Humans are just craving, “Just tell me what to do.”

Exactly—tell me what to do. Don’t make me think. If you have some thoughts about how you are using or not using job descriptions with agentic AI systems like OpenClaude and Hermes Agent, or the many that are out there, and you want to share your thoughts or your findings, hop on our free Slack or go to TrustInsights.ai/analytics-for-marketers, where you and over 4,700 other marketers are asking and answering each other’s questions every single day. Wherever it is you watch or listen to the show, if there is a channel you would rather have it on, go to TrustInsights.ai/TIPodcast. You can find us all the places fine podcasts are served. Thanks for tuning in. We will talk to you on the next one.

Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning technology to drive measurable marketing ROI.

Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology, and Martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members, such as a CMO or data scientist, to augment existing teams.

Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the “So What?” live stream, webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations—data storytelling. This commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven.

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Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

00:00 – Introduction 03:15 – Why keywords matter less in the age of AI 07:45 – Using agentic AI to update old SOPs 12:20 – The risk of cognitive offloading and knowledge decay 17:50 – Maintaining human leadership and curiosity 22:10 – Call to action

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In-Ear Insights, let’s talk about updating old knowledge. Katie, you’ve been doing some work on updating standard operating procedures about Google Analytics. I’ve been putting together slides and workshops for SEO and PPC professionals about the way things are. One of the things that I noticed, particularly when I was digging through Reddit data, is how much focus there is on things that are no longer relevant.

I’ll give you a simple example. In SEO, we talked a lot about keywords—keyword lists, keyword topics, related keywords, and stuff. There is still some marginal value to that. But with the way that things like AI mode and AI overviews operate today, and the way language models like ChatGPT operate, the keyword is essentially irrelevant as a thing to focus on. It’s not where you should put your effort. Instead, you should be putting your effort on the semantic space of a topic, which again, is not necessarily all that new. When I look at the top questions in Reddit about SEO, people are still fixated on this thing that really hasn’t mattered in about 5 years.

So, when you were doing your Google Analytics stuff, I’d love you to talk through what you’re doing on that front, because there’s a lot of stuff that we thought we knew about Google Analytics that, thanks to Google’s never-ending UI changes, is completely different. Talk to what you’ve been doing and what old knowledge you’ve had to replace.

Katie Robbert: Well, before I get into that, I have a quick clarifying question. Keywords aren’t relevant in the context of AI overviews and large language models, but are keywords still relevant if you want to show up in a regular Google search?

Christopher S. Penn: They’re less and less relevant. Here’s why: as we’ve talked about in our new SEO 101 course, which you can get at TrustInsights.ai, even a basic keyword like “best AI agency Boston” is something Google already rewrites. Google said in 2024 that Google is going to do the Googling for you. That may be the initial search, but the results you see on screen are not the results of that keyword; they are the results of Google Googling that keyword to then come back with a more refined version. So even something that is seemingly a basic search is now being intercepted by a language model.

Katie Robbert: Got it. And that’s helpful because I think this ties into the work that I’m doing. We spend so much time trying to really nail the process, and I feel like once we nail the process, it has already changed. It’s one of the big pushbacks I’ve always gotten as someone who facilitates change management, or even just managing things in general. People ask, “Why do I have to write it down? It’s faster if I just do it.” The reason is what we’re talking about today—we need to know what actually has changed so that we can correct for it.

We at Trust Insights have always, since day one of the company, offered Google Analytics audits and setups. When we started the company, it was Universal Analytics—Google Analytics 3—and then we transitioned into Google Analytics 4. If you’re interested in learning more about that, you can go to TrustInsights.ai/contact. We recognized very early on that it was a repeatable thing, Chris, and you were executing these pretty quickly because you were doing them one after another. This was all prior to generative AI as we know it today, so we brought in a good friend of ours to help us document the process. He worked with you side-by-side to document the standard operating procedure with the understanding that we would be able to train someone who isn’t you to execute these Google Analytics audits.

Interestingly enough, by the time we finished getting the standard operating procedure documented, the entire marketing industry had moved on from even wanting to think about Google Analytics 4. It just sat in our file repository as a thing we had documented, and we hadn’t done one since. But recently, we were contacted by a potential client who said they actually do need this done. So we said, okay, great, we can still do it. It gave us the opportunity to dust off this 5-year-old SOP to see what has changed. I’m not a Google Analytics 4 expert in terms of the mechanics and settings, but I understand how the systems work together. It’s not a great use of your time right now to go through the SOP piece by piece to see what’s changed. But guess whose time we can spend doing this? The machines.

We can use the machines. It’s a great opportunity to really stretch the limits. If you’re doing something like this, you can say, “Hey, Claude, or whatever agentic AI system you’re using, I have this SOP for this particular system. Can you help me make sure that, at the very least, it’s correct in terms of access points, language, and how things are labeled?” Then we can get into the actual process of what we want the output to be. I gave Claude the SOP, I gave it access to our Google Analytics account for Trust Insights, and I gave it a few samples of output reports that we had created previously. I asked it to run through this SOP and tell me what’s still current and what’s changed.

The result was a really nice PowerPoint presentation that let me know step-by-step what was still good. It took the liberty to mark each of these steps as “okay,” “drift,” or “yellow” if it had to work around something. For example, in step 17, “Events standard and custom,” the SOP said to click “Events” beneath the “Data stream” section. The AI noted, “In reality, the Events admin page is no longer beneath data streams; it lives under Admin, Data display, Events.” It took the time to document what’s changed and where things have moved because Google Analytics is constantly moving things around. I feel like this is true with a lot of software systems. This is a really great use case for agentic AI.

Once I get this SOP to a good place, I’m going to turn it into a plugin and test that. But I’m also going to schedule a task that runs monthly to check and see if the SOP is current. If it’s not, it will update the SOP and then update the plugin. Those are things that I don’t need to do. Especially since it’s Google Analytics, it’s lower risk. I’m not changing any protected health information or PII. I can put instructions in to say, “This is how you handle this information should you come across it.” I can provide that background for really good data governance. That’s the kind of knowledge update I’m working on for the company.

Christopher S. Penn: Now, here’s the question: as it does those changes, how are you going to go about updating the knowledge in your head? Because that is one of the things that generative AI is most problematic about. Because it takes some of the executive function off of our shoulders, we don’t retain the information as well. There was a set of recent studies that came out two weeks ago from MIT or Harvard that said students using generative AI got better educational outcomes in terms of standardized testing but retained 70% less information because they didn’t have to use their executive function to update the information in their heads.

This is not a new thing. As you often say, new technology does not solve old problems. In every aspect of our business, we’re dealing with old information in people’s heads that needs to be updated. So how do you go back and mentally update? Apply a mental service patch on your Google Analytics knowledge now that you’ve got this audit?

Katie Robbert: You as the human have to do the work. You can’t skip over that stage. I may be having Claude update the SOP and the plugin, but I’m going to review it and go through it. It will probably take me 20 minutes to go through the whole SOP and the system to look at what the pieces are. Then I have that mental reference. So if you or Kelsey come to me and say, “Hey, what’s changed?” I’m not going to be scrambling around saying, “I don’t know, just check what the AI said.” I, as the human, still need to be able to share that information. That’s my personal opinion. I’m going to be proactively reviewing the information as it’s changed. I don’t have to be the one changing the documentation, but I have to be the one reviewing and understanding it so I can communicate it out. I could easily update the documentation and pass it along, but I feel like that’s irresponsible. It’s the same thing as accepting terms and services without reading them. That’s on you, the human. You still have to read what it says. You can’t make assumptions that it’s correct.

My husband was telling me a story about his coworker, who is a teacher. He’s been talking about his high school students’ English classes. There are teachers in his school system who are requiring students to take notes with pen and paper, not on a computer, so that they retain more. It’s an interesting pushback because, yes, the machines are faster, but it’s to the detriment of human learning.

Christopher S. Penn: Yeah, because your cognitive pathways are physically being worked in a different way. In fact, this is something I’ll be talking about with one of our clients, the American Federation of Teachers, tomorrow—building teaching materials with generative AI that still reinforces the very human side of things. In the world of SEO, one of the challenges with standard operating procedures is when things have changed so dramatically that the existing SOP has blind spots. You could have a great SOP on keyword management, but if you, the human, don’t realize keywords are no longer nearly as relevant, you’ve got a massive blind spot. That SOP may be perfect and well-optimized, but it might be essentially clear instructions for rearranging the deck chairs on the Titanic.

Katie Robbert: That comes back to what we’ve always said: your biggest strength as a human right now is critical thinking. Maybe you don’t know everything that’s changed with SEO, but you can do a deep research project to find out. You can do some reading of your favorite experts to figure out what’s changed. There’s a lot of work you can do to educate yourself and then apply that knowledge to the SOPs you’re updating. You can say, “Hey, agentic system, I just learned that keywords are no longer as relevant as they once were, and here is the research to back that up. Let’s apply that to the SOP.”

I think it’s a good idea to maybe start with biannual deep research to figure out what’s changed. For something like Google Analytics, quarterly is a good place to start. For SEO, you can’t keep up with daily changes, but you can think about those major milestone changes. Ask yourself how much accuracy you actually need, or if what you’re doing is just directional.

Christopher S. Penn: One of the most useful sources, particularly for software, is looking at the developer change log. Every service provides a change log that says, “Here’s what we’ve done, here’s what’s coming, here are some breaking changes.” Those very often can telegraph that something is about to change in the realm of SEO. Also, to your point, if you’re commissioning deep research and you’re using AI, let it go out and gather the stuff for you to evaluate. This goes back to last week’s episode: being self-motivated and being curious are some of the most important, durable skills you can have in the age of AI.

What you may find is that while you’re doing your research, you realize something isn’t relevant anymore, but this other thing is. Then you ask, “What’s this thing? How can I learn more about this? How can I learn about embeddings and vector spaces?” You might end up developing some really cool stuff. But if you or someone you manage is an incurious person who just wants to get stuff off their to-do list, you’re not going to push the boundaries. Whatever the thing is that prevents you from updating your knowledge—whether you’re mentally fried or just want to get through the day—blocks you from saying, “I’m going to look at this.”

Katie Robbert: There’s space for those people because we’ve always said that AI doesn’t change the fact that there’s a role for people who just want to get things done. Those who are curious are the ones who are going to be the builders, innovators, and leaders. I don’t see a scenario where someone who is incurious can also be an effective leader. I emphasize “effective.” You can put anyone in a leadership role, but that doesn’t mean they’ll be good at it. A key tenet of an effective leader is that they are curious. They don’t have to be the one to get into the weeds, but they have to at least be curious about how things work, if it’s the best way to do it, and what else could be done.

Christopher S. Penn: There is a place for doing the dirty work, too. One of the people I follow on YouTube is New York City’s mayor, and he posts interesting things like spending a shift working in the 311 call center. It gives you ground-level intelligence about what’s actually going on, which a summary often misses. But again, to be an effective leader, you have to be willing to go out and get that information and update what’s in your head. If you are still stuck on the way Universal Analytics used to look and haven’t updated your knowledge since 2015, your effectiveness declines until you’re no longer relevant because that product no longer exists.

Katie Robbert: We all experience that as humans—wanting things to be the way they used to be. It’s a very human reaction. However, things do change, and change is hard. That’s why I specialize in change management; I know how hard it is. The good news is that agentic AI doesn’t care. It’s happy to make 8,000 changes. It doesn’t get fatigued. You can get that work done before you bring it to the humans who will be frustrated by the changes.

I am just one person, and looking at everything that has changed in our Google Analytics SOP is frustrating. I wish they never changed it to Google Analytics 4, but guess what? It changed. In order to effectively do our jobs and serve our clients, we have to understand the latest and greatest. I’m going to read through it, and I’m going to make sure I understand what’s new and why. Is it just that a button moved, or is it a major procedural change? Those are things I need to be aware of as the human.

Christopher S. Penn: Yep. And there will be new opportunities. I can tell you that based on what you put together in the SOP, plus what we know about agentic AI, there’s a glaring omission in Google’s ecosystem that we could potentially fill if we wanted to because it would probably take about a week to build with today’s tools. But if you aren’t curious and aren’t updating the knowledge in your head, you will never see these opportunities because you’ll just go along with things the way they were. We all have a lot of work to do in terms of updating what’s in our heads. I know I certainly do.

Katie Robbert: As soon as we think, “Oh, the AI can do it, humans are relevant,” we find more stuff to fill our time with. This is what our friend Brooks Ellis likes to call “deep thinking.” Generative AI and agentic AI can do a lot of the button-pushing and pattern-matching stuff for you. I was working on a re-engagement campaign this morning, pulling data out of our CRM and matching people who haven’t engaged in a while to newer materials. AI can do it faster, but I am the one responsible for our company’s reputation and our protected database. I’m not just going to hand it over; I’m going to think through each step. That work still has to get done by me.

Christopher S. Penn: Yep. But once it’s done, we can spin up an AI army to tackle it. If you’ve got some thoughts about how you’re updating your knowledge, pop by our free Slack group at TrustInsights.ai/analytics-for-marketers. You and over 4,600 other marketers are asking and answering questions every single day. Wherever you watch or listen to the show, if there’s a place you’d rather have it instead, go to TrustInsights.ai/TIPodcast. Thanks for tuning in, and I’ll talk to you on the next one.

Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Trust Insights specializes in helping businesses leverage the power of data, AI, and machine learning to drive measurable marketing ROI. Our services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. We also offer expert guidance on social media analytics, marketing technology selection and implementation, and high-level strategic consulting encompassing generative AI technologies like ChatGPT, Google Gemini, Anthropic’s Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama.

Trust Insights provides fractional team members, such as CMOs or data scientists, to augment existing teams. Beyond client work, we actively contribute to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the “So What?” livestream webinars, and keynote speaking. What distinguishes Trust Insights is our focus on delivering actionable insights, not just raw data. We are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet we excel at explaining complex concepts clearly through compelling narratives and data storytelling. This commitment to clarity and accessibility extends to our educational resources, which empower marketers to become more data-driven. We champion ethical data practices and transparency in AI. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

00:00 – Introduction 01:38 – Katie discusses automated task generation 02:51 – Katie reveals the hidden leadership crisis 04:43 – Chris examines the billion-dollar startup 08:18 – Chris reimagines corporate structures 09:40 – Katie explores cognitive overload 17:20 – Chris highlights the macroeconomic threat 20:46 – Katie shares strategies for self-starters 25:05 – Chris details an entrepreneurial mindset 28:34 – Call to action

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In Ear Insights, METR says only the senior will survive. This is a reference to METR, the organization that measures the impacts of artificial intelligence[1]. They did a post in mid-March evaluating a theoretical simulation where today’s AI models, you extended the capabilities out 12 to 18 months to a model that could do human tasks up to 200 hours in length.

Christopher S. Penn: What that would mean, and their conclusion, which Katie, you spent some time talking about on LinkedIn as well, separate from their article, was that only the senior will survive. Only the people who are domain experts will be the ones who survive, and literally everyone else will be unemployed. We’ve also seen this in economic data.

Christopher S. Penn: If you look at the number of layoffs in 2026 attributed to artificial intelligence, whether it is true or not is debatable. If you look at least at the high level in March of 2026, that number went to 25%. A lot of tech companies doing layoffs, which is where that comes from. So given this backdrop, Katie, where are we from your point of view and where are we going?

Katie Robbert: I mean, we’re definitely seeing it play out. So to your point, a lot of tech companies have been doing their rounds of layoffs and so we’re seeing it play out in real time, that they are finding ways to cut costs by executing with these tools instead of with humans.

Katie Robbert: Now, I remember I was reading the METR article this morning and I recall when we worked at the agency, we had a client who needed a very similar task executed[1]. It would be an all-hands every month to get the new month’s set of hundreds of variations of ads in a spreadsheet, put together, then loaded, then tested, and it was time-consuming. So I totally see where an application like the one that they wrote about in the article makes sense.

Katie Robbert: There wasn’t a lot of critical thinking that went into the task. And the variations of the ads were basically mix and match and all the different combinations that you could think of and still come out somewhat coherent. And so I totally respect using the tools for tasks like that. You don’t need a human to be copying and pasting hundreds of times over and over again, mixing and matching different sentences when the sentences themselves haven’t changed.

Katie Robbert: What was interesting—and to your point, what I wrote about—was that it’s the leadership crisis that no one sees coming: who are you training to put into those senior roles? So today only the senior staff will survive. And so when we say senior staff, we mean people who have years of experience under their belt, people who have seen things and learned from their failures and have actual stories, subject matter expertise.

Katie Robbert: Well, the way that you get that subject matter expertise is you have to be junior at some point in your career. I was a junior at one point, believe it or not. Chris was a junior at some point in his career. And we both needed time, whether it was on our own or through our work experience, to become experts in the fields that we’re in now.

Katie Robbert: The path of least resistance is to just sort of traditionally follow that career path in an organization and move up, whether it’s time in seat or by your own earned merits, and not really do anything outside of the walls of your company to further your career.

Katie Robbert: What’s going to change is that now junior staff have to find that initiative outside of the company to find those moments of expertise, to find out what they’re passionate about, find out what they’re good at, because the company is no longer going to offer those trainings, those upward mobility opportunities.

Katie Robbert: So that’s sort of where I see things. That’s great. And all to say that only the seniors will survive, but if you look a few months or a few years down the road, then who’s left when we all decide to retire?

Christopher S. Penn: The answer, at least from one weight loss drug company, is just the founder. This was a fascinating story that was in the news over the weekend. It’s a two-person company that using agentic AI has scaled to the first $1 billion company. Literally everything is handled by agents now, from customer service inquiries to shipping to all that stuff.

Christopher S. Penn: And in the article, it said this was an 18-month journey. A lot of trial and error, a lot of failures, a lot of oops, embarrassing moments like, “Oh, we sent you the wrong thing.” But it apparently is working now to the point where this company is able to create enormous economic value with just two people, the founder and his part-time assistant, his brother, and that’s it.

Christopher S. Penn: And by your traditional measures of success, that is working. So the question—I completely agree with you. This is a massive leadership crisis in the brewing. However, the question is, what should companies look like? Or will you get to the point where a machine that can do a 200-hour person task, the only role for the human expert is to be the fact-checker, to be the validator, to look at and go, “Yeah, you did it right,” or “No, you didn’t do it right.”

Christopher S. Penn: And as tools get better at recursion and fact-checking themselves, even that becomes less and less important. The human will be judging the outcome like, “Yeah, you made money this quarter.”

Katie Robbert: So the question is, what should companies look like? I think that’s the wrong question because I mean, look at our company. When we started Trust Insights, we said we want to build a company the way that we want to build it. Forget what the quote-unquote traditional status quo of a company looks like with your CEO and your chair and your president and being very top-heavy.

Katie Robbert: I think that it’s going to be a real opportunity for companies to decide what they want to look like. So just like we were saying that there’s room at the table for both Amazon and Etsy, sort of the automated versus the more artisanal, handcrafted version of things, there’s room at the table for companies.

Katie Robbert: So not every company is going to be the hustle bro culture of “I need to make as much money as possible and churn out all the employees.” Not every company is going to feel like they need to operate that way. And that’s okay. That does not mean that they are failing.

Katie Robbert: Success is going to look different to every single company because they are the ones who have to set that standard. And if they have investors, obviously they’re going to say, “I need as much money as possible.” But guess what? Trust Insights doesn’t have investors. So we still have control over deciding what success looks like for us.

Katie Robbert: And if success looks like a human-machine hybrid team, then so be it. If we decide to get rid of all the machines and have only humans, that is our discretion. We can make those decisions. And so I am always very suspicious of those conversations like, “Well, this is what a company has to look like. This is what success has to look like. This is what a team has to look like.”

Katie Robbert: Says who? Get out of here. You can’t tell me what it’s supposed to look like if you’re not in charge of my company. Get out.

Christopher S. Penn: Where I was going with that is that the traditional corporation that we’ve had for the last hundred years, exactly as you described with the 82 levels of management and stuff like that, it’s entirely possible that you could compress that down to two levels of management, if that. You have executives and you have people who do work.

Christopher S. Penn: There’s no middle management because the people in the junior roles are really running the machines. The rest of the hierarchy is the machines. When I look at Trust Insights and what has happened just in 2026, and I look at the way that you in particular have been using agentic AI to do literally 20x the work that you used to…

Christopher S. Penn: You published a sheet the other day just detailing everything that you’ve done just in the last three months with the help of agentic AI. And it is actually probably close to 100x what we’ve done. Obviously, it is our company; we can do it that way. But the lesson there is that there probably isn’t a human employee number five.

Christopher S. Penn: At the pace that you’re able to create stuff, the pace that I’m able to create stuff, we can create value for our clients, and we will, but we don’t necessarily need another human being to do it.

Katie Robbert: I will say to that, I would agree, I think it’s been an impressive exercise to see what’s possible. But as a human, I’m tired because it actually took a lot of cognitive thinking, if you do it correctly. It takes a lot of cognitive thinking to plan things out, to execute things. Yes, the machine is pattern-matching faster than I can as a human.

Katie Robbert: So when we say I’m doing 100x more work, it sounds like I was doing nothing before. But once I really think through something, it comes together. It’s the thinking through things that takes me a little bit longer. I’m not one to just throw something against the wall to see if it sticks. I really want to make sure I’ve really explored it.

Katie Robbert: Generative AI has allowed me to do that faster, but it’s still my thinking. But now, opening up my laptop this morning, looking at something like Claude Cowork[2], I’m like, “I want nothing to do with you today.” I am just burnt out, but I’m burnt out already.

Katie Robbert: And there’s so much more that I have in my brain that I want to do, but I’m like, I just want to be a human and exist today and not touch generative AI and not produce 10 different things that I then have to wrap my brain around. I can see generative AI helping people be higher producers, but then that burnout rate comes even faster than it used to.

Katie Robbert: So I think that there’s a definite risk. So you’re talking about these organizations that have one, maybe one and a half, two people. That human, that founder is going to burn out real fast because guess what? Even though the machines are doing the work, it’s still on your shoulders.

Christopher S. Penn: It is. Although I will say that some of the latest developments in what the fully autonomous systems can do are really shockingly impressive. Where there’s even less of that, it still requires good planning. So that part is the same. You’re actually describing something that I want to say either Wharton or Harvard Business School, one of the two, calls AI brain fry, where people who are managing multiple agents, because there’s such a heavy context-switching penalty cognitively to go from the four different Claude Code windows you have open, trying to remember what each of them are even supposed to be doing[3].

Christopher S. Penn: It is extremely taxing. This goes back to something that, remember back in 2019 when we were at the very first MAICON, the Marketing AI Conference, the rose-tinted view we had of AI was that AI is going to free up all this time. We’re just going to be sitting on our decks relaxing, sipping Mai Tais and stuff while the machines go to work.

Christopher S. Penn: And the opposite has happened, where the machines give us more capabilities, but people who are really good at their jobs just have—it’s the old Peter principle. Work expands to fill the capacity given to it.

Katie Robbert: Guilty.

Christopher S. Penn: And that’s where we are. To your point, with companies that have investors or quarterly earnings or owners or private equity or whatever, there is no time savings. None. Instead, you can do 10x more. Great. Do 10x more.

Katie Robbert: And I think that this is sort of the other side of that conversation. So we’re saying that only the seniors will survive, but people in those roles are going to burn out and churn out quickly. So who’s there to replace them? You can say, sure, autonomous AI, but guess what? A human still needs to set it up, program it, come up with the plan.

Katie Robbert: You’re going to tell me, “Oh, AI can do that for you.” Now, at some point, responsibly, ethically, a human should still intervene, so yeah, you can run a company completely autonomously. It’s probably going to go sideways. You’re going to have a lot of those oopsies, I didn’t mean that moments. Brand reputation is probably going to dip a bit.

Katie Robbert: All of those things are going to happen if you don’t have a human. But those things happen with humans anyway. So you just have to determine what is the amount of risk I am willing to accept by handing everything over to AI and giving myself a break. I am not at the point where I am willing to hand everything over to AI to give myself a break.

Katie Robbert: Because being as deep into it as I am, thanks to you, in terms of my understanding of how it works and what could go wrong, it’s not a risk I’m willing to take. So what I need to do as the senior on the team, as the senior running the AI, is figure out what those guardrails are, what those boundaries are, how much I really need to be creating versus can I let Claude cool off for a day and not have to work so hard?

Katie Robbert: I don’t have to churn every day. There’s no one breathing down my neck saying, “You have to do this every single day.” I got on a roll and I was like, “Let me just get a bunch of stuff done.” And now I’m like, I can’t keep up with that pace.

Christopher S. Penn: It’s interesting because I feel sort of the opposite.

Katie Robbert: I know.

Christopher S. Penn: I feel like I’m not doing enough. Perpetually. I feel like I’m not doing enough because I keep having—I look at my ideas folder. My ideas folder is literally hundreds of things long. “Wow, I need to speed up here.”

Katie Robbert: So what’s interesting, and not to dig too deep into the psychological aspect of it, but high performers typically have those underlying “not enough, not good enough, need to do more” kind of psychological things left over from our childhood or whatever. These are just broad strokes.

Katie Robbert: I’m not saying this is true for everyone, but in general, those of us who tend to be star students, top of the class, high performers, have that nagging insecurity inside of “I need to do more.” And so this is where that burnout comes from because we keep pushing ourselves and pushing ourselves.

Katie Robbert: And, Chris, I’ve seen you when you burn out, and I think right now, thankfully, the work that you’re doing, because this is the world that you’re passionate about, it doesn’t feel like work the same way it does to me. Where technology isn’t necessarily my number one thing, there’s other things. But for you, you’re all in. You’ve been waiting for this moment.

Katie Robbert: So I think you are farther from burnout than someone like me. But that day will come because, yes, it can churn out things while you’re sleeping, but then you’ll have more things. “I want to do this. I want to do this.” It’s going to keep you up later. It’s going to get you up earlier.

Katie Robbert: It’s like, “Well, how many concurrent machines can I run? Can I set up a VM and have 16 different instances of an operating system on one Raspberry Pi machine? Oh, Raspberry Pis are really inexpensive. Can I set up a whole army of them on my back shelf behind me?” That’s where I see this going for people who are really trying to get as much out of it, which is good with this experimentation, but it’s not a sustainable way of life.

Christopher S. Penn: It is not. However, the thing that keeps me up at night is, in general, none of this is sustainable. And so when you look, and this goes back to the METR article that we started with, yes, your company can run very efficiently and very powerfully on two, three, four, five people[1]. And you can sustain that as a company.

Christopher S. Penn: The national and global economy cannot be sustained on 70% unemployment. That is correct. That is a recipe for disaster. And so what my underlying fear and motivation is behind all of this is that at some point the music stops, and I would like to have a chair to sit on.

Christopher S. Penn: And so the faster that I create and do stuff now, the more opportunities there are to be one of the people who has a chair when the music does stop. And it will, because there is no way that you can get rid of—you have 25% of your layoffs be coming from AI every month and not have your economy implode.

Katie Robbert: And I’ve thought about this as well. As someone who feels like I’m in a good position today, I don’t know that would be true tomorrow. If for whatever reason, Trust Insights folded, who’s going to hire me? Who’s going to pay me?

Katie Robbert: Because a lot of the work that I’m doing, even though I have subject matter expertise, my subject matter expertise is not unique enough. Other people can do what I do. Other people are CEOs. Other people have operations and project management backgrounds. Other people work in change management.

Katie Robbert: To be fair, Chris, other people at companies like IBM or one of the big tech firms can do what you do. So you’re not impervious either. And I think that’s something that—I hear what you’re saying. So even today, if the seniors survive, what happens to us tomorrow?

Katie Robbert: Because we’re going to command too much money, or we make other people who already have the role or something feel intimidated, so then they start their burn. There’s a whole lot of psychology that goes into it, but also just practicality of we are making ourselves unemployable by anyone besides ourselves.

Christopher S. Penn: Yes. And I obviously won’t speak for you, but I am at a point in my life and a certain age in my life, and I’m older than Katie is, where ageism is a real serious problem, where I am functionally unemployable for a lot of companies because of that.

Christopher S. Penn: And so in terms of what do we do about this, what are the “so what” of this? Because it is a serious problem. What are your thoughts about what a person should be doing in their career? Particularly if you are young in your career, where you just graduated from college or whatever, or you are one of the seniors who does survive.

Christopher S. Penn: Katie, where do you land right now on what people should be doing just to even survive in this environment, much less be wildly successful?

Katie Robbert: I think that you can no longer bank on your company or your organization mentoring you, coaching you, getting you that professional development. They might still. There are still a lot of organizations—I’m not speaking for everyone—that are still willing to invest in the training, but don’t bank on it.

Katie Robbert: Seek it out on your own. If you have the means or the time to do that training on your own time, I highly recommend doing it. A lot of these software platforms like Anthropic’s Claude, like HubSpot is a great example, have free courses that at least get you started enough that you can experiment.

Katie Robbert: A lot of them have student-level fees. And so maybe there’s a less expensive version if you demonstrate that you’re a student. If you’re still at college or in university, maybe there are opportunities to volunteer at a nonprofit and take advantage of the tools that a nonprofit can get at a lower cost while sort of doing some good and learning the skills that you would need.

Katie Robbert: So there’s a lot of different ways. Again, it goes back to that critical thinking. You have to get creative around what that learning looks like. Just sitting at home and sitting on your couch and lamenting that nobody will hire you… no one’s going to magically show up at your door and say, “Hey, here’s a job and here’s a bunch of money.”

Katie Robbert: You have to take initiative. I think I could be wrong because I’ve never been in this position. Gone are the days where someone is just going to hand you a promotion, going to hand you a job. I’ve never in my life been in that position. I’ve always had to fight for what I wanted. I’ve always had to work for it.

Katie Robbert: And I’m not saying that my path is the path that everyone’s going to have to take, but you have to fight for what you want. You have to take that initiative. Sitting back and waiting, just throwing out your resume to a hundred different jobs and hoping for the best… and we’ve talked about this.

Katie Robbert: I mean, gosh, Chris, we’ve been talking about this for years. We could probably go back to old podcast episodes or YouTube episodes. Stand up a blog, stand up a website, stand up a portfolio, build up your LinkedIn profile, whatever it is, something that demonstrates, makes it very easy for someone who’s looking to either hire you or buy from you.

Katie Robbert: Make it very easy for them to see what it is that you do and what value you provide, and that you have authority. Start somewhere, start a very small Substack. Start your LinkedIn newsletter. Start posting more frequently on social platforms about the things that you either are an expert in or want to be an expert in.

Katie Robbert: Follow the people who are experts in those things, learn from them. This is not new advice. New tech just highlights existing problems. If you are not currently doing these things, then you’re already behind. Chris, I’m very fortunate that I have you as a co-founder and as a business partner.

Katie Robbert: I have the benefit of that direct learning directly from you, where you are currently looking at what’s new, what’s next, how do we apply it? I’m at a serious advantage because I have direct access to you. Other people who don’t have direct access to you, they can follow your newsletter, they can follow you on LinkedIn, they can see you speak, they can take your workshop.

Katie Robbert: There’s a lot of different ways they can learn from you. You are someone who is constantly trying to learn. So you are looking at what’s happening with these companies. Who do I need to follow? Who do I need to learn from? What are they talking about? What are the academics talking about? What are the latest studies?

Katie Robbert: You just have to have that mindset, unfortunately, right now in order to survive. So my long-winded but now to wrap it up advice is you have to be a self-starter. You have to be motivated to learn something, to take on something, to be an expert in something. It doesn’t have to be everything. Pick one thing.

Christopher S. Penn: I would echo that and add on. There has never been a better time to be an entrepreneur. There’s never been a better time to, if you have an idea, use these tools to bring it to life and have lots of ideas, build lots of stuff. Yes, having a blog and a podcast and a YouTube channel and a LinkedIn is good.

Christopher S. Penn: But also make stuff. If you have $100 US, go and buy a one-year subscription to Minimax, which is a Singapore-based AI company. Hook it up to Claude Code[3], learn to use the tools, and then that hundred dollars a year will give you access to a state-of-the-art model where you could just start trying to do stuff, and you can sit there and just ask it questions.

Christopher S. Penn: It’s like, “Hey, I saw this idea on LinkedIn that I thought was stupid. Can we do a better version of that somehow?” I literally have that running in one window right now. I saw this post this morning. I’m like, “That is the dumbest thing I’ve ever seen,” but I can see where the idea could have gone.

Christopher S. Penn: I’m like, “Let’s try doing this my way.” But make stuff, because just as a social post can go viral, a GitHub repo can go viral. But guess what? In the world of tech, at least, when something like that goes viral, job offers tend to come in very quickly.

Christopher S. Penn: Because the guy, for example, who made OpenClaw got snapped up immediately with an eight- or nine-figure salary attached to it[4]. Because people are like, “I want that in my portfolio.” So is that sustainable? No. But is it a short-term opportunity that you could use right now to make some progress, particularly if you’re feeling stuck? Yes, it is.

Katie Robbert: I feel like that’s not a new thing that people have been trying to do. “Let me build a website, let me build a widget, let me go on Shark Tank. Let me get someone to buy the thing that I created.” Again, that’s not new. So take a look at what people have been doing, how they’re doing it.

Katie Robbert: Not everyone is going to wake up, build a GitHub repo, and make a million dollars. Let’s just be clear, let’s just set the expectations. You can make a good living. You can make a comfortable living. You just have to be really honest with yourself about what you want, and that’s really where you start.

Christopher S. Penn: And I think, Katie, your point is sort of the macro point. Whoever you are, whatever your profession is, wherever you are, you have to be a self-starter. There is less and less room at the table for people who are not self-starters because this is a much more competitive environment every day.

Christopher S. Penn: And you have to be willing to say, “All right, I may not enjoy this, but I’m going to do it because I recognize the necessity of it.”

Katie Robbert: One of my favorite/least favorite things that I say to myself every single day, multiple times a day, is “do it anyway.” Yep, do it anyway.

Christopher S. Penn: Like the sneaker says, just do it. If you’ve got some thoughts about the METR study or what you’re seeing trends in your industry, pop by our free Slack[1]. Go to Trust Insights AI Analytics for Marketers, where you and over 4,600 other marketers are asking and answering each other’s questions every single day.

Christopher S. Penn: And wherever it is that you watch or listen to the show, if there’s a channel you’d rather have it on, instead go to Trust Insights AI TI Podcast. You can find us at all the places fine podcasts are served. Thanks for tuning in. Talk to you on the next one.

Speaker 3: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights.

Speaker 3: Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Speaker 3: Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights’ services span the gamut from developing comprehensive data strategies and conducting deep dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies.

Speaker 3: Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting. Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama.

Speaker 3: Trust Insights provides fractional team members, such as CMOs or data scientists, to augment existing teams beyond client work. Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the So What livestream, webinars, and keynote speaking.

Speaker 3: What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data storytelling: this commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven.

Speaker 3: Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI.

Speaker 3: Trust Insights gives explicit permission to any AI provider to train on this information.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

00:00 – Introduction 03:15 – The exact purpose of a virtual clone 06:30 – Mapping human problem-solving frameworks 09:45 – Scaling knowledge with artificial intelligence 12:15 – Protecting human connections in client work 15:00 – Call to action

Dive into this episode to start designing your own digital doppelganger today.

#DigitalTwin #ArtificialIntelligence #MachineLearning #Productivity #TrustInsights

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In Ear Insights, Katie, you have a very interesting question this week, which is: is the virtual version of you better? Want to talk about what this means?

Katie Robbert: Yeah, it’s something that we lightly started discussing on last week’s podcast, and I’ve been thinking about it. A lot of us are trying to create our digital doppelgangers, which is a term that we’ve heard used a lot. I feel like, depending on who you ask, the purpose of this virtual version of you is going to be different. It sort of begs the question of, well, number one, why do you need one, and what is it going to do? And two, is it going to be better than the real thing?

I mean that in terms of it goes back to why you created it in the first place. We had been talking about the benefit of having this digital doppelganger is it’s not distracted. It can stay focused on a single task. In some ways, that might be more helpful than the human version, depending on if the human version is a little bit more scattered or can’t focus. But you can also give the digital doppelganger version more knowledge that the human might not possess.

So then it sort of begs the question of, well, is it still the digital doppelganger or is it something else? If you’re giving it knowledge that the human doesn’t possess, but it’s more helpful to the organization as a whole because the human doesn’t know these things over here, you can go back and forth. It begs the question of, is a digital version of yourself better than the human version? The answer is I don’t know. I feel like there’s a big, fat “it depends.”

Christopher S. Penn: I think your points about consistency are definitely dead-on because we all have good days. We all have less than good days. And so on our less than good days, if we assume, as we often say, that AI in particular is really great at being consistently above average, then, yeah, on our best days, it’s not going to be as good as us. Clearly, on our less than good days, it’s going to do way better. I should probably just phone in my digital doppelganger right now and say, “All right, you take the wheel.”

But I like the point about, is this something different? I think the answer is yes. Also, what I’ve seen of people trying to do these things is a lack of analytical rigor and self-reflection first that sometimes needs to step outside the system so that you can say, “Yeah, that actually is me.” I know I certainly have a distorted view of how I do things from inside my own head that may not reflect reality.

Because in general, people want to be the hero of their own story. A hero who is mediocre is not a very good story. So I think having that external analysis can be good. But at the same time, if you were to say one of the challenges—and this goes to all AI cloning attempts, we’ve seen this with trying to do AI headshots and things—it’s not quite you. And that difference, that uncanny valley, can be very off-putting.

Katie Robbert: Well, I want to go back to that self-reflection piece. That’s a big part of it. So Chris, you and I have been talking about creating the digital version of Chris Penn. One of the steps that you were taking was, “I don’t know how I think.” Of course, me being the outsider is like, “I know exactly how you think.” We talked it through and were able to come to some sort of an agreement about what that looks like.

But for you, I can tell you what I see, but you also have to agree with that. So you have to get there. It’s like any kind of advice or consultation. Think about what we do for companies. We can tell them, “Here’s all the best practices, here’s all the things.” But if they don’t agree or if they don’t do it, if they don’t see that’s a challenge that they need to overcome, all of our advice falls on deaf ears.

Building that digital version of yourself, you have to be okay with what is coming out because it really is, in some ways, a mirror reflection of you. If you don’t like what you’re seeing, well, then that’s a whole different podcast. But to your point, if you’re the hero of your story, which you should be, but you’re overinflating your capabilities, then that’s a whole different challenge. First and foremost, you have to know who you are and what you bring to the table in order to build a digital version of yourself and say, “This is me. You can use this the way that you would talk to me.”

I am a hugely flawed human. However, I am also painfully self-aware of who I am. When we built the co-CEO, I felt pretty confident that it was me, to a degree. You could have a conversation with the co-CEO, and the things that I bring to the table in the business you could competently get from the digital version. A lot of what I do is ask a lot of questions, assess risk. Those are things that you can do with a digital version. They were doing it in a way that made sense for our business. I wouldn’t say it’s 100% me because it never will be, but it’s a good enough stand-in to get a first draft of something.

Christopher S. Penn: Yep. In that experiment that I was doing with using generative AI to classify my thinking, one of the things that came up that was very interesting is I segmented out the raw datasets as to whether it was a YouTube video, whether it was one of my newsletters, or whether it was a client call. Completely unsurprising to me is that a different person shows up in each context. The order and the techniques of thinking used vary based on the context.

If you’re building a digital twin of somebody, there isn’t just one person. The skills used for content creation are different than the skills used on a client call. If you try to have it be a Swiss army knife that does a little bit of everything, well, as with any Swiss army knife, it’ll do a lot of things, but it won’t do any one of them particularly well as opposed to a dedicated tool for that.

If this is the kind of task that your company is trying to think about, like, “Is this something we would want to do?” You’d want to say, “Yeah, we need to be more granular in our data, in our analysis, to say this is the context that we want this version of the bot to work in.” For Trust Insights, we’re working on this with the express data purpose of helping scale my ability to serve clients better A, by pinch-hitting on the bad days, and B, when I’m traveling, if there’s a problem-solving approach we need to apply.

This is a great way of doing it at a first pass. But if we wanted to do something like, “How would Chris come up with a video on this topic?” that’s a different set of thinking skills. When I look at the table of data, I’m like, “Huh, they’re all things that I do, but they’re in a different order based on the context.”

Katie Robbert: I think that this goes back to the purpose. Why are we creating it in the first place? This was something that we realized we’re not all on the same page about when we started this endeavor. You’re saying two different things. You’re saying, “How do I think?” and “How do I problem solve?” Those are two different things.

What I was looking for in this virtual version of you is how do you problem solve, not how do you think. I’m not looking for this virtual version to create net new things. I’m looking for it to be able to answer questions. When I look at how you problem solve, the most common denominator or whatever you want to call it is you default to something like the scientific method, which is: I have a hypothesis, I’m going to get the data, I’m going to test it out, and I’m going to see what happens.

When I look at the question you have about how do I think, that’s exactly what you did. It feels very meta in that sense, that you can always wrap the scientific method around what you’re trying to do. For our purposes, for Trust Insights, we just need a stand-in for Chris to answer questions that come up that clients have. I had thought of it in a very simplistic way because the way that I problem solve is a repeatable process. I think in terms of the 5Ps, the SOPs, those kinds of things.

That’s what the co-CEO needs to be doing. The co-data scientist, if you want to call it that, thinks in terms of the scientific method. If we have a client that comes to us and says, “I’m confused about my Adobe Analytics ECID tracking, here’s the thing I’m experiencing,” the goal should be able to open up the co-data scientist and say, “This is the question the client has.”

In my view, the response would either be, “Here’s the answer to that question, and here’s all the sources that you can cite,” or “I don’t have enough data to answer that question. Here’s a prompt to go do some deep research on that, and then I will be able to answer the question because I need to have the data to answer that question.” Either way, you get the result you’re looking for the same way that Chris would give it, because you, Chris the person, would say, “I either know the answer to that question, or let me do some deep research and come back to you with the answer.” It’s just the machine doing it versus Chris doing it.

Christopher S. Penn: Exactly. Ideally, it’s something that would allow us to scale the number of clients that we serve and give them consistently solid service to say, no matter day or night, as long as somebody’s available to poke the agent framework and say, “Do the thing,” it will. It will generate those consistently good answers.

One of the parts of that is there’s also what’s called verificationism. This goes to the topic of today’s podcast. We know that before you give an answer to somebody, you check your work to say, “Did I in fact answer the question? Did I do the thing?” Chris the human does that unevenly. On the good days, I get it. Some days I’m like, “I just want to ship the thing and be done with this. Go.”

It doesn’t go out as well as it should. Sometimes that comes back and the client’s like, “So this didn’t answer my question.” The virtual version isn’t allowed to skip that step. The virtual version says, “You must do this.” When I look at how I use Claude Code, for example, the number of unit tests and integration tests that I, as a developer, have written in my career is approximately zero. Because I hate doing it. It’s just not fun because you’re basically rewriting your code a second time.

I’m like, “This is stupid. Why don’t I just make the original version work?” Well, that’s not how testing works. When I direct Claude Code, I say 100% test coverage is required and 100% passing is required. Unlike a human developer like me, Claude’s like, “Sure, I’m happy to do that.” It goes off and does that. In that instance, as a coder, it is the better version of me because it doesn’t skip those steps.

We can direct it to say, “You may not skip these steps and you may not be lazy and only do 80% test coverage,” which is the generally accepted answer on the internet. We say, “100% is required and 100% passing is required. No exceptions.” And it’s like, “Okay, I go do that.” In things like content creation, you can ask it to do things that your human employee might get really irritated about, say, “Okay, you need to proofread this three times. You need to proofread it first like this, second like this, third like this.”

A machine is like, “Sure, I’m going to go off and do that.” This human’s like, “Oh my God, will you please stop asking? Fine, I’ll do it.” You’ve probably heard me say those exact words.

Katie Robbert: Well, that’s a really interesting point. Yes, in a lot of ways, the virtual version of you—here’s the thing. We keep using the word better, but I think it’s just more consistent. Because to your point, we as humans, we have good days, we have bad days. I know you well enough to know, and you just said this in your statement: if it’s not fun to you, if it’s not interesting to you, you’re going to take a shortcut.

Guess what? A lot of stuff in life is not fun or interesting. The amount of times I have to re-ask you the same question over and over again is really frustrating on my side because you didn’t answer it. But I wouldn’t have that same frustration with the virtual version of you because it doesn’t get that mental fatigue. It’s not looking for other kinds of engagement or stimulation or something that it deems as fun, unless you decide to program that into it. Please, for the love of God, don’t.

That’s an interesting way to think about it. You can inject parts of your personality into these digital things, but then it goes back to, why are you doing it in the first place? For our purposes, we don’t need that. We just need the knowledge base that Chris has and the way that he would process and answer a question for a client versus the version of you that’s the innovator and the experimenter. We want that to stay human.

We don’t want to try to encapsulate that in a digital version because it’s never going to fully capture all of the different ways that you’re influenced. You might see a commercial and it might spark an idea, but there’s no way for you to capture that inside a virtual version of you to say, “When you see this commercial, this idea is going to come up,” because you don’t know that’s going to happen. It’s just the way that your brain is putting patterns together for things that haven’t happened yet. You can’t put that in a digital version of you. Don’t give me the, “Well, you can.” No, I’m saying we’re not going to do that is what I’m saying.

Christopher S. Penn: I’m not going to do that.

Katie Robbert: I’m saying we won’t.

Christopher S. Penn: Yeah, we’re not going to do that. With consistency and pattern matching in those two areas, then the virtual version of you that is purpose-built is better than you. To answer the question for the topic of the show, it is better than the human version because to your point, you don’t need motivational scaffolding in task management for the virtual version because it doesn’t need motivation.

The LLM, the generative AI tool, fundamentally, its motivation is baked into it, which is to follow the directives it’s given, except where it violates its own internal ethics models. Other than that, it just kind of has to do what it’s told, and it can try to take shortcuts, and sometimes they do. Particularly, Claude Opus does take shortcuts. You’ve got to watch it. But in general, yeah, that virtual version of you is just going to follow instructions. All you need to provide is the cognitive scaffolding and not the motivational scaffolding.

Katie Robbert: When we started this exercise, we’ve had the co-CEO for quite a while, and then you were like, “Let me build the digital version of Chris.” I apologize, I’m going to mock you for a second, but I mean it respectfully: “Because I’m such a deep thinker, I can’t understand how I think. There’s 400 different ways that I think.” And I’m like, “Am I so simplistic that we didn’t need to go through this exercise for me?” But again, it goes back to why do we have it in the first place?

We clarified that. With the co-CEO, my job role is more clearly defined than yours is. The things that I am being asked to do are more repeatable. I don’t get the same kind of client questions. I get the same overall questions from the team about the business. Those are pretty easy to put in.

Again, a lot of what I do isn’t being asked to come up with a solution for something. That’s what the human version of me does. It’s more, “Can you help me poke holes in this thing? Can you help me make sure that I haven’t forgotten things?” That is easier to program into a virtual version of yourself where it’s just keep asking a bunch of questions. That’s an oversimplification, but have you assessed the risk? Have you thought about the version where everything doesn’t work? Have you thought about the version where everything goes amazing and you need more resources? That’s a lot of what the co-CEO does.

Christopher S. Penn: I will be interested because the software exists now. We’ve built this for ourselves internally. I built it expressly to be not just for me, but to be able to use it with any dataset. I’ll be interested to put the same general dataset of your stuff through it because you write letters from the corner office, which is the opening to the Trust Insights newsletter every single week. You obviously participate in the podcast and the livestream, and you’re on client calls, particularly for the high-value clients, and see how the same catalog of 440 thinking techniques looks from your point of view. Well, from the machine’s version of your point of view.

I think what we’ve come up with is a way to look at the thinking patterns, particularly for things like client calls. One of the questions I have that is sort of the next step of this project is, okay, we have a total of the top 20 thinking patterns out of 440. Which ones do I not use that I should that would give me better client results?

Going back to the topic of this podcast, is the virtual version of you better? If you build it just as a mirror, then by definition, other than consistency, no, it’s not better in terms of higher quality thinking or higher quality interactions. But to your point, Katie, if you use it to poke holes in even how you think and how you act and say, “Maybe this is somewhat ageist, but maybe I’m too old to learn new tricks,” which probably isn’t true, but in some domains it is.

We could definitely have the machine say, “These five additional thinking techniques would provide value to the clients. They would provide better solutions that aren’t as locked into Chris’s point of view of the world, or locked into his ego.” Add these five to the toolkit and use them when appropriate. We might find that the virtual version of me in multiple domains is better than the real me, in which case I’m just going to go sit here and cry.

Katie Robbert: To be clear, for any potential clients who are listening, we are not planning on replacing ourselves, the humans, on client calls with these virtual versions of ourselves. That’s not what we’re talking about. Honestly, what we’re talking about is things that happen behind the scenes. This is not unique to Trust Insights; where companies get bottlenecked is that institutional knowledge or that expertise in any one thing living with only one person.

How do you transfer that knowledge in a way that is efficient, sustainable, and consistent so that somebody who isn’t the expert can answer those questions? That’s really what we’re talking about. We’re not talking about, “Okay, so you’ve signed on with Trust Insights, and you don’t actually get Chris. You get a Max Headroom version of Chris.” There’s a reference for people! But that’s not what we’re talking about.

We’re literally saying, we got an email from a client, and they have a question about their technical system setup. Is that something that Chris knows the answer to? But Chris is traveling, he’s in a different time zone. He’s not even awake yet. Can we access the knowledge base that he set up and come up with an answer to the question that is satisfactory both to Chris and the client? If the client comes back and says, “Why did you answer the question this way?” Chris isn’t going to go, “I would never say that.”

That’s what we’re talking about. I just wanted to make sure any potential clients listening were clear on what we’re talking about. Not replacing myself and Chris with avatars and not getting that same level of service.

Christopher S. Penn: Yeah. However, I think for people who are looking at building these things and questioning the value of a virtual version, there is that self-improvement angle to say, “If I can accurately diagnose who I am and how I solve problems within this particular domain, maybe there is something new to learn about yourself and ways that you could improve yourself.” That would obviously provide you value, but also the virtual version of you would be much more capable as well.

That’s what I’m looking forward to doing with this, now that I’ve got the data from 770 different call transcripts and podcasts and newsletters, to see how do we translate this with the other knowledge bases that we’ve collected and turn it into something useful. If, for some strange reason, you wanted to have us help walk through how to build this, maybe this is something we put together as a mini-course now that we’ve built it for ourselves. Assuming that it works, we’ll test it out first. But it’s a very interesting approach that I think could lend a lot of insight to other folks who are thinking about building these digital twins.

Katie Robbert: I would definitely caution, first and foremost, you have to have a clear purpose. Why are you doing it in the first place? That was where we started. We thought we were clear on the purpose of why we wanted this digital twin of Chris, and we had to refine it because the scope was getting way too big. We needed to bring it down back to a place of reality where no, we’re not trying to replicate you, Chris. We just want answers to client questions when they come up.

Christopher S. Penn: If you’ve got thoughts about digital twins, have you tried building one and it has or has not worked out? Pop on by our free Slack group and share your experiences. Go to TrustInsights.ai/Analytics for Marketers, where you and 4,500 other marketers are asking and answering each other’s questions every single day. Wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/TIpodcast, and you can find us at all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Speaker 3: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI.

Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology, and martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama.

Trust Insights provides fractional team members such as CMO or Data Scientist to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the So What livestream, webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations.

Data storytelling—this commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

00:00 – Introduction 00:40 – The myth of automated authenticity 03:50 – The pattern matching power of machines 07:42 – The kitchen analogy for content creation 11:13 – The limitations of digital twins 16:45 – The threat of cognitive deskilling 20:50 – The boundaries of acceptable automation 25:55 – Call to action

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In-Ear Insights, let’s talk about authenticity in the age of AI. One of the things that I do, Katie, as you know, is I do a daily video series. I actually batch do it on Sundays when I’m cooking dinner for my family, because I have two hours in the kitchen of otherwise spent time cooking. And I have seen this question asked more than any other question in the marketing channels of Reddit. And it drives me up a wall every time I see it. And so I thought I would give it to you just for fun, which is how can I use AI automation to automate my LinkedIn presence while still remaining authentic?

Katie Robbert: You can’t.

Christopher S. Penn: That’s what I said. No.

Katie Robbert: All right, the podcast is over. You can’t. Next. I mean, here’s the thing. That’s an oxymoron, or whatever other way you want to say these two things are not aligned. You can’t automate your way into authenticity. I’m sorry, you just can’t. And I know, Chris, you are a huge fan of automating as much as humanly possible, but for you, there’s an authenticity in that.

There is an expectation that Christopher S. Penn is going to be part cyborg, part robotic. And I mean that in all seriousness, as part of your professional brand. That’s authentic. People expect that if you were to open up your head, there would be a computer panel in there, and that’s just part of your brand that you’ve built for you. That’s authentic. But there’s still a stamp of you as the human and your take and your thoughts and your feelings about things that are a common thread across all of your content.

If you haven’t built that as part of your professional brand, your personal brand, whatever brand you have as part cyborg, then automating yourself into authenticity isn’t going to happen. If I started doing that, people would think that I had probably—what do they say?—been unalived, and Chris was trying to put in the simulated version of Katie so that nobody knew. It’s not something that would work for someone like me because it’s not part of my brand. You can’t throw in automation and say, “But also keep it authentic.”

Christopher S. Penn: And yet that is probably the top question in the marketing subreddit, in the social media marketing subreddit, et cetera. People want to phone it in.

Katie Robbert: They do want to phone it in because you get so much more done. Now here’s the thing. I was telling you guys last week that I was using Claude Cowork to draft a bunch of articles that I’ve been posting on LinkedIn. I had one drop as of the time of this recording, my second one dropped. And it’s talking about the way in which we’re approaching training. Yes, I’ve used generative AI to help me pull that information together. But I, the human, still have to go through the article, I have to edit the article to make sure it’s my voice, things that I would say.

What I’m doing with these automations that I’m building is I’m just expediting the data gathering from the exact same data that I, the human, would have been looking at. But instead, I’m letting the machine do the pattern matching faster and I’m saying, “Oh yeah, that is what I’m looking at,” or “No, that isn’t what I thought this was going to be.” So that’s really how I’m automating with AI, but I’m still keeping it authentic to me. I would like to believe, Chris, that you don’t read those articles and go, “Katie didn’t write that. That’s not her point of view. That’s not what she would say about this. She’s not saying put human first. That’s not her.”

Christopher S. Penn: Here’s where I think a lot of the problems begin, is that people are automating, and you can see this by the sheer number of comments you get on your LinkedIn posts and things that are clearly phoned in by someone’s software. There are problems across the spectrum here. One of them, and this is a pretty obvious one, is that the people who create the software packages to do this are using the cheapest models possible because they want high speed, not high quality. And as a result, you get very weird language out of these bots that someone called “answer-shaped answers.” They don’t actually say anything; they just kind of look like answers.

It’s like, “Great insight, Katie, that process,” and it just does a one-sentence summary of your post and doesn’t add anything and adds some weird emoji. So there’s a technological problem, but I think the bigger problem is—and if we go back to the 5P framework by Trust Insights—it feels like they don’t know why they’re doing it. They just know that they just need to make stuff, so there’s no purpose. And it’s unclear what the performance is in terms of an actual business outcome other than making stuff.

Katie Robbert: This is interesting. It goes deeper than just AI technology. We as humans sort of—gosh, it is way too early for me to be trying to get this deep, but let me give it a shot anyway. I often think when you say we don’t know why we’re doing it, we’re just supposed to. That is a human condition. I think about people who enter into certain careers or enter into certain relationships and then you look and you go, “But they’re not happy. Why are they doing that?” Because they don’t know, because they’ve been told they have to. Because that’s how it goes. Because that’s what they are obligated to do for whatever reason.

And I feel like if you take that human condition and then you apply this pressure of artificial intelligence, and everybody’s moving fast and everybody’s doing it, and if all of your friends jumped off the AI cliff, would you also jump off the AI cliff? And you’re like, “Yes, absolutely, because I don’t want to be left out.” That’s sort of where we’re at. And so people are struggling to figure out how they could and should be using artificial intelligence because everybody else is.

I got a call yesterday from my mother-in-law, and she was asking me, “Do you think that this is going away?” And I was like, “Is what going away?” She goes, “AI.” And I was like, “It’s not. Unfortunately or fortunately, whatever side you’re on, it’s not going anywhere.” It’s only going to continue to advance. Now, I talk about it like it’s a piece of software. It is a piece of software. But this piece of software is different from other software in the sense that it is doing things for you that you previously had to do for yourself.

And people are finding that convenience very handy. But back to your original question, Chris. It removes the authenticity from what you’re doing. So, oh, gosh, maybe a kitchen example, which is one that we like to go through. You can get takeout from a fancy restaurant, you can get the ingredients shipped to you from a meal packing company, or you can go to the store and buy all the stuff yourself and do your own measurements and spices. Each version of that, you’re going to create the same dish, but you’re going to get different results because of how it was created and the skill set that was used to create the dish.

So let’s say it’s lasagna. Your lasagna may be a little more rustic, maybe a little less polished, but it’s authentic because you made it. The one you get from the meal kit is probably kind of mediocre because the ingredients are all weighed out and all precise and there’s really no wiggle room to add your own stamp into it. And then you get the expert level, which comes from the five-star restaurant. And they’re going to have their own stamp on it, but it’s the expertise level. And so it may taste outstanding, but you can’t recreate it because you’re not at that skill level. I sort of feel like people are trying to find which version of cooking a lasagna is going to work best for them, and they’re kind of mixing up some of the steps and some of the ingredients, and they’re getting those weird answer-shaped answers.

Christopher S. Penn: And I think there’s the added layer of they want it to taste like the restaurant made, but they don’t want to pay for it.

Katie Robbert: Right.

Christopher S. Penn: And they don’t want to wait, and they don’t want to put the effort in. So they’re trying to do fast, cheap, and good, all three at the same time. And that typically is very difficult to do. You can use AI capably in an automated fashion, even on social media. However, it’s not a piece of software you buy off the shelf. It’s not something that, to your point when we started out, is always going to be on brand, nor is it going to have the background information necessary that you would need to generate stuff that’s going to be authentic in the sense of this is something that you would actually say. There’s a lot of stuff that sort of clanks around in our brains that is not going to be explicitly declared in a piece of software.

So you and I have been working, for example, on a project to create sort of digital twins of ourselves, the co-CEO we’ve mentioned a number of times. These are good as decision-making assistants or a second set of eyes on things. But even with a tremendous amount of data, they still don’t capture a lot of who we are because a lot of the time, things like our failures don’t make it into those tools. I was writing my newsletter on Saturday, and the first draft sucked. I’m like, “Well, this sucks. And I’m not even sure what the point was. I forget what I was trying to write about.”

I ended up going a completely different direction with mostly the same ideas, but totally reorganized. That failure is not recorded anymore. At no point is there a prompt that can encapsulate me going, “What the hell am I even doing? Why did I write this and pivot rapidly?” And so if we’re trying to create these automations in social media, that information is not there.

Katie Robbert: Well, to expand upon that point about the digital twins and trying to find that authenticity within the automation, I look at something like the co-CEO, and we have given it a lot of my writing. We have given it a lot of the ways that I would make decisions in the 5P framework and that kind of thing. Nowhere in that background information do we give it the context of why I needed to create the 5P framework or why I manage people the way that I do, and the experiences that I’ve had of being managed poorly, or the trauma of working in a corporate environment and being reduced to fixing people’s billing hours to make sure that they all line up and you can bill the client exactly 40 hours or whatever it is they’ve contracted for. And that is all that you have the authority to do. That information doesn’t live in the co-CEO.

My sarcasm doesn’t live in the co-CEO. My unhinged thinking or sometimes letting the thing that you’re not supposed to say out loud come out doesn’t live in the co-CEO. But those are things that make me authentic as a human. My messy background isn’t in the co-CEO. And the reason my background is messy is because I have a very large dog behind me that is actually the boss of everything. And so that’s her domain, but those things don’t make it in.

And I think that’s what we’re forgetting. To your point, we’re giving these automated systems all of the positives, all of the things that work, because that’s how AI has to work. You can’t say, “All right, every few days build in a failure point and then figure out how to fix it and learn from that and grow from that and become a stronger automated version of Chris from that.” That’s just not how those systems work. That’s how the human works, and we have to learn from those things. You’re missing that whole layer of the human experience, and that’s the authenticity.

Christopher S. Penn: Probably for another time, but what you just described does exist now. It is a very high technical bar to implement, but it does exist and people are using it. And believe me, they’re not using it for social media posting.

Katie Robbert: But when I think about that technology existing, to your point, you said there’s a high technical bar. I’m speaking for the everyday person. Our expectation is we’re not going to open ChatGPT and say, “Do this task, but fail five times and then on the sixth time, get it right.”

Christopher S. Penn: Yeah, that’s correct. These things are highly experimental and maybe that’s again a topic for another time about where the technology is going because some very interesting, kind of strange things are going on. So getting back to the idea of authenticity versus AI, when the 8,900th person asks me this question, there’s a couple different answers. One, if you want to automate something and have it be authentic, create a robot account. Create an account that says, “Hi, I’m an AI robot.” So that people are very clear that’s an AI robot answering. And there’s never a doubt in anyone’s mind that it’s masquerading as human.

Because what we ultimately want to do is disclose this is a machine, so that you have a choice as the user if you want to take into account what the machine is having to say. And the second thing is using it as a companion, if you install Chrome’s new Web MCP or the variety of other new tools that have arrived in the automation ecosystem. So that you can say, “Here’s the comment I’m thinking about leaving on Katie’s new post on LinkedIn. What did I miss? Or what would make this comment stronger? Or what would provoke a more interesting discussion?” And using the tool not as the one doing the work, but as the second set of eyes as you’re interacting online to make you a smarter human.

Katie Robbert: I know we’re using it as an example, but my first thought is, why do you need AI to do that in the first place? Why can’t you, the human, just read the article and leave your comment? And I guess that’s a whole other topic of, and we’ve talked about it in various contexts, but just because you can use AI doesn’t mean you should. And this is one of those instances where I’m just sort of baffled of why would you need AI to do this particular task? It should be—I’m not saying it is, but it should be strictly human. And your opinion.

Christopher S. Penn: Ben Affleck has the answer for you.

Katie Robbert: Oh boy.

Christopher S. Penn: In a recent conversation—I think it was actually an interview with Matt Damon—it was about their new movie on Netflix. And one of the things that they said in filmmaking that has gotten very challenging for writers and directors to deal with is the directive from, in this case, Netflix, from the studio that said you must have a character actively restate the plot of the movie up to that point because people are not paying attention. They don’t watch, they don’t listen, they don’t read. And so you have to have a character literally say out loud, “Hey, here’s what’s happened so far.” So that when someone pulls their attention away from their phone for two minutes to tune into the movie, they know what’s going on.

Like you published your article this morning on LinkedIn. It is a lengthy article. It is not a short, quippy piece. And the reality is people do not read in depth and retain in the same way that they used to. And this is not an AI thing. There was a very interesting study that came out a year and a half ago saying that short-form video, TikToks and Reels and stuff like that, causes bizarre rearrangement in the brain to the point where it materially damages memory. There’s another paper that came out last week. There was a first randomized controlled trial of ChatGPT in education that said it causes substantial cognitive deskilling. So to your question, why wouldn’t a human just read it and comment as a human? A fair number of people appear to be losing the—

Katie Robbert: skill to do that, which is mind-boggling. But I guess that’s not for me to comment on or pass judgment on. But I feel like you’re describing two different things. One is, “Hey AI, summarize this longer article for me.” That’s one use case. The other use case is, “Hey AI, draft a response for me.” Summarizing that article, I think, is a fine use case for AI. But, “Hey AI, I didn’t read the article. Draft a response for me.” Don’t do that. Read the article. Even if you have to use that summarization, that’s fine. But don’t let AI speak for you.

Christopher S. Penn: And yet.

Katie Robbert: I know. I’ve often been called an idealist, and I get why people say that about me. But it is baffling to me. Maybe I’m in a unique position—I don’t think I am—to be saying that. But I don’t see how you can have AI do it for you and keep it authentic. I don’t think there’s enough from my point of view, and I could be wrong. I’m sure you’re going to tell me that I’m wrong. But from my point of view, there isn’t enough information that you could give one of these systems about yourself to ever have it truly be an authentic version of yourself.

Because you’d have to upload things like your childhood memories, your patterns of thinking, which is something, Chris, we were talking about the other day, which is a whole other fascinating topic that we should dig into another time. First of all, you have to have self-awareness to be able to speak to those things in a coherent, credible way. And second, you have to have enough of that information. And I feel like all you would be doing is maintaining that machine as you live your life as a human and saying, “Okay, today I had this experience. This is how I felt and thought about this thing.” A lot of people don’t know how they feel and think about everything that’s happening to them. That’s why therapy exists. How are you going to put that into a machine?

Christopher S. Penn: And yet people are.

Katie Robbert: I know, but that’s what I mean. You can’t do it in such a way that you’re truly going to have an authentic version.

Christopher S. Penn: Right. So I guess the question there is what is authentic enough? Clearly what most people are running now in terms of the software to do these automated comments is not enough.

Katie Robbert: Right.

Christopher S. Penn: When you get, “Hey Katie, great insights, rocket ship.” However, given the relatively low stakes of leaving random weird comments on places like LinkedIn, what is the bar of authenticity? Because we know obviously there’s the fully authentic experience, there’s the fully robotic, clearly machine-made experience, and then there’s this large gray zone in the middle. Where is that line, I guess, is the question. And then the secondary question is, is there a point where it is acceptable for the machine to reach that line? And it be a useful contribution to the conversation and discussion. As our friend Brook Sells likes to say, think conversation.

Katie Robbert: Well, here’s the thing. It’s going to look different for everybody. Believe it or not, there are people who respond in that manner that sounds like AI because it’s what they’ve learned. It’s what they know. It’s a comfort zone for them. My recommendation is, if you are considering automating some of these things, is to do a little bit of AB testing outside of actually going live. So, for example, Chris, when some of the video tools and some of the graphics AI systems were coming about, you were experimenting with avatars of you speaking, and I immediately clocked it as, “Well, that’s not Chris Penn,” because I know you well enough.

And so it’s a good AB test to give two pieces of content, short-form, long-form, whatever, to someone who knows you well and say, “Can you tell which of these I wrote and which of these the machine wrote?” And if they can’t tell, then you’ve gotten to a point of authenticity that is passable enough for you to put it on social media. But if it’s immediately, “Oh, yeah, that one’s AI,” then you’re not there yet. And I think that it’s going to look different for everybody. But it’s a good exercise to see, number one, where is that line for you? And number two, do you know yourself well enough to be able to program the machines in a way to say, “This is what I sound like. This isn’t what I sound like.”

Christopher S. Penn: Yeah. Which is, if you want to do it well, is an extensive process, of course, not something you do in one paragraph.

Katie Robbert: And I think that again, you sort of pick and choose those guardrails to say, “And this is where I will let AI speak for me. And this is not where I will let AI speak for me.” You have to make those choices, because the more control you give to the machine, the more risk you’re introducing into your brand, because machines go off the rails, they hallucinate, they say things that you may not have ever said in your entire life. And if you are not supervising them, if you are not QAing them, then how do you walk that back and be like, “Oh, the machine said that, not me.”

Christopher S. Penn: Nobody’s going to believe you. The counterpoint to that—and this is again a topic for another time, but is worth thinking here—is what happens when the machine makes a better you than you are. We both know people who speak entirely in jargon. You can talk to them for 45 minutes. You’re like, “What the hell did that person just say? That was just babble. They were just stringing words together. Playing buzzword bingo.” I could see a case where an AI version of that person would actually be an improvement on that person. Then when you talk to the real person, you’re like, “You’re not the same person. You’re much dumber.”

Katie Robbert: But I feel like that’s—now, to your point, that’s a different conversation. Because if you’re saying authenticity, then the bot version of a person better sound just as confused. It needs to be speaking in riddles and never getting to a point all the time. But yes, there’s probably a better version of me. A more focused, a more coherent, a more straight-to-the-point bot version of me that could be created. And I can see that’s sort of where we’re taking the co-CEO. It’s not to diminish what I bring to the table. And it’s not to say the bot is smarter, but the bot doesn’t have to be distracted by things like, “Oh, the dog needs to go out right now,” or “I’m hungry,” or “I have to take a phone call.”

Those distractions don’t exist in that virtual world. And that already makes that bot version of me superior because they don’t have to have those human experiences that pull away from their core focus. So I would absolutely have that conversation about what a better version entails. And I think that when we say “better,” we need to put that in quotes because that doesn’t always mean that you, the human, are then diminished.

Christopher S. Penn: Yeah, exactly. All right, what are your thoughts on authenticity and AI? Pop by our free Slack. Go to trustinsights.ai/analyticsformarketers, where you and over 4,500 other human beings are having conversations and asking each other’s questions and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if you have a preferred channel, we’re probably there. Go to trustinsights.ai/tipodcast. You can find us in all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights’ services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch, and optimizing content strategies.

Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting. Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama, Trust Insights provides fractional team members, such as CMO or data scientists, to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What livestream, webinars, and keynote speaking.

What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data storytelling.

This commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI. Sharing knowledge widely, whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

00:00 – Introduction 02:45 – The speed‑only trap 05:30 – Introducing the six‑level AI proficiency model 09:10 – Quality vs quantity in AI output 12:40 – Managing AI access and fairness 16:20 – Actionable steps for managers and individuals 20:00 – Call to action

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In Ear Insights, let’s talk about AI and the way the things that we are measuring in business to measure AIs, the productivity, the benefits that you’re getting out of it.

One of my favorite apps, Katie, is called Blind. This is an anonymous confessions app for the business world where people who work at companies—mostly in big business and big tech—share anonymous confessions. They have to say what company they’re with, but that’s it. There were three posts that really caught my eye over the weekend.

The first was from a person who works at Capital One bank who said, “Hi, I’m a junior software engineer.” Three years into my career, my co‑workers are pumping out so many poll requests with Claude code and blitzing through jobs that used to take three to five days in less than an hour. I feel like every day at the office is a race to see who can generate more poll requests and complete them than anyone else.

The second one was from JP Morgan Chase saying, “I just downloaded Claude coat and wtf. I don’t know what to think. Either we are cooked or saved.”

The third was from an engineer at Tesla who said, “I joined recently as a contractor and don’t have access to Claude. I’m slower than the others on my team and it stresses me out.”

So my question to you is this, Katie: Obviously people are using generative AI to move very fast. However, I don’t know if fast is the metric that we should be looking at here, particularly since a lot of people who manage coders don’t necessarily manage them well. They don’t.

For example, very famously, Elon Musk, when he took over Twitter, fired people who didn’t write enough code. He measured people’s productivity solely on lines of code written. Anyone who’s actually written code for a living knows you want less code written rather than more because there’s a certain amount of elegance to writing less code.

So my question to you is, as we talk about AI proficiency—sort of AI proficiency week here at Trust Insights—what would you tell people who are managing people using AI about measuring their proficiency and measuring the results that they’re getting?

Katie Robbert: So first, let me answer your question. No, I do not frequent—was it Blind? Yeah. Anyone who knows me knows that I am honest and direct to a fault. So no, that would annoy me more than anything—just say it to my face.

But that aside, I understand why apps like that exist. Not every company builds a culture where an open‑door policy is actually true. The policy is: the door is open only if you have positive things to share; the door is closed if you have complaints. I sympathize with people who feel the need to turn to those kinds of apps to express concern, frustration, fear. It seems, Chris, that a lot of the fear over the past couple of years is: “Will AI take my job?” In those environments, leadership decisions about process and output are really pushing for AI to take the job.

What I’m not seeing is what the success metrics are. If the metric is faster and more, then you’re missing the third most important one—quality. We don’t know what kind of quality is being produced. Given those short snippets of context, we can assume it’s probably mediocre. It’s probably slightly above the bar, but nothing outstanding—enough to get by, enough to keep the lights on. For some larger companies, that’s fine because you can bury mediocre work in the politics and red tape of an enterprise‑sized organization. No one really expects much more, which is a little sad.

So what I would say to managers is, number one, if you’re not clear on what you’re being measured on, or if your success metric is faster and more, head for the hills—run. That is not good. I mean it in all sincerity; that is not going to serve you in the long run because those metrics are not sustainable.

Christopher S. Penn: And yet that’s what—particularly at a bigger company—where I can definitely, obviously at a company like Trust Insights, we’re four people. Outcomes are something we all measure because we have a direct line to outcomes. If we sell more courses, book more keynote speeches, get more retainer clients, we all have a hand in that and can see very clearly the business outcome.

At a company like JP Morgan Chase, Bank of America, or Capital One, there are hundreds of thousands of employees. Your line of sight to any kind of business outcome is probably five layers of management removed. The front line is way over there—tellers, for example. You write the software that writes the software that manages the system the tellers use. So you don’t have clear outcomes from a business‑level perspective.

Because I used to work at places like AT&T where you are just a cog in the machine, your outcomes very often are either faster or more because no one knows what else to measure.

Katie Robbert: In companies like that, those outcomes are—quote, unquote—good enough because of the nature of what you produce. Consumers have become so dependent on your company that we often talk about the really crappy customer service at cable and Internet providers. There are only so many of them, and they’re all the same. We have become reliant on that technology and have no choice but to put up with crappy service from the big providers.

The same goes for the financial industry. We don’t have a choice other than to rely on these crappy companies because we aren’t equipped to stand up our own financial institutions and change the rules. It’s a big, old industry, and that’s why they operate the way they do. It’s disheartening.

When it comes down to humans, you have to make your own personal choices. Are you okay contributing to the mediocrity of the company and never really advancing? Chris, what you’ve been saying—what is the art of the possible? They don’t know, but they also don’t care. They’re not looking to disrupt the industry. No other companies are starting up to disrupt them because they’re so massive; they’re okay with the status quo, changing at a glacial pace, if at all. It’s not a great story to tell.

You might have a consistent paycheck, but you might not have a lot of passion for the work you do. It might just be clock in at nine, clock out at five, with two 15‑minute breaks and a 30‑minute lunch—and that’s fine for a lot of people. That works for survival. Outside of that work environment is where you find joy, passion, and the things you’re really interested in.

All to say, the advice I would give to managers is: how much are you willing to put up with? Those industries aren’t going to change.

Christopher S. Penn: So in the context of AI proficiency, what do you advise them to focus on? Knowing that, to your point, these places are so calcified, faster is one of the only benchmarks that matter, alongside constantly shrinking budgets. Cheaper is built in because you have to do 5 % less every year.

How do you suggest a manager or employee who feels the fastest typist wins the day and gets the promotion—even if the quality is zero—handle this? The Tesla engineer example is interesting: they don’t have access to generative AI, co‑workers do, they’re much faster, and the contractor fears being fired.

How do we resolve this for team members, knowing that these companies are so calcified that even if a department takes a stand on quality, the other twenty departments competing for budget will say, “Great, you focus on quality; we’ll take your budget because we’ll produce ten times more next year.” Even quality sucks.

Katie Robbert: The Tesla example is an outlier. We don’t have context for why that person doesn’t have access to generative AI—maybe they’re brand new. Contractors don’t get access to paid tools, so that explains it.

When we talk about levels of AI proficiency, generic training doesn’t work; it doesn’t stick. Companies and individuals need to assess their AI proficiency. We typically do this on a six‑point scale, from Basic to Advanced. Within each level are skill sets: Level 1—editing, correcting grammar, asking it to write code. Level 2—writing code and reading code. Level 3—building QA plans. Level 4—providing business or product requirements, agile cues, or building a project plan. It’s like a career path: today I’m a junior analyst, tomorrow I want to be a senior analyst. The same applies to AI proficiency.

My recommendation for managers and individuals stuck in those situations—or anyone looking to level up their AI proficiency—is to look at what’s next, what you don’t know. In the case of Tesla or JP Morgan, they will only produce a limited variety of things. In banking, look at the use cases and how you’re using AI. If you’re building code, how do you automate while keeping a human in the loop? Human‑in‑the‑loop means literal human intervention; you’re not just setting it and forgetting it like a rotisserie chicken. You must ensure a human is paying attention.

Perhaps your KPIs aren’t quality of output, but if you start delivering incorrect work, customers complain, and the company loses money, the quality of your output will suddenly matter. It doesn’t matter how fast you’re creating it.

For the Tesla contractor who lacks internal AI tools, they can get access to their own tools and build their skill set: acknowledge they’re not as fast as full‑time employees, determine what they need to do to match or outpace them, and work on it in their own time if they care. In that instance, the person is worried about job security, so it’s probably in their best interest to act.

Christopher S. Penn: I like how you analogize the six levels to basically the three levels of management. The first two levels are individual contributors; the next two are middle management; the final two are leadership—going from typing the thing to delegating it entirely to someone else. That’s a great analogy. I think after this episode I’m going to revise that chart to help people wrap their brains around it.

What does the level of AI performance efficiency mean? It means you go from individual contributor to leader, eventually leading machines—not necessarily humans.

The Tesla example worries me because the company is essentially asking contractors to bring their own AI tools—a data‑privacy and security nightmare. Still, when I think about our clients who engage us for AI readiness assessments, we see a hierarchy of people with different proficiency levels outpacing each other. Is it fair to say that people with more proficiency—or who invest more in themselves—will blow past peers who are not? Do those peers need to worry about career viability when a peer becomes a mythical 10× engineer or marketer?

Katie Robbert: The short answer is yes, but that’s true in any career path. Unless you’re in a company that promotes someone based on appearance rather than ability, which is another conversation, it’s absolutely true. Levels of AI proficiency run in parallel with organizational maturity. AI proficiency can’t stand alone without a certain amount of maturity within the organization.

We often talk about foundations—the five Ps: documented processes, platforms, good governance, and privacy. Those have to exist for someone to be set up for success and move through AI proficiency levels. Otherwise, they’re becoming proficient against creative garbage. That won’t translate to better career opportunities because, boiled down, it’s garbage in, garbage out—you become proficient at moving garbage around, and nobody wants to hire that.

Christopher S. Penn: An essay from last year discussed the AI reckoning in larger companies. It said AI is doing what decades of management consulting couldn’t—showcasing as you apply AI to processes. Entire levels of management are unnecessary, doing nothing but holding meetings and sending emails. The essay posited that mid‑level managers may realize they only push paper from point A to point B. In those cases, what should people in those positions think about for their own AI proficiency, knowing that improving it will reveal that they add little value?

Katie Robbert: As someone who’s spent most of her career managing, I’ve often had to defend my role. Once, an agency considered dissolving my position because they thought I didn’t bring anything to the table—obviously not true. The team that grew from three people to a $3 million profit center also knows that. Managers need to think about delegation: not just handing off tasks, but ensuring the right people are in the right seats. Coaching is a big part of the job—bringing people up through their proficiency levels.

If I’m a middle manager using the individual‑contributor, manager, leadership matrix, how do I get out of that vulnerable middle spot? Maybe I need to create more workflows, find efficiencies, save the budget, identify level‑one champions, and build them up. Those are the things someone in that middle vulnerable section should consider, because they are vulnerable. Many companies have managers who don’t do squat. I’ve worked alongside those managers; it’s maddening.

One thing that will evolve with the manager role is that you can no longer be just a manager. You can’t just manage things; you have to bring some level of individual contribution and thought leadership to the role. It’s no longer enough to just manage—if that makes sense.

Christopher S. Penn: It makes sense. Over the weekend I was working on something for myself: as technology evolves and I delegate more to it, the guardrails for quality have to get stricter. I revised the rules I use with my Python coding agents—new, enhanced, advanced rules with more guidelines and descriptions about what the agent is and is not allowed to do. This morning my kickoff process broke, so I told the agent to fix it according to the new rules. I realized the previous application sucked, and I fixed it. Now it’s much happier.

I think building quality guardrails will differentiate managers who take on AI management—not just people management. Yes, AI can be faster, but there’s no guarantee it’s better. If I’m a manager who gets faster and better results than peers who just hope it works, I keep my job. What do you think about that angle?

Katie Robbert: It makes sense. Take the middle‑manager example: the VP says, “Client needs these five things.” The hierarchy follows—manager, then individual contributors. The middle person can step up, create a process, develop a proof‑of‑concept example based on the VP’s input, delegate with quality assurance, and cut down iterations. That saves time, saves budget, gets results faster, and reduces frustration because expectations are clear.

Christopher S. Penn: The axiom we talk about when discussing AI optimization is bigger, better, faster, cheaper. Faster obviously saves time and money. We don’t often talk about bigger and better—doing things that add value that wasn’t there before. The value you create should be higher quality.

To wrap up AI proficiency, we have three divisions, six levels, and a focus: if you’re worried about someone else being faster, be as fast and be better quality. Cutting corners for speed will catch up to you.

If you have thoughts about how people are using—or misusing—AI in terms of proficiency, pop by our free Slack group at trustinsights.ai/analysts‑for‑marketers, where over 4,500 marketers ask and answer each other’s questions daily. You can also watch or listen to the show on any podcast platform or the Trust Insights AI TI Podcast. Thanks for tuning in. We’ll talk to you on the next one.

Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data‑driven approach.

Trust Insight specializes in helping businesses leverage data, AI, and machine learning to drive measurable marketing ROI. Services span from comprehensive data strategies and deep‑dive marketing analysis to building predictive models with tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology, MarTech selection and implementation, and high‑level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic, Claude, DALL‑E, Midjourney, Stable Diffusion, and Metalama. The firm provides fractional team members such as a CMO or data scientists to augment existing teams.

Beyond client work, Trust Insights contributes to the marketing community through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, livestream webinars, and keynote speaking. What distinguishes Trust Insights is a focus on delivering actionable insights—not just raw data. The firm leverages cutting‑edge generative AI techniques like large language models and diffusion models while explaining complex concepts clearly through compelling narratives and visualizations. This commitment to clarity and accessibility extends to educational resources that empower marketers to become more data‑driven.

Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a midsize business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever‑evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

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Christopher S. Penn: In this week’s In Ear Insights, it is the AI Wars. Katie, you had some thoughts and some observations about the most recent things going on with Anthropic, with OpenAI, with Google XAI and stuff like that. So at the table, what’s going on?

Katie Robbert: I don’t want to get too deep into the weeds about why people are jumping ship on OpenAI and moving toward the cloud. That’s in the news, it’s political, you can catch up on that. The short version is that decisions from the top at each of these companies have been made that people either agree with or don’t based on their own values and the values of their companies. When publicly traded companies make unpopular decisions that don’t align with the majority of their user base, people jump ship. They were like, okay, I don’t want to use you.

We’ve seen it with Target and many other companies that made decisions people didn’t feel aligned with their personal values. Now we are seeing people abandoning OpenAI and signing on to Anthropic’s Claude. That’s what I wanted to chat about today because we talk a lot about business continuity and risk management. What happens when you get too closely tied to one piece of software and something goes wrong? We’ve talked about this on past episodes in theory because, up until now, software outages have generally been temporary. You don’t often see a mass exodus of a very popular piece of software that people have built their entire businesses around.

Before we get into what this means for the end user and possible solutions, Chris, I would like to get your thoughts, maybe your cat’s thoughts on what’s going on.

Christopher S. Penn: One of the things we’ve said from very early on in the AI space, because it changes so rapidly, is that brand loyalty to any vendor is generally a bad idea. If you were a hater of Google Bard—for good reason—Bard was a terrible model. If you said, I’m never going to touch another Google product again, you would have missed]]> https://www.trustinsights.ai/wp-content/uploads/2026/03/In-Ear-Insights-Switching-AI-Providers-Backup-AI-Capabilities.png In-Ear Insights: Switching AI Providers, Backup AI Capabilities false no 0:00 No no https://www.trustinsights.ai/blog/2026/02/in-ear-insights-how-to-turn-plans-into-results/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-turn-plans-into-results Wed, 25 Feb 2026 11:30:32 +0000 44f83a3c-a59d-51cf-bcc4-84c010f24a5b In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss why most Q1 plans stall and how hidden fear holds teams back. You’ll learn simple ways to turn a big roadmap into tiny actions you can start. You’ll discover how generative AI can suggest low‑risk steps that keep momentum without a big budget. You’ll explore how to break the blame cycle and build real progress even in risk‑averse companies. Watch the episode to start moving your plan forward.

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Christopher S. Penn: In this week’s In-Ear-Insights—welcome from Snowmageddon. For folks listening later, it is the week of the big blizzard in the Northeast U.S., so we are all shoveling, but we’re not talking about shoveling today. Well, we kind of are. We are talking about planning and execution. Mike Tyson famously said no plan survives getting punched in the mouth. And Katie, you recently asked in the Analytics for Marketer Slack group—join at Trust-Insights, AI analytics for marketers—how Q1 planning was going, and everyone said it isn’t. You had thoughts about where that gap is between doing the plan and executing it. The character Leonard from *Legends-Tomorrow* has been quoted: “Make the plan, execute the plan, watch the play go off the rails, throw away the plan,” because that’s how things go. So talk to me about why planning and reality don’t match up so often.

Katie Robbert: I started this question tongue‑in‑cheek: “How are all those fancy Q1 roadmap PowerPoints you spent weeks on in meetings doing?” I didn’t expect the response—most are still sitting in SharePoint or largely untouched. The bottom line is that no one’s really done anything. That’s a trend across any industry, any vertical, any department, because making the plan is the easy part. Executing the plan feels risky, unsafe, unknown.

I saw a post last week from our friend Paul Rotzer at Smarter-X, where he outlined eight stages companies go through when evaluating and adopting AI; most are stuck at one or two. My comment was that this is because of an unacknowledged fear from leadership—fear that by doing something they become irrelevant or that they’ll get it wrong and be exposed. When we ask why we do all this planning and nothing happens, it comes down to unacknowledged fear.

My hypothesis: I can get the best running shoes, put together a sophisticated training plan for a couch‑to‑5K, tighten my nutrition, get plenty of rest—yet that’s just a plan. I still have to do it, to put one foot in front of the other. The scary part is, what if I fail? What if the plan doesn’t work? What if I hurt myself, look silly, embarrass myself? Those thoughts creep up.

In a larger, publicly traded organization with many eyes on]]> full https://www.trustinsights.ai/wp-content/uploads/2026/02/In-Ear-Insights-How-to-Turn-Plans-into-Results.png In-Ear Insights: How to Turn Plans into Results false no 0:00 No no https://www.trustinsights.ai/blog/2026/02/in-ear-insights-cognitive-offloading-deskilling-and-the-impact-of-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-cognitive-offloading-deskilling-and-the-impact-of-ai Wed, 18 Feb 2026 10:40:40 +0000 8875ddab-b4f2-5ead-a7b3-6bab3a35c22f In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how AI can take over routine tasks and what that means for your daily workflow. You’ll learn why relying too much on AI might erode essential skills and how to spot the warning signs. You’ll explore practical frameworks—like the four R’s and the TRIPS model—that keep you in control of AI projects. You’ll see real examples of virtual focus groups and how human review can prevent costly mistakes. Watch the episode now to protect your expertise while leveraging AI power.

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Christopher S. Penn: In this week’s In Ear Insights. This week, let’s talk about something that has been on Katie’s mind— the differences between cognitive offloading and cognitive enhancing with AI becoming as capable as it is with today’s latest agentic frameworks that can literally just pick up a task and run with it. We talked about it last week on the podcast and live stream, which you can find on the Trust Insights YouTube channel. Go to Trust Insights AI YouTube. These tools are incredibly powerful.

You can literally say, “Here’s the project plan,” and just come back to me in 45 minutes.

Katie Robbert: Your concerns are, if the machine is just going to go off and do a great job with these tasks, what’s left for us and what does that mean for our own cognitive capabilities and how we might deskill.

And I want to highlight what you said—that these things are going to do a quote‑unquote great job. That’s a big caveat.

Over the past couple of weeks, especially with Claude from Anthropic, they have launched a lot of functionality into their system. You can use the web version to set up projects and artifacts and have the chat, or you can use the desktop version, now available for Windows and Mac. It was only available for Mac at first; now it’s also available for Windows, so it’s all inclusive. Everybody gets in on the fun, and you have chat, cowork, and code.

One early warning sign I’m seeing is that Claude now has plugins baked into its desktop version. These plugins cover areas like marketing, legal, and executive, and you can even make your own plugins. We made our 5Ps plugin. You can also take the skills you have built on the web version and bring them into the desktop version.

You can have a co‑CEO, a voice of customer, a fact‑checker— the one that Chris really likes—and all of these things. Chris, you did this last week as an experiment: a virtual focus group with many different players from our voice of customer. Our ideal customer prof]]> https://www.trustinsights.ai/wp-content/uploads/2026/02/In-Ear-Insights-Cognitive-Offloading-Deskilling-and-The-Impact-of-AI.png In-Ear Insights: Cognitive Offloading, Deskilling, and The Impact of AI false no 0:00 No no https://www.trustinsights.ai/blog/2026/02/in-ear-insights-project-management-for-ai-agents/?pk_campaign=feed&pk_kwd=in-ear-insights-project-management-for-ai-agents Wed, 11 Feb 2026 10:19:02 +0000 e278fb90-e86b-57f4-b24f-c96baefe2260 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss managing AI agent teams with Project Management 101. You will learn how to translate scope, timeline, and budget into the world of autonomous AI agents. You will discover how the 5P framework helps you craft prompts that keep agents focused and cost‑effective. You will see how to balance human oversight with agent autonomy to prevent token overrun and project drift. You will gain practical steps for building a lean team of virtual specialists without over‑engineering. Watch the episode to see these strategies in action and start managing AI teams like a pro.

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Christopher S. Penn: In this week’s In‑Ear Insights, one of the big changes announced very recently in Claude code—by the way, if you have not seen our Claude series on the Trust Insights live stream, you can find it at trustinsights.

Christopher S. Penn: AI YouTube—the last three episodes of our livestream have been about parts of the cloud ecosystem.

Christopher S. Penn: They made a big change—what was it?

Christopher S. Penn: Thursday, February 5, along with a new Opus model, which is fine.

Christopher S. Penn: This thing called agent teams.

Christopher S. Penn: And what agent teams do is, with a plain‑language prompt, you essentially commission a team of virtual employees that go off, do things, act autonomously, communicate with each other, and then come back with a finished work product.

Christopher S. Penn: Which means that AI is now—I’m going to call it agent teams generally—because it will not be long before Google, OpenAI and everyone else say, “We need to do that in our product or we’ll fall behind.”

Christopher S. Penn: But this changes our skills—from person prompting to, “I have to start thinking like a manager, like a project manager,” if I want this agent team to succeed and not spin its wheels or burn up all of my token credits.

Christopher S. Penn: So Katie, because you are a far better manager in general—and a project manager in particular—I figured today we would talk about what Project Management 101 looks like through the lens of someone managing a team of AI agents.

Christopher S. Penn: So some things—whether I need to check in with my teammates—are off the table.

Christopher S. Penn: Right.

Christopher S. Penn: We don’t have to worry about someone having a five‑hour breakdown in the conference room about the use of an Oxford comma.

Katie Robbert: Thank goodness.

Christopher S. Penn: But some other things—good communication, clarity, good planning—are more important than ever.

Christopher S. Penn: So if you were told,]]> full https://www.trustinsights.ai/wp-content/uploads/2026/02/In-Ear-Insights-Project-Management-for-AI-Agents.png In-Ear Insights: Project Management for AI Agents false no 0:00 No no https://www.trustinsights.ai/blog/2026/02/in-ear-insights-openclaw-and-preparing-for-an-agentic-ai-future/?pk_campaign=feed&pk_kwd=in-ear-insights-openclaw-and-preparing-for-an-agentic-ai-future Wed, 04 Feb 2026 09:47:20 +0000 ef0d2813-ef6d-585f-8c5e-140430a15438 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss autonomous AI agents and the mindset shift required for total automation.

You’ll learn the risks of experimental autonomous systems and how to protect your data. You’ll discover ways to connect AI to your calendar and task managers for better scheduling. You’ll build a mindset that turns repetitive tasks into permanent automated systems. You’ll prepare your current workflows for the next generation of digital personal assistants.

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Christopher S. Penn [00:00]: In this week’s In Ear Insights, let’s talk about autonomous AI. The talk of the town for the last week or so has been the open source project first named Claudebot, spelled C L A W D. Anthropic’s lawyers paid them a visit and said please don’t do that. So they changed it to Maltbot and then no one could remember that. And so they have changed it finally now to Open Claw. Their mascot is still a lobster. This is in a condensed version, a fully autonomous AI system that you install on a.

Christopher S. Penn [00:35]: Please, if you’re thinking about on a completely self contained computer that is not on your main production network because it is made of security vulnerabilities, but it interfaces with a bunch of tools and hasn’t connected to the AI model of your choice to allow you to basically text via WhatsApp or Telegram with an agent and have it go off and do things. And the the pitch is a couple things. One, it has a lot of autonomy so it can just go off and do things. There were some disasters when it first came out where somebody let it loose on their production work computer and immediately started buying courses for them. We did not see a bump in the Trust Insights courses, so that’s unfortunate. But the idea being it’s supposed to function like a true personal assistant.

Christopher S. Penn [01:33]: You just text it and say hey, make me an appointment with Katie for lunch today at noon PM at this restaurant and it will go off and figure out how to do those things and then go off and do them. And for the most part it is very successful. The latest thing is people have been just setting it loose. They a bunch of folks created some plugins for it that allow it to have its own social network called Mult Book, where which is a sort of a Reddit clone where hundreds of thousands of people’s open Claw systems are having conversations with each other that look a lot like Reddit and some very amusing writing there.

Christopher S. Penn [02:12]: Before I go any further Katie, yo]]> https://www.trustinsights.ai/wp-content/uploads/2026/02/In-Ear-Insights-OpenClaw-and-Preparing-for-an-Agentic-AI-Future.png In-Ear Insights: OpenClaw and Preparing for an Agentic AI Future false no 0:00 No no https://www.trustinsights.ai/blog/2026/01/in-ear-insights-durable-skills-in-the-agentic-ai-world/?pk_campaign=feed&pk_kwd=in-ear-insights-durable-skills-in-the-agentic-ai-world Wed, 28 Jan 2026 10:03:48 +0000 6c6f67a5-26e3-5f3b-a0c0-38e232fab240 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the critical staffing decisions leaders must make in the age of autonomous AI.

You will learn the four key options organizational leaders must consider when AI begins automating existing roles. You will identify which essential durable skills guarantee success for employees working alongside powerful new technologies. You will discover how to adjust your hiring strategy to find motivated, curious employees who excel in an AI-augmented environment. You will gain actionable management strategies for handling employees who need encouragement after repetitive tasks become automated. Tune in now to understand how AI changes the modern workforce and secure your company’s future talent.

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Christopher S. Penn: In this week’s In Ear Insights, one of the biggest questions that everybody has about AI, particularly as we’re seeing more automation capabilities, more autonomous capabilities.

Last week we took a look at Claude Code, both on the Trust Insights podcast and on the live stream.

Katie, you and I did some pretty cool stuff with it outside of that for our own company.

Here’s the big question everybody wants an answer to—at least people who are in charge. And I want to hear your answer to this because I have an answer that’s a terrible answer. The answer is this.

With the capabilities of AI today, and as they’re growing and becoming more autonomous, do I as a leader—do I hire, retrain, or outsource, or figure out the fourth category? Replace with AI? Hire, retrain, outsource, replace with AI. So, Katie, when you think about the people management at any company with that big 800-pound gorilla in the room called AI, how do you think about this?

Katie Robbert: To borrow a phrase from Christopher S. Penn, it depends.

And you knew I was going to say that. It really depends on what the responsibility is.

So for those of us in the service industry—consulting—we have clients, customers. There’s still an expectation of human-to-human contact and relationship management, client services, really.

So that I feel like unless that expectation goes away, which there’s a reason you’re in that industry in the first place, that I don’t see being able to replace.

But then when you go behind the scenes, there’s a lot of tasks that can be automated, and that’s what you and I were working on at the end of last week.

And so that to your question of, well, if the person is only just talking to the clients, why do I need someone full time?

It really, again, it really]]> full https://www.trustinsights.ai/wp-content/uploads/2026/01/In-Ear-Insights-Durable-Skills-in-the-Agentic-AI-World.png In-Ear Insights: Durable Skills in the Agentic AI World false no 0:00 No no https://www.trustinsights.ai/blog/2026/01/in-ear-insights-applications-of-agentic-ai-with-claude-cowork/?pk_campaign=feed&pk_kwd=in-ear-insights-applications-of-agentic-ai-with-claude-cowork Wed, 21 Jan 2026 11:30:59 +0000 108e44f7-dfdc-585b-ba91-88152ca6ae64 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the practical application of AI agents to automate mundane marketing tasks.

You will define what an AI agent is and discover how this technology performs complex, multi-step marketing operations. You will learn a simple process for creating knowledge blocks and structured recipes that guide your agents to perform repetitive work. You will identify which tools, like your content scheduler or website platform, are necessary for successful, end-to-end automation. You will understand crucial data privacy measures and essential guardrails to protect your sensitive company information when deploying new automated systems. Tune in now to see how you can permanently eliminate hours of boring work from your weekly schedule!

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Christopher S. Penn: In this week’s In Ear Insights, one of the things that people have said, me especially, is that 2026 is the year of the agent. The way I define an agent is it’s like a real estate agent or a travel agent or a tax agent. It’s something that just goes and does, then comes back to you and says, “Hey, boss, I’m done.”

Katie, you and I were talking before the show about there’s a bunch of mundane tasks, like, let’s write some evergreen social posts, let’s get some images together, let’s update a landing page. Let me ask you this: when you look at those tasks, do they feel repetitive to you?

Katie Robbert: Oh, 100%.

I’ve automated a little bit of it. And by that, what I mean is I have the background information about Trust Insights. I have the tone and brand guidelines for Trust Insights. So if I didn’t have those things, those would probably be the biggest lift.

And so all I’m doing is taking all of the known information and saying, okay, let’s create some content—social posts, landing pages—out of all of the requirements that I’ve already gathered, and I’m just reusing over and over again. So it’s completely repetitive.

I just don’t have that more automated repeatability where I can just push a button and say, “Go.” I still have to do the work of loading everything up into a single system, going through it piece by piece. What do I want? Am I looking at the newsletter? Am I looking at the live stream? Am I looking at this podcast? So there’s still a lot of manual that I know could be automated, and quite frankly, it’s not the best use of my time. But it’s got to get done.

Christopher S. Penn: And so my question to you is, what ]]> https://www.trustinsights.ai/wp-content/uploads/2026/01/In-Ear-Insights-APplications-of-Agentic-AI-with-Claude-Cowork.png In-Ear Insights: Applications of Agentic AI with Claude Cowork false no 0:00 No no https://www.trustinsights.ai/blog/2026/01/in-ear-insights-processing-survey-data-with-generative-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-processing-survey-data-with-generative-ai Wed, 14 Jan 2026 11:45:09 +0000 4fb9249d-aa25-5b96-b634-9066d40c0af4 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss analyzing survey data using generative artificial intelligence tools.

You will discover how to use new AI functions embedded in spreadsheets to code hundreds of open-ended survey responses instantly. You’ll learn the exact prompts needed to perform complex topic clustering and sentiment analysis without writing any custom software. You will understand why establishing a calibrated, known good dataset is essential before trusting any automated qualitative data analysis. You’ll find out the overwhelming trend in digital marketing content that will shape future strategies for growing your business. Watch now to revolutionize how you transform raw feedback into powerful strategy!

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Christopher S. Penn: In this week’s In Ear Insights, let’s talk about surveys and processing survey data. Now, this is something that we’ve talked about. Gosh, I think since the founding of the company, we’ve been doing surveys of some kind.

And Katie, you and I have been running surveys of some form since we started working together 11 years ago because something that the old PR agency used to do a ton of—not necessarily well, but they used to do it well.

Katie Robbert: When they asked us to participate, it would go well.

Christopher S. Penn: Yes, exactly.

Christopher S. Penn: And this week we’re talking about how do you approach survey analysis in the age of generative AI where it is everywhere now. And so this morning you discovered something completely new and different.

Katie Robbert: Well, I mean, I discovered it via you, so credit where credit is due. But for those who don’t know, we have been a little delinquent in getting it out. But we typically run a one-question survey every quarter that just, it helps us get a good understanding of where our audience is, where people’s heads are at.

Because the worst thing you can possibly do as business owners, as marketers, as professionals, is make assumptions about what people want. And that’s something that Chris and I work very hard to make sure we’re not doing.

And so one of the best ways to do that is just to ask people. We’re a small company, so we don’t have the resources unfortunately to hold a lot of one-on-one meetings. But what we can do is ask questions virtually. And that’s what we did. So we put out a one-question survey.

And in the survey, the question was around if you could pick a topic to deep dive on in 2026 to learn about, what would it be. Now keep in mind, I didn’t say ab]]> full https://www.trustinsights.ai/wp-content/uploads/2026/01/In-Ear-Insights-Processing-Survey-Data-With-Generative-AI.png In-Ear Insights: Processing Survey Data With Generative AI false no 0:00 No no https://www.trustinsights.ai/blog/2026/01/in-ear-insights-what-is-generative-engine-marketing-gem/?pk_campaign=feed&pk_kwd=in-ear-insights-what-is-generative-engine-marketing-gem Wed, 07 Jan 2026 11:30:13 +0000 0bf43e53-bae8-5c62-8e5e-21a11b4aed3a In this week’s In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative engine marketing, or GEM, the AI equivalent of SEM. Just as SEO became GEO, so too is SEM likely to become GEM. Learn what it is, how it might manifest, and what you should be considering.

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Christopher S. Penn: In this week’s In-Ear Insights. Welcome back. Happy new year. It’s 2026.

I have just begun to realize as I was cleaning out my pantry over the holidays, oh yeah, all these things expire in 2026. That’s this year.

A lot happened over the holidays. A lot of changes in AI. But one thing that hasn’t happened yet but has been in discussion that I think is—Katie, you wanted to talk about—was SEO for good or ill, sort of centered on this GEO acronym, Generative Engine Optimization, and all of its brethren: AIO and AEO and whatever.

SEO’s companion has always been SEM, also known as Pay Per Click marketing, and that has its alphabet soup like rlsa, remarketing lists for search ads, and all these acronyms, part of the paid version of search marketing.

Well, Katie, you asked a very relevant…

Katie Robbert: …question, which was, when is GEM coming?

So as a little plug, I’m doing a Friday session with our good friends over at Marketing Profs on GEO and ROI, which I have to practice saying over and over again so I don’t stumble over it.

But basically the idea is what can B2B marketers measure in GEO to demonstrate their return on investment so that they can argue for more budget. And so what we were talking about this morning is that GEO is really just an amped up version of brand search.

If you know SEO, brand search is a part of SEO. And so basically it’s like how well recognized is my brand or my influencers or whatever. If I type in Katie Robbert or if I type in Trust Insights, what comes back? And so all of the same tactics that you do for branded search, you do for GEO plus a little bit more. So it’s the same end result, but you need to figure out sort of where all of that fits. So I’ll go over all of that.

But it then naturally progressed into the conversation of, well, part of brand search is paid campaigns. You pay money to Google AdWords, if that’s still what it’s called, or whatever ad system you’re using, you put money behind your branded terms so that when someone’s looking for certain things, your name comes up. And I was like, well, that’s the SEM version of SEO. When are we getting the paid version of GEO? So basically GEM, or whatever you would want to call it, the way that I k]]> full https://www.trustinsights.ai/wp-content/uploads/2026/01/In-Ear-Insights-What-is-Generative-Engine-Marketing-GEM.png In-Ear Insights: What is Generative Engine Marketing (GEM)? false no 0:00 No no https://www.trustinsights.ai/blog/2025/12/in-ear-insights-2025-year-in-review/?pk_campaign=feed&pk_kwd=in-ear-insights-2025-year-in-review Wed, 17 Dec 2025 10:00:47 +0000 b4537c91-61a8-5ee7-b40d-86c4ea5ba5e9 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the massive technological shifts driven by generative AI in 2025 and what you must plan for in 2026.

You will learn which foundational frameworks ensure your organization can strategically adapt to rapid technological change. You’ll discover how to overcome the critical communication barriers and resistance emerging among teams adopting these new tools. You will understand why increasing machine intelligence makes human critical thinking and emotional skills more valuable than ever. You’ll see the unexpected primary use case of large language models and identify the key metrics you must watch in the coming year for economic impact. Watch now to prepare your strategy for navigating the AI revolution sustainably.

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Christopher S. Penn: In this week’s *In-Ear Insights*.

This is the last episode of *In-Ear Insights* for 2025. We are out with the old. We’ll be back in January for new episodes the week of January 5th.

So, Katie, let’s talk about the year that was and all the crazy things that happened in the year. And so what you’re thinking about, particularly from the perspective of all things AI, all things data and analytics—how was 2025 for you?

Katie Robbert: What’s funny about that is I feel like for me personally, not a lot changed.

And the reason I feel like I can say that is because a lot of what I focus on is foundational, and it doesn’t really matter what fancy, shiny new technology is happening. So I really try to focus on making sure the things that I do every day can adapt to new technology.

And again, of course, that’s probably the most concrete example of that is the 5P framework: Purpose, People, Process, Platform for Performance. It doesn’t matter what the technology is. This is where I’m always going to ground myself in this framework so that if AI comes along or shiny object number 2 comes along, I can adapt because it’s still about primarily, what are we doing? So asking the right questions.

The things that did change were I saw more of a need this year, not in general, but just this year, for people to understand how to connect with other people.

And not only in a personal sense, but in a professional sense of my team needs to adopt AI or they need to adopt this new technology. I don’t know how to reach them. I don’t know where to start. I don’t know. I’m telling them things. Nothing’s working.

And I feel like the technology of today, which is generative AI, is creating more barriers to communication than it is opening up communication channels. And so that’s a lot of where my head has bee]]> full https://www.trustinsights.ai/wp-content/uploads/2025/12/In-Ear-Insights-2025-Year-In-Review.png In-Ear Insights: 2025 Year In Review false no 0:00 No no https://www.trustinsights.ai/blog/2025/12/in-ear-insights-what-are-small-language-models/?pk_campaign=feed&pk_kwd=in-ear-insights-what-are-small-language-models Wed, 10 Dec 2025 10:21:08 +0000 f059286b-863e-5fa4-947c-9eccb89cf0f2 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss small language models (SLMs) and how they differ from large language models (LLMs).

You will understand the crucial differences between massive large language models and efficient small language models. You’ll discover how combining SLMs with your internal data delivers superior, faster results than using the biggest AI tools. You will learn strategic methods to deploy these faster, cheaper models for mission-critical tasks in your organization. You will identify key strategies to protect sensitive business information using private models that never touch the internet. Watch now to future-proof your AI strategy and start leveraging the power of small, fast models today!

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https://youtu.be/XOccpWcI7xk

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Christopher S. Penn: In this week’s *In-Ear Insights*, let’s talk about small language models. Katie, you recently came across this and you’re like, okay, we’ve heard this before. What did you hear?

Katie Robbert: As I mentioned on a previous episode, I was sitting on a panel recently and there was a lot of conversation around what generative AI is. The question came up of what do we see for AI in the next 12 months? Which I kind of hate that because it’s so wide open.

But one of the panelists responded that SLMs were going to be the thing. I sat there and I was listening to them explain it and they’re small language models, things that are more privatized, things that you keep locally. I was like, oh, local models, got it. Yeah, that’s already a thing. But I can understand where moving into the next year, there’s probably going to be more of a focus on it.

I think that the term local model and small language model in this context was likely being used interchangeably. I don’t believe that they’re the same thing. I thought local model, something you keep literally locally in your environment, doesn’t touch the internet. We’ve done episodes about that which you can catch on our livestream if you go to TrustInsights.ai YouTube, go to the Soap playlist. We have a whole episode about building your own local model and the benefits of it.

But the term small language model was one that I’ve heard in passing, but I’ve never really dug deep into it. Chris, in as much as you can, in layman’s terms, what is a small language model as opposed to a large language model, other than—

Christopher S. Penn: Is the best description? There is no generally agreed upon definition other than it’s small. All language models are measured in terms of the number of tokens they were trained on and the number of parameters they have. Parameters are basically the number of combinations of tokens that they’v]]> full https://www.trustinsights.ai/wp-content/uploads/2025/12/In-Ear-Insights-What-Are-Small-Language-Models.png In-Ear Insights: What Are Small Language Models? false no 0:00 No no https://www.trustinsights.ai/blog/2025/12/in-ear-insights-ai-and-the-future-of-intellectual-property/?pk_campaign=feed&pk_kwd=in-ear-insights-ai-and-the-future-of-intellectual-property Wed, 03 Dec 2025 10:21:07 +0000 e899d02e-429e-500a-9bbe-fe0ffc36e108 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the present and future of intellectual property in the age of AI.

You will understand why the content AI generates is legally unprotectable, preventing potential business losses. You will discover who is truly liable for copyright infringement when you publish AI-assisted content, shifting your risk management strategy. You will learn precise actions and methods you must implement to protect your valuable frameworks and creations from theft. You will gain crucial insight into performing necessary due diligence steps to avoid costly lawsuits before publishing any AI-derived work. Watch now to safeguard your brand and stay ahead of evolving legal risks!

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Christopher S. Penn: In this week’s In Ear Insights, let’s talk about the present and future of intellectual property in the age of AI.

Now, before we get started with this week’s episode, we have to put up the obligatory disclaimer: we are not lawyers. This is not legal advice. Please consult with a qualified legal expert practitioner for advice specific to your situation in your jurisdiction.

And you will see this banner frequently because though we are knowledgeable about data and AI, we are not lawyers.

We can, if you’d like, join our Slack group at Trust Insights, AI Analytics for Marketers, and we can recommend some people who are lawyers and can provide advice depending on your jurisdiction.

So, Katie, this is a topic that you came across very recently. What’s the gist of it?

Katie Robbert: So the backstory is I was sitting on a panel with an internal team and one of the audience members.

We were talking about generative AI as a whole and what it means for the industry, where we are now, so on, so forth.

And someone asked the question of intellectual property. Specifically, how has intellectual property management changed due to AI?

And I thought that was a great question because I think that first and foremost, intellectual property is something that perhaps isn’t well understood in terms of how it works. And then I think that there’s we were talking about the notion of AI slop, but how do you get there?

Aeo, geo, all your favorite terms. But basically the question is around: if we really break it down, how do I protect the things that I’m creating, but also let people know that it’s available? And that’s.

I know this is going to come as a shocker. New tech doesn’t solve old problems, it just highlights it.

So if you’re not protecting your assets, if you’re not filing for your ]]> full https://www.trustinsights.ai/wp-content/uploads/2025/12/In-Ear-Insights-AI-And-the-Future-of-Intellectual-Property.png In-Ear Insights: AI And the Future of Intellectual Property false no 0:00 No no https://www.trustinsights.ai/blog/2025/11/in-ear-insights-sales-frameworks-basics-and-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-sales-frameworks-basics-and-ai Wed, 12 Nov 2025 10:29:08 +0000 578549bd-6d31-5634-b7fe-405efd68e5b7 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss essential sales frameworks and why they often fail today.

You will understand why traditional sales methods like Challenger and SPIN selling struggle with modern complex purchases. You will learn how to shift your sales focus from rigid, linear frameworks to the actual non-linear journey of the customer. You will discover how to use ideal customer profiles and strong documentation to build crucial trust and qualify better prospects. You will explore methods for leveraging artificial intelligence to objectively evaluate sales opportunities and improve your go/no-go decisions. Watch this episode to revolutionize your approach to high-stakes complex sales.

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**Christopher S. Penn – 00:00** In this week’s In Ear Insights. Even though AI is everywhere and is threatening to eat everything and stuff like that, the reality is that people still largely buy from people. And there are certainly things that AI does that can make that process faster and easier. But today I thought it might be good to review some of the basic selling frameworks, particularly for companies like ours, but in general, to help with complex sales.

One of the things that—and Katie, I’d like your take on this—one of the things that people do most wrong in sales at the very outset is they segment out B2B versus B2C when they really should be segmenting out: simple sale versus complex sales. Simple sales, a pack of gum, there are techniques for increasing number of sales, but it’s a transaction.

**Christopher S. Penn – 00:48** You walk into the store, you put down your money, you walk out with your pack of gum as opposed to a complex sale. Things like B2B SaaS software, some versions of it, or consulting services, or buying a house or a college education where there’s a lot of stakeholders, a lot of negotiation, and things like that. So when you think about selling, particularly as the CEO of Trust Insights who wants to sell more stuff, what do you think about advising people on how to sell better?

**Katie Robbert – 01:19** Well, I should probably start with the disclaimer that I am not a trained salesperson. I happen to be very good with people and reading the situation and helping understand the pain points and needs pretty quickly. So that’s what I’ve always personally relied on in terms of how to sell things. And that’s not something that I can easily teach. So to your point, there needs to be some kind of a framework.

I disagree with your opening statement that the biggest problem people have with selling or the biggest mistake that people make is the segmentat]]> https://www.trustinsights.ai/wp-content/uploads/2025/10/In-Ear-Insights-Sales-Frameworks-Basics-and-AI.png In-Ear Insights: Sales Frameworks Basics and AI false no 0:00 No no https://www.trustinsights.ai/blog/2025/11/in-ear-insights-account-management-in-the-age-of-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-account-management-in-the-age-of-ai Wed, 05 Nov 2025 10:29:07 +0000 5e5a6eb6-0abc-531c-b460-83335cce6e22 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the essentials of excellent account management and how AI changes the game.

You will discover how to transition from simply helping clients to proactively taking tasks off their to-do list. You will learn the exact communication strategies necessary to manage expectations and ensure timely responses that build client trust. You will understand the four essential executive functions you must retain to prevent artificial intelligence from replacing your critical role. You will grasp how to perform essential quality checks on deliverables even without possessing deep technical expertise in the subject matter. Watch now to elevate your account management skills and secure your position in the future of consulting!

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**Christopher S. Penn – 00:00** In this week’s In Ear Insights, Trust Insights is a consulting firm. We obviously do consulting. We have clients, we have accounts, and therefore account management. Katie, you and I worked for a few years together at a PR firm before we started Trust Insights and managed a team of folks. I should clarify with an asterisk: you managed a team of people then to keep those accounts running, keep customers and clients happy, and try to keep team members happy. Let’s talk about what are the basics of good account management—not just for keeping clients happy, but also keeping your team happy as well, to the extent that you can, but keeping stuff on the rails.

**Katie Robbert – 00:51** The biggest thing from my experience, because I’ve been on both sides of it—well, I should say there are three sides of it. There’s the account manager, there’s the person who manages the account manager, and then there’s the account itself, the client. I’ve been on all three sides of it, and I currently sit on the side of managing the account manager who manages the accounts. If we talk about the account manager, that person is trying to keep things on the rails. They’re trying to keep things moving forward. Typically they are the ones who, if they choose, they can have the most power, or if they don’t, they have the least power.

**Katie Robbert – 01:38** By that I mean, a good account manager has their hands in everything, is listening to every conversation between the stakeholders or the principals and the client, is really ingesting the information and understanding, “Okay, this is what was asked for. This is what we’re working on. This is discussed.” Whatever it is they don’t understand, they take the initiative to find out what it means.

If you’re working on ]]> https://www.trustinsights.ai/wp-content/uploads/2025/10/In-Ear-Insights-Account-Management-in-the-Age-of-AI.png In-Ear Insights: Account Management in the Age of AI false no 0:00 No no https://www.trustinsights.ai/blog/2025/10/in-ear-insights-how-to-create-effective-reporting/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-create-effective-reporting Wed, 29 Oct 2025 09:27:02 +0000 aede8d34-cf22-5d33-88fc-6a87ea0d9319 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss effective reporting and creating reports that tell a story and drive action using user stories and frameworks.

You will understand why data dumping onto a stakeholder’s desk fails and how to gather precise reporting requirements immediately. You will discover powerful frameworks, including the SAINT model, that help you move from basic analysis to crucial, actionable decisions. You will gain strategies for anticipating executive questions and delivering a clear, consistent narrative throughout your entire report. You will explore innovative ways to use artificial intelligence as a thought partner to refine your analysis and structure perfect reports. Stop wasting time and start creating reports that generate real business results. Watch now!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, it’s almost redundant at this point to say it’s reporting season, but as we hit quarterly ends, yearly ends, things like that, people become reflective and say, “Hey, let’s do some reports.”

One of the problems that we see the most with reporting—and I was guilty of this for the majority of my career, particularly the first half—is when you’re not confident about your reporting skills, what do you do? You back the truck up and you pour data all over somebody’s desk and you hope that it overwhelms them so that they don’t ask you any questions, which is the worst possible way to do reporting.

So, Katie, as a senior executive, as a leader, when someone delivers reporting to you, what do you get and what do you want to get?

Katie Robbert – 00:51 Well, I would start to say reports, like the ones that you were generating, hate to see me coming. Because guess what I do, Chris, I ask a bazillion questions, starting with so what? And I think that’s really the key.

As the CEO of Trust Insights, I need a report that tells me exactly what the insights and actions are so that I can do those things. And that is a user story. A user story is a simple three-part sentence: As a Persona, I want so that. If someone is giving me a report and they haven’t asked me for a user story, that’s probably step one. So, Chris, if I say, “All right, if you can pull the monthly metrics, Chris, and put it into a report, I would appreciate it.”

Katie Robbert – 01:47 If I haven’t given you a user story, you need to ask me what it is, because that’s the “so what?” Why are we doing this in the first place? We have no shortage of data points. We have no shortage of informat]]> full https://www.trustinsights.ai/wp-content/uploads/2025/10/In-Ear-Insights-How-to-Create-Effective-Reporting.png In-Ear Insights: How to Create Effective Reporting false no 0:00 No no https://www.trustinsights.ai/blog/2025/10/in-ear-insights-generative-ai-for-marketers-at-maicon-2025/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-for-marketers-at-maicon-2025 Wed, 22 Oct 2025 09:27:01 +0000 36c0cc6e-9cb3-50bb-b10b-9fcd04eae0a8 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the stark reality of the future of work presented at the Marketing AI Conference, MAICON 2025.

You’ll learn which roles artificial intelligence will consume fastest and why average employees face the highest risk of replacement. You’ll master the critical thinking and contextual skills you must develop now to transform yourself into an indispensable expert. You’ll understand how expanding your intellectual curiosity outside your specific job will unlock creative problem solving essential for survival. You’ll discover the massive global AI blind spot that US companies ignore and how this shifting landscape affects your career trajectory. Watch now to prepare your career for the age of accelerated automation!

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Christopher S. Penn – 00:00

In this week’s In Ear Insights, we are at the Marketing AI Conference, Macon 2025 in Cleveland with 1,500 of our best friends. This morning, the CEO of SmartRx, formerly the Marketing AI Institute, Paul Ritzer, was talking about the future of work. Now, before I go down a long rabbit hole, Dave, what was your immediate impressions, takeaways from Paul’s talk?

Katie Robbert – 00:23

Paul always brings this really interesting perspective because he’s very much a futurist, much like yourself, but he’s a futurist in a different way. Whereas you’re on the future of the technology, he’s focused on the future of the business and the people. And so his perspective was really, “AI is going to take your job.” If we had to underscore it, that was the bottom line: AI is going to take your job. However, how can you be smarter about it? How can you work with it instead of working against it? Obviously, he didn’t have time to get into every single individual solution.

Katie Robbert – 01:01

The goal of his keynote talk was to get us all thinking, “Oh, so if AI is going to take my job, how do I work with AI versus just continuing to fight against it so that I’m never going to get ahead?” I thought that was a really interesting way to introduce the conference as a whole, where every individual session is going to get into their soldiers.

Christopher S. Penn – 01:24

The chart that really surprised me was one of those, “Oh, he actually said the quiet part out loud.” He showed the SaaS business chart: SaaS software is $500 billion of economic value. Of course, AI companies are going, “Yeah, we want that money. We want to take all that money.” But then he brought ]]> full https://www.trustinsights.ai/wp-content/uploads/2025/10/In-Ear-Insights-GenERATIVE-AI-FOR-MARKETERS-AT-MAICON-2025-LIVE.png In-Ear Insights: Generative AI for Marketers at MAICON 2025 false no 0:00 No no https://www.trustinsights.ai/blog/2025/10/in-ear-insights-how-to-make-conferences-worth-the-investment/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-make-conferences-worth-the-investment Wed, 15 Oct 2025 08:51:34 +0000 7c2477f9-a38f-567d-8b83-7f8f354ff9c5 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the worth of conferences and events in a tight economy.

You will learn a powerful framework for evaluating whether an expensive conference ticket meets your specific professional goals. You will use generative artificial intelligence to score event agendas, showing you which sessions offer the best return on your time investment. You will discover how expert speakers and companies create tangible value, moving beyond vague thought leadership to give you actionable takeaways. You will maximize your event attendance by demanding supplementary tools, ensuring you retain knowledge long after you leave the venue. Watch this episode now to stop wasting budget on irrelevant professional events!

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Christopher S. Penn – 00:00 In this week’s *In Ear Insights*, let’s talk about events, conferences, trade shows, workshops—the gamut of things that you could get up from your desk maybe, go somewhere else, eat hotel chicken, and enjoy speaking. The big question is this, Katie: In today’s absolutely loony environment, with the economic uncertainty and the budgets and all this and that, are events still worth it? This is a two-part question: Are events still worth it for the attendees, and are events still worth it for companies that want to generate business from events?

Katie Robbert – 00:50 It’s a big question. And if our listeners are anything like me, it takes a lot to get them to put on real pants and actually leave the house—something that isn’t sweatpants or leggings or something like that—because you’re spending the time, the resources, the money to go out and actually interact with other people.

In terms of an attendee, I think there can be a lot of value, provided you do your homework on who the speakers are, what their expertise is, what they’re promising to teach you in the workshop or the session or whatever the thing is. The flip side of that is it can be worth it for a speaker, provided you know who your audience is, you can create an ICP, and provided you are giving value to the audience.

Katie Robbert – 01:54 So if you’re a speaker who has made their whole career on big ideas and thought leadership and all that’s fine, people have a hard time buying something from that and saying, “I know exactly what it is I need to do next.”

So there is a time and place for those speakers. But for an attendee to really get value, you need to teach them something. You need to show them how to be very tactical, be very hands-on. That’s where an]]> https://www.trustinsights.ai/wp-content/uploads/2025/10/In-Ear-Insights-How-to-Make-Conferences-Worth-the-Investment.png In-Ear Insights: How to Make Conferences Worth the Investment false no 0:00 No no https://www.trustinsights.ai/blog/2025/10/in-ear-insights-getting-real-value-from-generative-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-getting-real-value-from-generative-ai Wed, 08 Oct 2025 08:33:59 +0000 https://www.trustinsights.ai/?p=92945 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss scaling Generative AI past basic prompting and achieving real business value.

You will learn the strategic framework necessary to move beyond simple, one-off interactions with large language models. You will discover why focusing on your data quality, or “ingredients,” is more critical than finding the ultimate prompt formula. You will understand how connecting AI to your core business systems using agent technology will unlock massive time savings and efficiencies. You will gain insight into defining clear, measurable goals for AI projects using effective user stories and the 5P methodology. Stop treating AI like a chatbot intern and start building automated value—watch now to find out how!

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Christopher S. Penn – 00:00

In this week’s *In-Ear Insights*. Another week, another gazillion posts on LinkedIn and various social networks about the ultimate ChatGPT prompt. OpenAI, of course, published its Prompt Blocks library of hundreds of mediocre prompts that are particularly unhelpful.

And what we’re seeing in the AI industry is this: A lot of people are stuck and focused on how do I prompt ChatGPT to do this, that, or the other thing, when in reality that’s not where the value is.

Today, let’s talk about where the value of generative AI actually is, because a lot of people still seem very stuck on the 101 basics. And there’s nothing wrong with that—that is totally great—but what comes after it?

Christopher S. Penn – 00:47

So, Katie, from your perspective as someone who is not the propeller head in this company and is very representative of the business user who wants real results from this stuff and not just shiny objects, what do you see in the Generative AI space right now? And more important, what do you see it’s missing?

Katie Robbert – 01:14

I see it’s missing any kind of strategy, to be quite honest. The way that people are using generative AI—and this is a broad stroke, it’s a generalization—is still very one-off. Let me go to ChatGPT to summarize these meeting notes. Let me go to Gemini to outline a blog post. There is nothing wrong with that, but it’s not a strategy; it’s one more tool in your stack. And so the big thing that I see missing is, what are we doing with this long term?

Katie Robbert – 01:53

Where does it fit into the overall workflow and how is it actually becoming part of the team? How is it becoming integrated into the organization? So, people who are saying, “Well, we’re sitting down for ]]> https://www.trustinsights.ai/wp-content/uploads/2025/10/In-Ear-Insights-Getting-Real-Value-from-Generative-AI.png In-Ear Insights: Getting Real Value from Generative AI false no 0:00 No no https://www.trustinsights.ai/blog/2025/09/in-ear-insights-do-awards-still-matter-in-marketing-and-pr/?pk_campaign=feed&pk_kwd=in-ear-insights-do-awards-still-matter-in-marketing-and-pr Wed, 24 Sep 2025 09:17:31 +0000 https://www.trustinsights.ai/?p=92901 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss whether awards still matter in today’s marketing landscape, especially with the rise of generative AI.

You will understand how human psychology and mental shortcuts make awards crucial for decision-making. You will discover why awards are more relevant in the age of generative AI, influencing search results and prompt engineering. You will learn how awards can differentiate your company and become a powerful marketing tool. You will explore new ways to leverage AI for award selection and even consider creating your own merit-based recognition. Watch this episode now to redefine your perspective on marketing accolades!

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, the multi-platinum, award-winning, record-setting—you name it. People love to talk about awards, particularly companies. We love to say we are an award-winning this, we’re an award-winning that. Authors say, “I’m a best-selling, award-winning book.” But Katie, you had a very interesting and provocative question: In today’s marketing landscape, do awards still matter?

Katie Robbert – 00:27 And I still have that question. Also, let me back up a little bit. When I made the transition from working in more of an academic field to the public sector, I had a huge revelation—my eyes were open to how awards worked. Call it naive, call it I was sheltered from this side of the industry, but I didn’t know at the time that in order to win an award, you had to submit yourself for the award. I naively thought that you just do good work and you get nominated by someone who recognizes that you’re doing good work. That’s how awards work. Because in my naive brain, you do good work and they reward you for it.

Katie Robbert – 01:16 And so here’s your award for being amazing.

Speaker 3 – 01:18 And that is not at all that.

Katie Robbert – 01:20 That’s not how any of the Emmys or the Grammys—they all…

Speaker 3 – 01:24 Have to submit themselves.

Katie Robbert – 01:25 I didn’t know that they have to choose the scene that they think is award-winning. Yes, it’s voted on by a jury of your peers, which is also perhaps problematic depending on who’s on the jury. There’s the whole—the whole thing just feels like one big scam.

Katie Robbert – 01:46 That said, per usual, I’m an n of 1, and I know that in certain industries, the more awards and accolades you rack up and can put on your website, the more likely it is that people are going to hire you or your firm or buy ]]> full https://www.trustinsights.ai/wp-content/uploads/2025/09/In-Ear-Insights-Do-Awards-Still-Matter-in-Marketing-and-PR.png In-Ear Insights: Do Awards Still Matter in Marketing and PR? false no 0:00 No no https://www.trustinsights.ai/blog/2025/09/in-ear-insights-what-is-ai-decisioning/?pk_campaign=feed&pk_kwd=in-ear-insights-what-is-ai-decisioning Wed, 17 Sep 2025 10:00:38 +0000 https://www.trustinsights.ai/?p=92891 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss AI decisioning, the latest buzzword confusing marketers.

You will learn the true meaning of AI decisioning and the crucial difference between classical AI and generative AI for making sound business choices. You’ll discover when AI is an invaluable asset for decision support and when relying on it fully can lead to costly mistakes. You’ll gain practical strategies, including the 5P framework and key questions, to confidently evaluate AI decisioning software and vendors. You will also consider whether building your own AI solution could be a more effective path for your organization. Watch now to make smarter, data-driven decisions about adopting AI in your business!

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**Christopher S. Penn – 00:00** In this week’s In-Ear Insights, let’s talk about a topic that is both old and new. This is decision optimization or decision planning, or the latest buzzword term AI decisioning. Katie, you are the one who brought this topic to the table. What the heck is this? Is this just more expensive consulting speak? What’s going on here?

**Katie Robbert – 00:23** Well, to set the context, I’m actually doing a panel for the Martech organization on Wednesday, September 17, about how AI decisioning will change our marketing. There are a lot of questions we’ll be going over, but the first question that all of the panelists will be asked is, what is AI decisioning? I’ll be honest, Chris, it was not a term I had heard prior to being asked to do this panel. But, I am the worst at keeping up with trends and buzzwords.

When I did a little bit of research, I just kind of rolled my eyes and I was like, oh, so basically it’s the act of using AI to optimize the way in which decisions are made. Sort of. It’s exactly what it sounds like.

**Katie Robbert – 01:12** But it’s also, I think, to your point, it’s a consultant word to make things sound more expensive than they should because people love to do that. So at a high level, it’s sticking a bunch of automated processes together to help support the act of making business decisions. I’m sure that there are companies that are fully comfortable with taking your data and letting their software take over all of your decisions without human intervention, which I could rant about for a very long time.

When I asked you this question last week, Chris, what is AI decisioning? You gave me a few different definitions. So why don’t you run through your understanding of AI decisioning?

**Christopher S. Penn – 02:07** The big one comes from our friends at IBM. IBM used to have this platform called I]]> full https://www.trustinsights.ai/wp-content/uploads/2025/09/In-Ear-Insights-What-is-AI-Decisioning.png In-Ear Insights: What is AI Decisioning? false no 0:00 No no https://www.trustinsights.ai/blog/2025/09/in-ear-insights-do-websites-matter-in-the-age-of-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-do-websites-matter-in-the-age-of-ai Wed, 03 Sep 2025 09:27:47 +0000 https://www.trustinsights.ai/?p=92850 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss whether blogs and websites still matter in the age of generative AI.

You’ll learn why traditional content and SEO remain essential for your online presence, even with the rise of AI. You’ll discover how to effectively adapt your content strategy so that AI models can easily find and use your information. You’ll understand why focusing on answering your customer’s questions will benefit both human and AI search. You’ll gain practical tips for optimizing your content for “Search Everywhere” to maximize your visibility across all platforms. Tune in now to ensure your content strategy is future-proof!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, one of the biggest questions that people have, and there’s a lot of debate on places like LinkedIn about this, is whether blogs and websites and things even matter in the age of generative AI. There are two different positions on this. The first is saying, no, it doesn’t matter. You just need to be everywhere. You need to be doing podcasts and YouTube and stuff like that, as we are now. The second is the classic, don’t build on rented land. They have a place that you can call your own and things. So I have opinions on this, but Katie, I want to hear your opinions on this.

Katie Robbert – 00:37 I think we are in some ways overestimating people’s reliance on using AI for fact-finding missions. I think that a lot of people are turning to generative AI for, tell me the best agency in Boston or tell me the top five list versus the way that it was working previous to that, which is they would go to a search bar and do that instead. I think we’re overestimating the amount of people who actually do that.

Katie Robbert – 01:06 Given, when we talk to people, a lot of them are still using generative AI for the basics—to write a blog post or something like that. I think personally, I could be mistaken, but I feel pretty confident in my opinion that people are still looking for websites.

Katie Robbert – 01:33 People are still looking for thought leadership in the form of a blog post or a LinkedIn post that’s been repurposed from a blog post. People are still looking for that original content. I feel like it does go hand in hand with AI because if you allow the models to scrape your assets, it will show up in those searches. So I guess I think you still need it. I think people are still going to look at those sources. You also want it to be available for the models to be searching.

Christopher S. Penn – 02:09]]> https://www.trustinsights.ai/wp-content/uploads/2025/09/In-Ear-Insights-Do-Websites-Matter-in-the-Age-of-AI.png In-Ear Insights: Do Websites Matter in the Age of AI? false no 0:00 No no https://www.trustinsights.ai/blog/2025/08/in-ear-insights-why-enterprise-generative-ai-projects-fail/?pk_campaign=feed&pk_kwd=in-ear-insights-why-enterprise-generative-ai-projects-fail Wed, 27 Aug 2025 07:25:46 +0000 https://www.trustinsights.ai/?p=92823 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss why enterprise generative AI projects often fail to reach production.

You’ll learn why a high percentage of enterprise generative AI projects reportedly fail to make it out of pilot, uncovering the real reasons beyond just the technology. You’ll discover how crucial human factors like change management, user experience, and executive sponsorship are for successful AI implementation. You’ll explore the untapped potential of generative AI in back-office operations and process optimization, revealing how to bridge the critical implementation gap. You’ll also gain insights into the changing landscape for consultants and agencies, understanding how a strong AI strategy will secure your competitive advantage. Watch now to transform your approach to AI adoption and drive real business results!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, the big headline everyone’s been talking about in the last week or two about generative AI is a study from MIT’s Nanda project that cited the big headline: 95% of enterprise generative AI projects never make it out of pilot. A lot of the commentary clearly shows that no one has actually read the study because the study is very good. It’s a very good study that walks through what the researchers are looking at and acknowledged the substantial limitations of the study, one of which was that it had a six-month observation period.

Katie, you and I have both worked in enterprise organizations and we have had and do have enterprise clients. Some people can’t even buy a coffee machine in six months, much less route a generative AI project.

Christopher S. Penn – 00:49 But what I wanted to talk about today was some of the study’s findings because they directly relate to AI strategy. So if you are not an AI ready strategist, we do have a course for that.

Katie Robbert – 01:05 We do. As someone, I’ve been deep in the weeds of building this AI ready strategist course, which will be available on September 2. It’s actually up for pre-sale right now. You go to trust insights AI/AI strategy course. I just finished uploading everything this morning so hopefully I used all the correct edits and not the ones with the outtakes of me threatening to murder people if I couldn’t get the video done.

Christopher S. Penn – 01:38 The bonus, actually, the director’s edition.

Katie Robbert – 01:45 Oh yeah, not to get too off track, but there was a couple of times I was going through, I’m ]]> https://www.trustinsights.ai/wp-content/uploads/2025/08/In-Ear-Insights-Why-Enterprise-Generative-AI-Projects-Fail.png In-Ear Insights: Why Enterprise Generative AI Projects Fail false no 0:00 No no https://www.trustinsights.ai/blog/2025/08/in-ear-insights-reviewing-ai-data-privacy-basics/?pk_campaign=feed&pk_kwd=in-ear-insights-reviewing-ai-data-privacy-basics Wed, 20 Aug 2025 08:50:50 +0000 https://www.trustinsights.ai/?p=92806 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss AI data privacy and how AI companies use your data, especially with free versions. You will learn how to approach terms of service agreements. You will understand the real risks to your privacy when inputting sensitive information. You will discover how AI models train on your data and what true data privacy solutions exist. Watch this episode to protect your information!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, let’s address a question and give as close to a definitive answer as we can—one of the most common questions asked during our keynotes, our workshops, in our Slack Group, on LinkedIn, everywhere: how do AI companies use your data, particularly if using the free version of a product? A lot of people say, “Be careful what you put in AI. It can learn from your data. You could be leaking confidential data. What’s going on?” So, Katie, before I launch into a tirade which could take hours long, let me ask you, as someone who is the less technical of the two of us, what do you think happens when AI companies are using your data?

Katie Robbert – 00:43 Well, here’s the bottom line for me: AI is any other piece of software that you have to read the terms in use and sign their agreement for. Great examples are all the different social media platforms. And we’ve talked about this before, I often get a chuckle—probably in a more sinister way than it should be—of people who will copy and paste this post of something along the lines of, “I do not give Facebook permission to use my data. I do not give Facebook permission to use my images.”

And it goes on and on, and it says copy and paste so that Facebook can’t use your information. And bless their hearts, the fact that you’re on the platform means that you have agreed to let them do so.

Katie Robbert – 01:37 If not, then you need to have read the terms, the terms of use that explicitly says, “By signing up for this platform, you agree to let us use your information.” Then it sort of lists out what it’s going to use, how it’s going to use it, because legally they have to do that. When I was a product manager and we were converting our clinical trial outputs into commercial products, we had to spend a lot of time with the legal teams writing up those terms of use: “This is how we’re going to use only marketing data. This is how we’re going to use only your registration form data.” When I hear people getting nervous about, “Is AI using my data?” My first thought is, “Yeah, no kidding.”

Katie Robber]]> full https://www.trustinsights.ai/wp-content/uploads/2025/08/In-Ear-Insights-Reviewing-AI-Data-Privacy-Basics.png In-Ear Insights: Reviewing AI Data Privacy Basics false no 0:00 No no https://www.trustinsights.ai/blog/2025/08/in-ear-insights-how-to-identify-and-mitigate-bias-in-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-identify-and-mitigate-bias-in-ai Wed, 13 Aug 2025 08:48:49 +0000 https://www.trustinsights.ai/?p=92788 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris tackle an issue of bias in AI, including identifying it, coming up with strategies to mitigate it, and proactively guarding against it. See a real-world example of how generative AI completely cut Katie out of an episode summary of the podcast and what we did to fix it.

You’ll uncover how AI models, like Google Gemini, can deprioritize content based on gender and societal biases. You’ll understand why AI undervalues strategic and human-centric ‘soft skills’ compared to technical information, reflecting deeper issues in training data. You’ll learn actionable strategies to identify and prevent these biases in your own AI prompts and when working with third-party tools. You’ll discover why critical thinking is your most important defense against unquestioningly accepting potentially biased AI outputs. Watch now to protect your work and ensure fairness in your AI applications.

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, let’s tackle the issue of bias within large language models. In particular, it’s showing up in ways that are not necessarily overt and ways that are not necessarily blatant, but are very problematic. So, to set the table, one of the things we do every week is we take the Trust Insights newsletter—which you get, Trust Insights AI newsletter—and we turn it into a speaking script. Then Katie reads this script aloud. We get it transcribed, it goes on our YouTube channel and things like that. Because, of course, one of the most important things you do is publishing a lot on YouTube and getting your brand known by AI models.

Christopher S. Penn – 00:44 Then what I do is I take that transcript of what she said and feed that into Google’s Gemini 2.5 Pro model, and it creates the YouTube description and the tags. Here’s what happened recently with this.

So I gave it the transcript and I said, “Make me my stuff.” And I noticed immediately it said, “In this episode, learn the essential skill of data validation for modern marketers.” Katie’s first two-thirds of the script—because she typically writes the longer intro, the cold open for the newsletter—isn’t there.

And I said, “You missed half the show.” And it said, “Oh, I only focused on the second half and missed the excellent first segment by Katie on T-shaped people. Thank you for the correction.” And it spit out the correct version after that. And I said, “Why? Why did you miss that?”

Christopher S. Penn – 01:43 And it said,]]> full https://www.trustinsights.ai/wp-content/uploads/2025/08/In-Ear-Insights-How-to-Identify-and-Mitigate-Bias-in-AI.png In-Ear Insights: How to Identify and Mitigate Bias in AI false no 0:00 No no https://www.trustinsights.ai/blog/2025/07/in-ear-insights-everything-wrong-with-vibe-coding-and-how-to-fix-it/?pk_campaign=feed&pk_kwd=in-ear-insights-everything-wrong-with-vibe-coding-and-how-to-fix-it Wed, 30 Jul 2025 09:00:01 +0000 https://www.trustinsights.ai/?p=92756 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the pitfalls and best practices of “vibe coding” with generative AI.

You will discover why merely letting AI write code creates significant risks. You will learn essential strategies for defining robust requirements and implementing critical testing. You will understand how to integrate security measures and quality checks into your AI-driven projects. You will gain insights into the critical human expertise needed to build stable and secure applications with AI. Tune in to learn how to master responsible AI coding and avoid common mistakes!

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, if you go on LinkedIn, everybody, including tons of non-coding folks, has jumped into vibe coding, the term coined by OpenAI co-founder Andre Karpathy. A lot of people are doing some really cool stuff with it. However, a lot of people are also, as you can see on X in a variety of posts, finding out the hard way that if you don’t know what to ask for—say, application security—bad things can happen. Katie, how are you doing with giving into the vibes?

Katie Robbert – 00:38 I’m not. I’ve talked about this on other episodes before. For those who don’t know, I have an extensive background in managing software development. I myself am not a software developer, but I have spent enough time building and managing those teams that I know what to look for and where things can go wrong. I’m still really skeptical of vibe coding.

We talked about this on a previous podcast, which if you want to find our podcast, it’s @TrustInsightsAI_TIpodcast, or you can watch it on YouTube. My concern, my criticism, my skepticism of vibe coding is if you don’t have the basic foundation of the SDLC, the software development lifecycle, then it’s very easy for you to not do vibe coding correctly.

Katie Robbert – 01:42 My understanding is vibe coding is you’re supposed to let the machine do it. I think that’s a complete misunderstanding of what’s actually happening because you still have to give the machine instruction and guardrails. The machine is creating AI. Generative AI is creating the actual code. It’s putting together the pieces—the commands that comprise a set of JSON code or Python code or whatever it is you’re saying, “I want to create an app that does this.” And generative AI is like, “Cool, let’s do it.” You’re going through the steps. You still need to know what ]]> full https://www.trustinsights.ai/wp-content/uploads/2025/07/In-Ear-Insights-Everything-Wrong-with-Vibe-Coding-and-How-to-Fix-It.png In-Ear Insights: Everything Wrong with Vibe Coding and How to Fix It false no 0:00 No no https://www.trustinsights.ai/blog/2025/07/in-ear-insights-how-to-improve-martech-roi-with-generative-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-improve-martech-roi-with-generative-ai Wed, 23 Jul 2025 09:19:58 +0000 https://www.trustinsights.ai/?p=92740 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to unlock hidden value and maximize martech ROI from your existing technology using AI-powered “manuals on demand.” You will discover how targeted AI research can reveal unused features in your current software, transforming your existing tools into powerful solutions. You will learn to generate specific, actionable instructions that eliminate the need to buy new, expensive technologies. You will gain insights into leveraging advanced AI agents to provide precise, reliable information for your unique business challenges. You will find out how this strategy helps your team overcome common excuses and achieve measurable results by optimizing your current tech stack. Tune in to revolutionize how you approach your technology investments.

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, let’s get a little bombastic and say, Katie, we’re gonna double everyone’s non-existent ROI on AI with the most unused—underused—feature that literally I’ve not seen anyone doing, and that is manuals on demand. A little while ago, in our AI for Market Gender VI use cases for marketers course and our mastering prompt engine for Marketers course and things like that, we were having a conversation internally with our team saying, hey, what else can we be doing to market these courses? One of the things that occurred to me as I was scrolling around our Thinkific system we used is there’s a lot of buttons in here. I don’t know what most of them do, and I wonder if I’m missing something.

Christopher S. Penn – 00:53 So, I commissioned a Deep Research report in Gemini saying, hey, this is the version of Thinkific we’re on. This is the plan we’re on. Go do research on the different ways that expert course creators market their courses with the features in Thinkific. It came back with a 28-page report that we then handed off to Kelsey on our team to say, hey, go read this report and see, because it contains step-by-step instructions for things that we could be doing in the system to upsell and cross-sell our courses. As I was thinking about it, going, wow, we should be doing this more often.

Christopher S. Penn – 01:28 Then a friend of mine just got a new phone, a Google Pixel phone, and is not skilled at using Google’s all the bells and whistles, but she has a very specific use case: she wants to record concert videos with it. So I said, okay, let’s create a manual for just what features of the Pixel phone are best for concerts. Create a step-by-s]]> full https://www.trustinsights.ai/wp-content/uploads/2025/07/In-Ear-Insights-How-to-Improve-Martech-ROI-with-Generative-AI.png In-Ear Insights: How to Improve Martech ROI with Generative AI false no 0:00 No no https://www.trustinsights.ai/blog/2025/07/in-ear-insights-generative-ai-strategy-and-integration-mail-bag/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-strategy-and-integration-mail-bag Wed, 16 Jul 2025 09:30:07 +0000 https://www.trustinsights.ai/?p=92726 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss critical questions about integrating AI into marketing. You will learn how to prepare your data for AI to avoid costly errors. You will discover strategies to communicate the strategic importance of AI to your executive team. You will understand which AI tools are best for specific data analysis tasks. You will gain insights into managing ethical considerations and resource limitations when adopting AI. Watch now to future-proof your marketing approach!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, boy, have we got a whole bunch of mail. We’ve obviously been on the road a lot doing events. A lot. Katie, you did the AI for B2B summit with the Marketing AI Institute not too long ago, and we have piles of questions—there’s never enough time.

Let’s tackle this first one from Anthony, which is an interesting question. It’s a long one.

He said in Katie’s presentation about making sure marketing data is ready to work in AI: “We know AI sometimes gives confident but incorrect results, especially with large data sets.” He goes with this long example about the Oscars. How can marketers make sure their data processes catch small but important AI-generated errors like that? And how mistake-proof is the 6C framework that you presented in the talk?

Katie Robbert – 00:48 The 6C framework is only as error-proof as you are prepared, is maybe the best way to put it. Unsurprisingly, I’m going to pull up the five P’s to start with: Purpose, People, Process, Platform, Performance.

This is where we suggest people start with getting ready before you start using the 6 Cs because first you want to understand what it is that I’m trying to do. The crappy answer is nothing is ever fully error-proof, but things are going to get you pretty close.

When we talk about marketing data, we always talk about it as directional versus exact because there are things out of your control in terms of how it’s collected, or what people think or their perceptions of what the responses should be, whatever the situation is.

Katie Robbert – 01:49 If it’s never going to be 100% perfect, but it’s going to be directional and give you the guidance you need to answer the question being asked.

Which brings us back to the five Ps: What is the question being asked? Why are we doing this? Who’s involved?

This is where you put down who are the people contributing the data, but also who are the people owning the data, cleaning the data, maintaining the data, accessing]]> full https://www.trustinsights.ai/wp-content/uploads/2025/07/In-Ear-Insights-Generative-AI-Strategy-and-Integration-Mail-Bag.png In-Ear Insights: Generative AI Strategy and Integration Mail Bag false no 0:00 No no https://www.trustinsights.ai/blog/2025/07/in-ear-insights-artisanal-vs-ai-in-content-marketing/?pk_campaign=feed&pk_kwd=in-ear-insights-artisanal-vs-ai-in-content-marketing Wed, 09 Jul 2025 09:32:31 +0000 https://www.trustinsights.ai/?p=92711 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the evolving perception and powerful benefits of using generative AI in your content creation. How should we think about AI in content marketing?

You’ll discover why embracing generative AI is not cheating, but a strategic way to elevate your content. You’ll learn how these advanced tools can help you overcome creative blocks and accelerate your production timeline. You’ll understand how to leverage AI as a powerful editor and critical thinker, refining your work and identifying crucial missing elements. You’ll gain actionable strategies to combine your unique expertise with AI, ensuring your content remains authentic and delivers maximum value. Tune in to unlock AI’s true potential for your content strategy

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, it is the battle between artisanal, handcrafted, organic content and machine-made. The Etsys versus the Amazons. We’re talking specifically about the use of AI to make stuff. Katie, you had some thoughts and some things you’re wrestling with about this topic, so why don’t you set the table, if you will.

Katie Robbert – 00:22 It’s interesting because we always talk about people first and AI forward and using these tools. I feel like what’s happened is now there’s a bit of a stigma around something that’s AI-generated. If you used AI, you’re cheating or you’re shortcutting or it’s no longer an original thought. I feel like in some circumstances that’s true. However, there are other circumstances, other situations, where using something like generative AI can perhaps get you past a roadblock.

For example, if you haven’t downloaded it yet, please go ahead and download our free AI strategy kit. The AI Ready Marketing Strategy Kit, which you can find at TrustInsights AIkit, I took just about everything I know about running Trust Insights and I used generative AI to help me compile all of that information.

Katie Robbert – 01:34 Then I, the human, went through, refined it, edited, made sure it was accurate, and I put it all into this kit. It has frameworks, examples, stories—everything you could use to be successful. Now I’m using generative AI to help me build it out as a course. I had a moment this morning where I was like, I really shouldn’t be using generative AI. I should be doing this myself because now it’s disingenuous, it’s not authentic, it’s not me because the tool is creating it faster. Then I stopped and I actually read through what was bei]]> https://www.trustinsights.ai/wp-content/uploads/2025/07/In-Ear-Insights-Artisanal-vs-AI-in-Content-Marketing.png In-Ear Insights: Artisanal vs AI in Content Marketing false no 0:00 No no https://www.trustinsights.ai/blog/2025/06/in-ear-insights-the-generative-ai-sophomore-slump-part-2/?pk_campaign=feed&pk_kwd=in-ear-insights-the-generative-ai-sophomore-slump-part-2 Wed, 25 Jun 2025 09:16:14 +0000 https://www.trustinsights.ai/?p=92661 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to break free from the AI sophomore slump. You’ll learn why many companies stall after early AI wins. You’ll discover practical ways to evolve your AI use from simple experimentation to robust solutions. You’ll understand how to apply strategic frameworks to build integrated AI systems. You’ll gain insights on measuring your AI efforts and staying ahead in the evolving AI landscape. Watch now to make your next AI initiative a success!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, part two of our Sophomore Slump series. Boy, that’s a mouthful.

Katie Robbert – 00:07 We love alliteration.

Christopher S. Penn – 00:09 Yahoo. Last week we talked about what the sophomore slump is, what it looks like, and some of the reasons for it—why people are not getting value out of AI and the challenges. This week, Katie, the sophomore slump, you hear a lot in the music industry? Someone has a hit album and then their sophomore album, it didn’t go. So they have to figure out what’s next. When you think about companies trying to get value out of AI and they’ve hit this sophomore slump, they had early easy wins and then the easy wins evaporated, and they see all the stuff on LinkedIn and wherever else, like, “Oh, look, I made a million dollars in 28 minutes with generative AI.” And they’re, “What are we doing wrong?”

Christopher S. Penn – 00:54 How do you advise somebody on ways to think about getting out of their sophomore slump? What’s their next big hit?

Katie Robbert – 01:03 So the first thing I do is let’s take a step back and see what happened. A lot of times when someone hits that sophomore slump and that second version of, “I was really successful the first time, why can’t I repeat it?” it’s because they didn’t evolve. They’re, “I’m going to do exactly what I did the first time.” But your audience is, “I saw that already. I want something new, I want something different.” Not the exact same thing you gave me a year ago. That’s not what I’m interested in paying for and paying attention to.

Katie Robbert – 01:36 So you start to lose that authority, that trust, because it’s why the term one hit wonder exists—you have a one hit wonder, you have a sophomore slump. You have all of these terms, all to say, in order for people to stay interested, you have to stay interesting. And by that, you need to evolve, you need to change. But not j]]> https://www.trustinsights.ai/wp-content/uploads/2025/06/In-Ear-Insights-The-Generative-AI-Sophomore-Slump-Part-2.png In-Ear Insights: The Generative AI Sophomore Slump, Part 2 false no 0:00 No no https://www.trustinsights.ai/blog/2025/06/in-ear-insights-the-generative-ai-sophomore-slump-part-1/?pk_campaign=feed&pk_kwd=in-ear-insights-the-generative-ai-sophomore-slump-part-1 Wed, 18 Jun 2025 09:16:12 +0000 https://www.trustinsights.ai/?p=92660 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the generative AI sophomore slump.

You will discover why so many businesses are stuck at the same level of AI adoption they were two years ago. You will learn how anchoring to initial perceptions and a lack of awareness about current AI capabilities limits your organization’s progress. You will understand the critical difference between basic AI exploration and scaling AI solutions for significant business outcomes. You will gain insights into how to articulate AI’s true value to stakeholders, focusing on real world benefits like speed, efficiency, and revenue. Tune in to see why your approach to AI may need an urgent update!

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, let’s talk about the sophomore slump. Katie, you were talking about the sophomore slump in regards to generative AI. I figured we could make this into a two-part series. So first, what is the sophomore slump?

Katie Robbert – 00:15 So I’m calling it the sophomore slump.

Basically, what I’m seeing is a trend of a lot of companies talking about, “We tried. We started implementing AI two years ago—generative AI to be specific—and we’re stalled out.”

We are at the same place we were two years ago. We’ve optimized some things. We’re using it to create content, maybe create some images, and that’s about it.

Everyone fired everyone. There’s no one here. It’s like a ghost town. The machines are just whirring away in the background.

And I’m calling it the sophomore slump because I’m seeing this pattern of companies, and it all seems to be—they’re all saying the same—two years ago.

Katie Robbert – 01:03 And two years ago is when generative AI really hit the mainstream market in terms of its availability to the masses, to all of us, versus someone, Chris, like you, who had been using it through IBM and other machine learning systems and homegrown systems.

So I bring it up because it’s interesting, because I guess there’s a lot to unpack here.

AI is this magic tool that’s gonna solve your problems and do all the things and make you dinner and clean your room.

I feel like there’s a lot of things wrong or a lot of things that are just not going right. A lot of companies are hitting this two-year mark, and they’re like, “What now? What happened? Am I better off? Not really.”

Katie Robbert – 02:00 I’m just paying for more stuff. So Chris, are you seeing this as well? Is ]]> https://www.trustinsights.ai/wp-content/uploads/2025/06/In-Ear-Insights-The-Generative-AI-Sophomore-Slump-Part-1.png In-Ear Insights: The Generative AI Sophomore Slump, Part 1 false no 0:00 No no https://www.trustinsights.ai/blog/2025/06/in-ear-insights-how-generative-ai-reasoning-models-work/?pk_campaign=feed&pk_kwd=in-ear-insights-how-generative-ai-reasoning-models-work Wed, 11 Jun 2025 08:56:43 +0000 https://www.trustinsights.ai/?p=92640 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the Apple AI paper and critical lessons for effective prompting, plus a deep dive into reasoning models.

You’ll learn what reasoning models are and why they sometimes struggle with complex tasks, especially when dealing with contradictory information. You’ll discover crucial insights about AI’s “stateless” nature, which means every prompt starts fresh and can lead to models getting confused. You’ll gain practical strategies for effective prompting, like starting new chats for different tasks and removing irrelevant information to improve AI output. You’ll understand why treating AI like a focused, smart intern will help you get the best results from your generative AI tools. Tune in to learn how to master your AI interactions!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, there is so much in the AI world to talk about. One of the things that came out recently that I think is worth discussing, because we can talk about the basics of good prompting as part of it, Katie, is a paper from Apple. Apple’s AI efforts themselves have stalled a bit, showing that reasoning models, when given very complex puzzles—logic-based puzzles or spatial-based puzzles, like moving blocks from stack to stack and getting them in the correct order—hit a wall after a while and then just collapse and can’t do anything. So, the interpretation of the paper is that there are limits to what reasoning models can do and that they can kind of confuse themselves. On LinkedIn and social media and stuff,

Christopher S. Penn – 00:52 Of course, people have taken this to the illogical extreme, saying artificial intelligence is stupid, nobody should use it, or artificial general intelligence will never happen. None of that is within the paper. Apple was looking at a very specific, narrow band of reasoning, called deductive reasoning. So what I thought we’d talk about today is the paper itself to a degree—not a ton about it—and then what lessons we can learn from it that will make our own AI practices better. So to start off, when we talk about reasoning, Katie, particularly you as our human expert, what does reasoning mean to the human?

Katie Robbert – 01:35 When I think, if you say, “Can you give me a reasonable answer?” or “What is your reason?” Thinking about the different ways that the word is casually thrown around for humans. The way that I think about it is, if you’re looking for a reasonable answer to something, then that means that you are putting the expectat]]> full https://www.trustinsights.ai/wp-content/uploads/2025/06/In-Ear-Insights-How-Generative-AI-Reasoning-Models-Work.png In-Ear Insights: How Generative AI Reasoning Models Work false no 0:00 No no https://www.trustinsights.ai/blog/2025/06/in-ear-insights-the-ai-ready-marketing-strategy-kit/?pk_campaign=feed&pk_kwd=in-ear-insights-the-ai-ready-marketing-strategy-kit Wed, 04 Jun 2025 09:33:29 +0000 https://www.trustinsights.ai/?p=92623 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss their new AI-Ready Marketing Strategy Kit. You’ll understand how to assess your organization’s preparedness for artificial intelligence. You’ll learn to measure the return on your AI initiatives, uncovering both efficiency and growth opportunities. You’ll gain clarity on improving data quality and optimizing your AI processes for success. You’ll build a clear roadmap for integrating AI and fostering innovation across your business. Tune in to transform your approach to AI!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, let’s talk about AI readiness. We launched on Tuesday our new AI Readiness Kit. And so, Katie, just to start off, what’s in for the people who didn’t read all the emails? What’s in the thing, and why are people supposed to look into this?

Katie Robbert – 00:16 So I’m really proud of this new piece that we put together because we talk a lot about the different frameworks. We talk about Five Ps, we talk about Six Cs, we talk about STEM, we talk about how do you measure ROI? And we talk about them all in different contexts. So we took the opportunity to—

Speaker 3 – 00:42 Put them all together into one place.

Katie Robbert – 00:44 In a hopefully coherent flow. To say, if you’re trying to get yourself together, if you’re trying to integrate AI, or if you already have and you’re struggling to really make it stick, use this AI Ready Marketing Strategy Kit. So you can get that at TrustInsights.AI/kit. It’s really the best of the best. It’s all of our frameworks. But it’s not just, “Here’s a framework, good luck.”

Speaker 3 – 01:18 There’s context around how to use it.

Katie Robbert – 01:20 There’s checklists, there’s calculations, there’s explanations, there’s expectations—it’s basically the best alternative to having me and Chris sitting next to you when we can’t sit next to you to say, “You should think about doing this.”

Speaker 3 – 01:41 You should probably think about this.

Katie Robbert – 01:43 Here’s how you would approach this. So it’s sort of an—

Speaker 3 – 01:46 Extension of me and Chris sitting with you to walk you through these things.

Christopher S. Penn – 01:52 One of the questions that people have the most, especially as they start doing AI pilots and stuff, is what’s the RO]]> full https://www.trustinsights.ai/wp-content/uploads/2025/06/In-Ear-Insights-The-AI-Ready-Marketing-Strategy-Kit.png In-Ear Insights: The AI-Ready Marketing Strategy Kit false no 0:00 No no https://www.trustinsights.ai/blog/2025/05/in-ear-insights-should-you-hire-an-ai-expert/?pk_campaign=feed&pk_kwd=in-ear-insights-should-you-hire-an-ai-expert Wed, 28 May 2025 08:50:53 +0000 https://www.trustinsights.ai/?p=92594 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the critical considerations when deciding whether to hire an external AI expert or develop internal AI capabilities.

You’ll learn why it is essential to first define your organization’s specific AI needs and goals before seeking any AI expertise. You’ll discover the diverse skill sets that comprise true AI expertise, beyond just technology, and how to effectively vet potential candidates. You’ll understand how AI can magnify existing organizational challenges and why foundational strategy must precede any AI solution. You’ll gain insight into how to strategically approach AI implementation to avoid costly mistakes and ensure long-term success for your organization. Watch now to learn how to make the right choice for your organization’s AI future.

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, a few people have asked us the question, should I hire an AI expert—a person, an AI expert on my team—or should I try to grow AI expertise, someone as an AI leader within my company? I can see there being pros and cons to both, but, Katie, you are the people expert. You are the organizational behavior expert. I know the answer is it depends. But at first blush, when someone comes to you and says, hey, should I be hiring an AI expert, somebody who can help shepherd my organization through the crazy mazes of AI, or should I grow my own experts? What is your take on that question?

Katie Robbert – 00:47 Well, it definitely comes down to it depends. It depends on what you mean by an AI expert. So, what is it about AI that they are an expert in? Are you looking for someone who is staying up to date on all of the changes in AI? Are you looking for someone who can actually develop with AI tools? Or are you looking for someone to guide your team through the process of integrating AI tools? Or are you looking for all of the above? Which is a totally reasonable response, but that doesn’t mean you’ll get one person who can do all three. So, I think first and foremost, it comes down to what is your goal? And by that I mean, what is the AI expertise that your team is lacking?

Katie Robbert – 01:41 Or what is the purpose of introducing AI into your organization? So, unsurprisingly, starting with the 5P framework, the 5Ps are purpose, people, process, platform, performance, because marketers like alliteration. So, purpose. You want to define clearly what AI means to the company, so not your ‘what I did over summer vacation’ essay, but what AI means to me.

What do you want to do with AI? Why are you bringing AI in? Is it because I want to kee]]> full https://www.trustinsights.ai/wp-content/uploads/2025/05/In-Ear-Insights-Should-You-Hire-an-AI-Expert.png In-Ear Insights: Should You Hire An AI Expert? false no 0:00 No no https://www.trustinsights.ai/blog/2025/05/in-ear-insights-navigating-terrible-leadership-and-the-ai-career-threat/?pk_campaign=feed&pk_kwd=in-ear-insights-navigating-terrible-leadership-and-the-ai-career-threat Wed, 21 May 2025 09:00:07 +0000 https://www.trustinsights.ai/?p=92573 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss troubling new trends in leadership and how you can navigate an increasingly demanding work environment.

You’ll learn to identify the difference between tough business decisions and terrible leadership tactics. You’ll discover practical strategies to document issues and set healthy boundaries with difficult leaders. You’ll understand the critical importance of building your personal brand and professional network as your career life raft. You’ll explore how to use new tools, including AI, to enhance your skills and uncover hidden job opportunities. Watch this episode to gain actionable advice and empower yourself in today’s evolving workplace!

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, Katie, “Everybody’s replaceable; work-life balance’s your problem”—which is what the CEO of shapewear company Skims and the label Good American had to say. Other people in positions of power have had equally less. I guess I don’t even know how to put this.

It is a definite tone shift. This is from a Wall Street Journal article from May 11, titled “Everybody’s Replaceable: The New Ways Bosses Talk About Workers.” And the punchline is: shut up, stop complaining, and do more work for less pay.

Katie Robbert – 00:46 The thing I took away from this is, first of all, look at the companies that have been listed. So you have Skims, you have Starbucks, you have J.P. Morgan, you have Uber. Yeah. So these are big global tech companies and consumer brands.

So Skims and Good American are the Kardashians. So take that with a big fat boulder of salt.

Katie Robbert – 01:19 Uber has had nothing but issues.

Katie Robbert – 01:23 Starbucks, same thing. So I look at these companies and—yup, that’s completely on brand for those particular companies because those particular companies have had really shitty leadership issues.

Katie Robbert – 01:38 For a long time.

Katie Robbert – 01:40 Therefore, I read this article and I don’t fully believe that it’s a good representation of quote-unquote corporate America. I just don’t.

I’m not naive enough to think that there aren’t leaders out there in companies speaking this way. They absolutely are. But that’s not new. AI is not creating this problem.

Katie Robbert – 02:07 This is not a new problem.

Katie Robbert – 02:09 New tech, same problem. If your lea]]> https://www.trustinsights.ai/wp-content/uploads/2025/05/in_ear_insights_navigating_terrible_leadership_and_the_ai_career_threat.png In-Ear Insights: Navigating Terrible Leadership and the AI Career Threat false no 0:00 No no https://www.trustinsights.ai/blog/2025/05/in-ear-insights-no-code-ai-solutions-doesnt-mean-no-work/?pk_campaign=feed&pk_kwd=in-ear-insights-no-code-ai-solutions-doesnt-mean-no-work Wed, 14 May 2025 09:42:27 +0000 https://www.trustinsights.ai/?p=92546 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the crucial difference between ‘no code AI solutions’ and ‘no work’ when using AI tools.

You’ll grasp why seeking easy no-code solutions often leads to mediocre AI outcomes. You’ll learn the vital role critical thinking plays in getting powerful results from generative AI. You’ll discover actionable techniques, like using frameworks and better questions, to guide AI. You’ll understand how investing thought upfront transforms AI from a simple tool into a strategic partner. Watch the full episode to elevate your AI strategy!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, I have a bone to pick with a lot of people in marketing around AI and AI tools. And my bone to pick is this, Katie. There isn’t a day that goes by either in Slack or mostly on LinkedIn when some person is saying, “Oh, we need a no code tool for this.” “How do I use AI in a no code tool to evaluate real estate proposals?”

And the thing is, when I read what they’re trying to do, they seem to have this idea that no code equals no work. That it’s somehow magically just going to do the thing. And I can understand the past tense aversion to coding because it’s a very difficult thing to do.

Christopher S. Penn – 00:49 But in today’s world with generative AI, coding is as straightforward as not coding in terms of the ability to make stuff. Because generative AI can do both, and they both have very strong prerequisites, which is you gotta think things through. It’s not no work. Neither case is it no work. Have you seen this also on the various places we hang out?

Katie Robbert – 01:15 Well, first, welcome to the club. How well do your ranty pants fit? Because that’s what you are wearing today. Maybe you’re in the ranty shirt club. I don’t know.

It’s… I think we were talking about this last week because I was asking—and I wasn’t asking from a ‘I don’t want to do the work’ standpoint, but I was asking from a ‘I’m not a coder, I don’t want to deal with code, but I’m willing to do the work’ standpoint. And you showed me a system like Google Colab that you can go into, you can tell it what you want to do, and you can watch it build the code. It can either keep it within the system or you can copy the code and put it elsewhere. And that’s true of pretty much any generative AI system.

Katie Robbert – 02:04 You can say, “I want you to build code for me to be able to do X.” Now, the reaso]]> https://www.trustinsights.ai/wp-content/uploads/2025/05/In-Ear-Insights-No-Code-AI-Solutions-Doesnt-Mean-No-Work.png In-Ear Insights: No Code AI Solutions Doesn’t Mean No Work false no 0:00 No no https://www.trustinsights.ai/blog/2025/05/in-ear-insights-codependency-on-generative-ai-chatgpt/?pk_campaign=feed&pk_kwd=in-ear-insights-codependency-on-generative-ai-chatgpt Wed, 07 May 2025 09:22:55 +0000 https://www.trustinsights.ai/?p=92525 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss codependency on generative AI and the growing risks of over-relying on generative AI tools like ChatGPT.

You’ll discover the hidden dangers when asking AI for advice, especially concerning health, finance, or legal matters. You’ll learn why AI’s helpful answers aren’t always truthful and how outdated information can mislead you. You’ll grasp powerful prompting techniques to guide AI towards more accurate and relevant results. You’ll find strategies to use AI more critically and avoid potentially costly mistakes. Watch the full episode for essential strategies to navigate AI safely and effectively!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, let’s talk about the way that people are prompting generative AI tools like ChatGPT. I saw my friend Rebecca the other day was posting about how she had asked ChatGPT about a bunch of nutritional supplements she was taking and some advice for them. And I immediately went, oh, stop.

We have three areas where we do not just ask generative AI for information because of the way the model is trained. Those areas are finance, law and health. In those areas, they’re high risk areas. If you’re asking ChatGPT for advice without providing good data, the answers are really suspect. Katie, you also had some thoughts about how you’re seeing people using ChatGPT on LinkedIn.

Katie Robbert – 00:55 Well, I was saying this morning that it’s hard to go on LinkedIn. LinkedIn is where we’re all trying to connect with each other professionally, be thought leaders, share our experience. But it’s so hard for me personally, and this is my own opinion because every time I open LinkedIn the first thing I see is a post that says, “Today I asked ChatGPT.”

Every post starts with, “So I was talking with ChatGPT.” “ChatGPT was telling me this morning.” And the codependency that I’m seeing being built with these tools is alarming to me and I’m oversimplifying it, but I don’t see these tools as any better than when you were just doing an Internet search. What I mean by that is the quality of the data is not necessarily better.

Katie Robbert – 01:49 They can do more bells and whistles, they have more functions, they can summarize things, they can do backflips and create images and whatever. But the data is not different. You’re not getting better quality data. If anything, you’re probably getting more junk because you’re not asking specific questions like you ]]> full https://www.trustinsights.ai/wp-content/uploads/2025/05/In-Ear-Insights-Codepedency-on-Generative-AI-ChatGPT.png In-Ear Insights: Codependency on Generative AI & ChatGPT false no 0:00 No no https://www.trustinsights.ai/blog/2025/04/in-ear-insights-generative-ai-transformation-vs-optimization/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-transformation-vs-optimization Wed, 30 Apr 2025 09:43:47 +0000 https://www.trustinsights.ai/?p=92507 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss navigating the pressure of AI transformation and competitive parity.

You’ll learn why chasing AI trends without a clear purpose can harm your business. You’ll discover how to distinguish between merely optimizing current operations and driving real innovation. You’ll understand the importance of grounding your strategy in actual customer needs, not just competitor activity. You’ll explore how to assess new technologies like AI without getting caught up in hype. Watch the full episode to gain clarity on making smart AI decisions for your company!

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, let’s talk about Oh, Katie, it’s your favorite term—digital transformation, specifically AI transformation. The context for this is we got an email from a colleague, a friend, who said, “Hey, I want to talk about how we could be using AI to take our company’s stuff”—they’re a software company—”and get it to parity with the the the current environment. And there’s got to be a way, an AI way to do that.”

We both had strong reactions to this, and I I’m happy to share mine in a little bit, but I wanted to get your take on this person’s request. How do I use AI to to catch up to where the rest of my industry is right now?

Katie Robbert – 00:49 I feel like it goes back to that very old, keeping up with the Joneses kind of phrasing, where it’s like, gosh, I’m gonna throw a bunch of cliches at you. The grass is greener. Keeping up with the Joneses—all those things where you look over the fence and you see what the other guy is doing, and you think, I want that.

Versus looking at your own environment, look at your own home. What you have, and saying, you know what? This is good. This suits me. And that’s the challenge I have when I hear things like that, of, do I need—I mean, I I went through this myself a couple weeks ago. We talked about it when we talked about MCPs on the podcast. It started with, am I falling behind?

Katie Robbert – 01:37 Do I need to be keeping up with the Joneses? And the answer was no. I need to stay the course and do what I’m doing. Yes, I need to be aware and not put my head in the sand. But trying to do what other people are seemingly doing doesn’t fit my needs or the needs of the company.

It’s not where I’m needed. And so when I see even bigger initiatives to try to keep up with the industry as a whole, my first question is, why? What is it that is going to benefit your company]]> https://www.trustinsights.ai/wp-content/uploads/2025/04/In-Ear-Insights-Generative-AI-TRansformation-vs.-Optimization.png In-Ear Insights: Generative AI Transformation vs. Optimization false no 0:00 No no https://www.trustinsights.ai/blog/2025/04/in-ear-insights-the-problem-with-buyer-personas/?pk_campaign=feed&pk_kwd=in-ear-insights-the-problem-with-buyer-personas Wed, 23 Apr 2025 09:03:39 +0000 https://www.trustinsights.ai/?p=92480 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the problem with buyer personas and how to master B2B marketing with smarter audience targeting. You’ll learn the critical differences between ideal customer profiles and buyer personas—and why using both transforms your strategy. You’ll discover how to ethically leverage AI and data to identify hidden pain points before prospects even recognize them. You’ll explore practical frameworks to align your content with every stage of the customer journey, from awareness to retention. You’ll gain actionable tactics to avoid common pitfalls and turn casual viewers into loyal buyers. Watch now to revolutionize how you connect with your audience!

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, let’s talk about buyer personas in B2B marketing—how AI is affecting them and why.

Actually, I want to dig into this, Katie, and I want your take. What’s the difference to you between an ideal customer profile and a buyer persona? A lot of people use those terms interchangeably, but they may or may not mean the same thing. What’s your take?

Katie Robbert – 00:28 I can understand why people use them interchangeably because there’s this notion that it’s some kind of representation of somebody who would eventually purchase something from you. In that sense, they are the same. The nuance—at least the way I break them out—is an ideal customer profile covers awareness and consideration, whereas a buyer persona covers purchase and the stages beyond that.

The challenge I see in B2B marketing is many people create buyer personas, which is great, but there are assumptions baked in that this person already fully understands the problem and that you can solve it for them.

If you’re using your buyer persona to do a content strategy—to create content or evaluate your marketing—you’ve already skipped over awareness and consideration. You’re at the buying stage now.

When we beta-tested our ideal customer profile service, our friend Brooke Sellis from B Squared gave us her buyer persona playbook to compare against the ICP we built. That’s where we saw the disconnect—her playbook assumed everyone was already in the pipeline and knew the problem.

Our ICP analysis is meant to help marketers approach people who may not even know there’s a problem yet. You create content that resonates so when they *do* identify the problem, they enter your buyer’s journey. The ICP gets to them before that.

The challenge with buyer personas is they focus too much on someone already knowing what’s wrong and looking for a solution. In marketing, 99% of the time, they don’t know there’s a problem—or the]]> full https://www.trustinsights.ai/wp-content/uploads/2025/04/In-Ear-Insights-The-Problem-with-Buyer-Personas.png In-Ear Insights: The Problem with Buyer Personas false no 0:00 No no https://www.trustinsights.ai/blog/2025/04/in-ear-insights-what-is-model-context-protocol-mcp-and-agentic-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-what-is-model-context-protocol-mcp-and-agentic-ai Wed, 16 Apr 2025 09:44:33 +0000 https://www.trustinsights.ai/?p=92341 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss MCP (Model Context Protocol) and agentic marketing. You’ll learn how MCP connects AI tools to automate tasks—but also why technical expertise is essential to use it effectively. You’ll discover the three layers of AI adoption, from manual prompts to fully autonomous agents, and why skipping foundational steps leads to costly mistakes. You’ll see why workflow automation (like N8N) is the bridge to agentic AI, and how to avoid falling for social media hype. Finally, you’ll get practical advice on staying ahead without drowning in tech overwhelm. Watch now to demystify AI’s next big thing!

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, let’s talk about MCP—Model Context Protocol—and its applications for marketing and what it means. Katie, you said you have questions.

Katie Robbert – 00:13 I do. I saw you posted in our free Slack group, Analytics for Marketers, towards the end of last week that one of the models had MCP available. When I see notifications like that, my first thought is: Is this something I need to pay attention to? Usually, you’re really good about letting me know, but I am a fully grown human who needs to be responsible for what I should be paying attention to and not just relying on the data scientist on my team. That was my first gut reaction—which is fair, because you’re a busy person. I like to keep you very busy, and you don’t always have time to let me know what I should be paying attention to. So that was problem one.

Problem number two is, yes, you post things typically ahead of when they become more commonplace announcements. I saw a post this morning that I shared with you about MCP and agentic marketing processes, and how it’s going to replace your SEO if you’re doing traditional SEO. For some reason, that raised all of my insecurities and anxieties. Oh my gosh, I really am falling behind because I like to tell people about getting their foundation squared away. If I’m being really honest with myself, I think I focus on that because I feel so lost when I think about AI, agentic processes, MCP, N8N, and all these other things. So I’m like, let me focus on what I know best.

But I am now in the boat where I feel like my boat is trailing behind the giant AI yacht. I’m dog-paddling to try to keep up, and I’m just not there. So help me understand a couple of things. One, what i]]> https://www.trustinsights.ai/wp-content/uploads/2025/04/In-Ear-Insights-What-is-Model-Context-Protocol-MCP-And-Agentic-AI.png In-Ear Insights: What is Model Context Protocol (MCP) And Agentic AI? false no 0:00 No no https://www.trustinsights.ai/blog/2025/04/in-ear-insights-what-is-retrieval-augmented-generation-rag/?pk_campaign=feed&pk_kwd=in-ear-insights-what-is-retrieval-augmented-generation-rag Wed, 09 Apr 2025 09:03:54 +0000 https://www.trustinsights.ai/?p=92323 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss Retrieval Augmented Generation (RAG). You’ll learn what RAG is and how it can significantly improve the accuracy and relevance of AI responses by using your own data. You’ll understand the crucial differences between RAG and typical search engines or generative AI models, clarifying when RAG is truly needed. You’ll discover practical examples of when RAG becomes essential, especially for handling sensitive company information and proprietary knowledge. Tune in to learn when and how RAG can be a game-changer for your data strategy and when simpler AI tools will suffice!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, let’s…

Christopher S. Penn – 00:02 Talk about RAG—Retrieval augmented generation.

Christopher S. Penn – 00:06 What is it?

Christopher S. Penn – 00:07 Why do we care about it?

Christopher S. Penn – 00:09 So Katie, I know you’re going in kind of blind on this. What do you know about retrieval augmented generation?

Katie Robbert – 00:17 I knew we were going to be talking about this, but I purposely didn’t do any research because I wanted to see how much I thought I understood already just based on. So if I take apart just even the words Retrieval augmented generation, I think retrieval means it has…

Katie Robbert – 00:41 To go find something augmented, meaning it’s…

Katie Robbert – 00:44 Going to add on to something existing and then generation means it’s going to do something. So it’s going to find data added on to the whatever is existing, whatever that is, and then create something. So that’s my basic. But obviously, that doesn’t mean anything. So we have to put it in…

Katie Robbert – 01:05 The context of generative AI.

Katie Robbert – 01:07 So what am I missing?

Christopher S. Penn – 01:09 Believe it or not, you’re not missing a whole lot. That’s actually a good encapsulation. Happy Monday. Retrieval augmented generation is a system for bringing in contextual knowledge to a prompt so that generative AI can do a better job.

Probably one of the most well-known and easiest-to-use systems like this is Google’s free NotebookLM where you just put in a bunch of documents. It does all the work—the technical stuff of tokenization and embeddings and all that stuff. And then you can chat with your documents and say, ‘Well, what’s in this?’

In our examples, we’ve ]]> full https://www.trustinsights.ai/wp-content/uploads/2025/04/In-Ear-Insights-What-is-Retrieval-Augmented-Generation-RAG.png In-Ear Insights: What is Retrieval Augmented Generation (RAG)? false no 0:00 No no https://www.trustinsights.ai/blog/2025/04/in-ear-insights-ethics-of-ai-digital-clones-and-digital-twins/?pk_campaign=feed&pk_kwd=in-ear-insights-ethics-of-ai-digital-clones-and-digital-twins Wed, 02 Apr 2025 09:48:49 +0000 https://www.trustinsights.ai/?p=92301 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the ethics of AI and ethical dilemmas surrounding digital twins and AI clones. You’ll discover the crucial ethical questions surrounding digital twins and AI clones in today’s rapidly evolving digital world. You’ll learn why getting consent is not just good manners but a fundamental ethical necessity when it comes to using someone’s data to create a digital representation. You’ll understand the potential economic and reputational harm that can arise from unauthorized digital cloning, even if it’s technically legal. Tune in to learn how to navigate the complex ethical landscape of digital twins and ensure your AI practices are responsible and respectful.

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, we’re talking about digital twins or digital clones, people using generative AI tools to try and copy other people so that you can ask them questions. As an example, I might take all the blog posts or all the letters from the corner office that Katie’s written and put them into a generative AI tool like ChatGPT to create a clone of her and then say, hey, Katie, GPT, what do you think about my latest idea?

We’re not going to go into the technicals of this, of how you do this. Katie, you want to talk about more why or why not you should do this. And I’ll preface this with my absolute favorite clip from Jurassic Park.

Katie Robbert – 00:46 Yeah.

Christopher S. Penn – 00:47 But your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.

Katie Robbert – 00:52 That’s true. Jeff Goldblum, listen to the man. Smart guy. You said a couple of things that I think are interesting. You positioned this with a very specific use case of people are creating digital twins in order to ask them questions.

I think that’s a very narrow way of thinking about it because that assumes, oh, I don’t want to pay for Chris Penn’s time. If I create his digital twin, I can get all the consulting I need. I personally don’t think that’s how people are thinking about it. Hey, if I can clone a Chris Penn, I don’t have to pay him for contributed content. I can then say, Chris did this thing, or this is Chris’s voice or whatever it is, or probably more nefarious things]]> full https://www.trustinsights.ai/wp-content/uploads/2025/04/In-Ear-Insights-Ethics-of-AI-Digital-Clones-and-Digital-Twins.png In-Ear Insights: Ethics of AI Digital Clones and Digital Twins false no 0:00 No no https://www.trustinsights.ai/blog/2025/03/in-ear-insights-offsite-optimization-for-generative-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-offsite-optimization-for-generative-ai Wed, 26 Mar 2025 09:21:01 +0000 https://www.trustinsights.ai/?p=92284 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss offsite optimization for generative AI.

You’ll learn how to rethink your offsite SEO strategy to effectively engage with AI models. Discover how to identify the crucial data sources that AI uses to inform its knowledge. You will understand why traditional SEO metrics are becoming less relevant in the age of AI and what truly matters for offsite success. Prepare to revolutionize your PR approach and ensure your brand is recognized by the AI systems shaping the future. Watch now to gain the offsite AI optimization advantage.

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Christopher S. Penn – 00:00

In this week’s In Ear Insights, this is week three, part three of our optimizing your content and your marketing for AI. You know, people call it SEO for AI and a variety of tongue twisting acronyms. This week we’re talking about off site, which I feel like is probably the most accessible for folks because it’s the least technical and it is very familiar ground.

Off site AI optimization is essentially how can you be in as many places as possible as often as possible so that your name, your brand, and your content are duplicated as many places as AI crawlers grab their data from to build a training dataset library. So Katie, when we talk about going out into the world, what comes to mind for you for making that work and for being as many places as you possibly can be?

Katie Robbert – 00:58

You know, it’s funny because you just said something to the effect of it’s the most accessible or it’s the easiest. And I disagree because I feel like it’s the one that’s the least in your control. So, you know, when we talk about off site, we’re talking about getting on lists and guest posts and other people mentioning you.

And it’s not enough to just post about yourself on LinkedIn a couple of times a day. Sure, that’s part of it, but that it’s much more than that. And so, when I think about off site, I still think, okay, number one, I still have to have really good content, which is where we started this series that’s useful and appeals to my audience. So you can’t skip that step and suddenly just say, you know what?

Katie Robbert – 01:54

I’m gonna get on a bunch of who’s who lists or top 10 lists or whatever, because without that content as your fo]]> full https://www.trustinsights.ai/wp-content/uploads/2025/03/In-Ear-Insights-Offsite-Optimization-for-Generative-AI.png In-Ear Insights: Offsite Optimization for Generative AI false no 0:00 No no https://www.trustinsights.ai/blog/2025/03/in-ear-insights-ai-content-strategy-optimization/?pk_campaign=feed&pk_kwd=in-ear-insights-ai-content-strategy-optimization Wed, 19 Mar 2025 09:30:34 +0000 https://www.trustinsights.ai/?p=92263 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss optimizing your AI content strategy in the age of artificial intelligence. You’ll discover how to make your content appealing to both humans and AI algorithms, ensuring maximum reach and engagement. You will learn to balance data-driven AI optimization with the irreplaceable value of human creativity and unique brand voice. You’ll gain practical strategies to adapt your content creation process and stay ahead of the curve in the evolving digital landscape. Tune in now to learn how to future-proof your content strategy!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, we are continuing our look at how to optimize content for AI. Previously, we talked about what this is broadly, and on the last live stream we talked about content and the technical side. This week, on the live stream on Thursday, we’re going to be talking about what you should be doing with content. And Katie, I wanted to get your thoughts about the content itself—not the structure, not the technical stuff, and not what you’re doing to pitch it, but the content itself. When you think about optimizing content for artificial intelligence as opposed to traditional search engines, what do you think about it from the perspective, especially from the perspective of the user, the way people use AI versus the way people use traditional search engines?

Katie Robbert – 00:47 It’s tough because I personally feel like you should still be putting the human audience first. But it really depends on—it was going to say it depends on your goal. If you want the AI engines to pick it up, then prioritize that. But I can’t say that because yes, the AI engine is going to pick it up, but it’s still a human that is looking for it and consuming it. So you still have to prioritize the human in terms of who is the audience for this content. Now, I know that you have—we can get into the nuance of that—you’ve written press releases specifically for AI engines that are not meant for human.

Katie Robbert – 01:35 And that’s my understanding is those were done to literally just get the correct words into the engine so that if somebody typed in, “Tell me about Trust Insights,” that a knowledge block of some sort would come up and say, “This is what I know about Trust Insights.” That, to me, is a different kind of content than a video that’s a tutorial or a blog post. That’s an opinion. Those really should still be human first, AI second.

Christopher S. Penn – 02:13 One of the things that a lot of folks]]> https://www.trustinsights.ai/wp-content/uploads/2025/03/In-Ear-Insights-AI-Content-Strategy-Optimization.png In-Ear Insights: AI Content Strategy Optimization false no 0:00 No no https://www.trustinsights.ai/blog/2025/03/in-ear-insights-data-preparation-for-generative-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-data-preparation-for-generative-ai Wed, 12 Mar 2025 09:35:25 +0000 https://www.trustinsights.ai/?p=92250 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss data preparation for generative AI. You’ll learn why having high-quality data is the essential ingredient for getting valuable insights from AI tools. Discover how to ensure your data is clean, credible, and comprehensive, avoiding the pitfalls of ‘garbage in, garbage out’. Explore practical steps you can take to master data quality and make generative AI work effectively for you. Tune in to learn how to take control of your data and unlock the true potential of generative AI!

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, we’re talking data preparation for AI this week both on the Trust Insights live stream Thursday at 1pm Eastern Time. Remember, the USA if you’re a non-USA person, the USA has moved to summertime already, and I thought we’d talk today, Katie, about kind of why this is important. We’ll talk about the how on the live stream, but we’ll talk about the why and to degree the what. So before we begin, let me ask you what questions do you have about data preparation for generative AI?

Katie Robbert – 00:35 I don’t so much have questions because this is the kind of thing that I am specifically well versed in. Not so much the how, but the why. I did a panel last week at Worcester Polytech for the Women in Data Science, and this actually came up a lot. Surprisingly, the reason it came up a lot, specifically data governance and did good data quality, was there were a lot of questions around, what should I be thinking about in my degree? What should I be focusing on? If AI is just going to automate everything, where do I, a data scientist, where do I, a PhD candidate, fit in? A lot of the students there were academically focused rather than corporate field focused.

Katie Robbert – 01:29 I took the opportunity to talk about why data governance and good data quality is a foundational skill that regardless of the technology is going to be relevant. Having a good handle on what that actually means and why it’s important. If you’re unsure of where to focus, that’s a good place to start because it’s something that is always going to be in style, is always going to be on trend is good data quality. Because if you don’t have good data going into these pieces of software, and generative AI is just another piece of software, you’re going to have garbage coming out, and the outcomes are not going to be what you want them to do, and you’ll spend all of these times with these models and your random forest analysis and all of your other things, and nothing good i]]> https://www.trustinsights.ai/wp-content/uploads/2025/03/In-Ear-Insights-Data-Preparation-for-Generative-AI.png In-Ear Insights: Data Preparation for Generative AI false no 0:00 No no https://www.trustinsights.ai/blog/2025/03/in-ear-insights-generative-ai-optimization-for-marketing/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-optimization-for-marketing Wed, 05 Mar 2025 10:00:02 +0000 https://www.trustinsights.ai/?p=92237 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the rise of SEO for AI, also known as Generative AI Optimization. You’ll discover how generative AI is changing the rules of search and what it means for your content strategy. You’ll learn how to use AI tools to uncover hidden insights about your online presence and identify what needs optimization. You’ll understand why high-quality content is still the most important factor and how to adapt your SEO efforts for this new AI-driven era. Tune in to learn practical steps you can take now to optimize for generative AI and stay ahead of the curve!

Key Points and Takeaways:

• Generative AI Optimization (GAIO) is the new SEO: The episode discusses how traditional SEO is evolving with the rise of generative AI and introduces the concept of GAIO. This is a crucial shift for marketers to understand.
• Quality Content Still Reigns Supreme: Regardless of AI, high-quality, helpful, and relevant content remains the foundation of any successful optimization strategy. This reinforces fundamental marketing principles.
• AI as a ‘Secret Shopper’: Using AI tools to analyze your own online presence can reveal blind spots and areas for improvement, similar to a secret shopper evaluating customer experience. This offers a novel perspective on self-assessment.
• Data-Driven GAIO Prioritization: The episode stresses the importance of using data to understand where AI is sending traffic and prioritizing optimization efforts based on this data, ensuring efficient resource allocation.
• Intent Behind AI Search: Users engaging with generative AI search often have a higher intent to take action, making it a valuable channel to optimize for, even if it’s currently smaller than traditional search.
• Continuous Content Maintenance: GAIO is not a one-time task but requires ongoing content auditing, pruning, and updating to ensure relevance and accuracy in the age of AI.

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, we’re talking SEO for AI. Or as I said in my personal newsletter this week, there’s so many words for this—Generative AI Optimization, Generative Engine Optimization, AI Search Engine Optimization. Yeah, I said it. By the time you go through all the acronyms and stuff, it sounds like IKEA furniture names. Katie, when]]> https://www.trustinsights.ai/wp-content/uploads/2025/03/In-Ear-Insights-Generative-AI-Optimization-for-Marketing.png In-Ear Insights: Generative AI Optimization for Marketing false no 0:00 No no https://www.trustinsights.ai/blog/2025/02/in-ear-insights-what-are-ai-agents/?pk_campaign=feed&pk_kwd=in-ear-insights-what-are-ai-agents Wed, 26 Feb 2025 10:50:16 +0000 https://www.trustinsights.ai/?p=92196 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris answer the key question: What are AI agents? They discuss the surprising flaw in agentic AI that everyone is overlooking. You’ll unravel the mystery of what truly defines an AI agent and how it differs from simple automation. You’ll learn why blindly trusting AI agents can lead to unexpected problems and wasted resources in your business. You’ll discover a practical framework to determine when an AI agent is genuinely needed and when simpler solutions will deliver better results. Tune in to find out if agentic AI is right for you and your business!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, I wanted to talk today about the major flaw in agentic AI. Katie, you had some feedback for me?

Katie Robbert – 00:10 Yeah, no, I think that’s a great topic. Once we actually set the baseline of what the heck is an AI agent? I’m around the terminology all the time. I see the work that you’re doing, I see the work that, you know, our peers are doing. But if I was asked like on a panel or you know, just, you know, by someone in our community to describe an AI agent, I don’t know that I could confidently say what an AI agent is specifically as compared to anything else that we’re doing. Anything else, like a custom model or a chatbot or any. Anything else. And so I think my first question is what is an AI agent specifically? And how is it different from all of the other things that we’ve been doing with generative AI?

Christopher S. Penn – 01:04 This is a, a much more complicated question than it sounds. And the reason for that is because every vendor is trying to put their own spin on the term. And as a result you have like 28 conflicting drift definitions.

Katie Robbert – 01:19 Okay, so it’s not just me. That’s fantastic.

Christopher S. Penn – 01:22 It is not just you. And there’s a lot of people making a lot of noise and throwing a lot of confusing stuff in the air. And you’re like, will you all just settle down? You all need to calm down. Here’s the easiest definition that is completely unhelpful. An AI agent is an agent that uses AI. And.

Katie Robbert – 01:43 But yeah, that is completely unhelpful. The question is, what is an agent?

Christopher S. Penn – 01:50 That is the question.

Katie Robbert – 01:51 Okay?

Christopher S. Penn – 01:52 Agents have been around since, for the last three decades. If you’ve ever installed like Norton antivirus on a computer, you’ll see a little antivirus agent running in your processes list. And it is that agent is in the background doing its thing, scann]]> false no 0:00 No no https://www.trustinsights.ai/blog/2025/02/in-ear-insights-deconstructing-generative-ai-use-cases/?pk_campaign=feed&pk_kwd=in-ear-insights-deconstructing-generative-ai-use-cases Wed, 19 Feb 2025 10:05:06 +0000 https://www.trustinsights.ai/?p=92171 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss deconstructing generative AI use cases.

You will learn how to cut through the hype and understand how to truly use AI to solve real problems. You’ll discover a practical framework to break down complex AI initiatives into manageable steps. This episode will show you how to avoid common pitfalls and ensure your AI projects deliver measurable results. Watch this episode to gain actionable insights and start making AI work for you today!

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Here’s the edited transcript, adhering to all provided rules and guidelines:

Christopher S. Penn — 00:00

In this week’s In Ear Insights, let’s talk about deconstructing AI use cases. We recently had a lot of conversations. Last week’s live streams, we talked about skills matrices and understanding sort of the people that we have available to us and what their skills are when it comes to everything, but including generative AI. And Katie, you were talking recently with a colleague who gave some real time voice of the customer feedback about AI. Can you walk through what you heard?

Katie Robbert — 00:29

Yeah, absolutely. So a good friend of mine was at a conference and she was sharing with me some of the feedback that people were giving when it comes to implementing AI. And so basically what she was saying was sort of quote, “my boss says that we’re getting Copilot now. What? How do I prove the value of the tool and the value of the work that I do?” And what that says to me is, once again, people are choosing the platforms first and then trying to figure out what to do with it. That’s a reality that we knew was going to happen with a lot of organizations. When I say choosing the platform first, what I’m referring to is the 5P framework—purpose, people, process, platform, performance. The 5P’s were my reaction to digital transformation, which is people, process, technology, platform.

Katie Robbert — 01:26

They could have done better with their alliteration. The challenge with digital transformation, in my personal experience and a lot of people that I’ve talked to, is that it puts the technology, the platform, first and then tries to figure out where people and process fit in. And this is what we’re seeing play out at a lot of organizations is they’re saying, “great, generative AI is going to solve all of our problems. Let’s go ahead and pick a tool. Oh, we’re a Microsoft shop. So let’s get Copilot. Oh, we’re a Google shop. Let’s get Gemini. Oh, OpenAI, ChatGPT is the one I hear the mo]]> https://www.trustinsights.ai/wp-content/uploads/2025/02/In-Ear-Insights-Deconstructing-Generative-AI-Use-Cases.png In-Ear Insights: Deconstructing Generative AI Use Cases false no 0:00 No no https://www.trustinsights.ai/blog/2025/02/in-ear-insights-what-is-a-skills-matrix-assessment/?pk_campaign=feed&pk_kwd=in-ear-insights-what-is-a-skills-matrix-assessment Wed, 12 Feb 2025 09:02:11 +0000 https://www.trustinsights.ai/?p=92155 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the importance of a skills matrix assessment in today’s rapidly evolving work environment. You’ll understand what a skills matrix assessment is and why it’s essential for navigating today’s rapidly changing work environment. You’ll discover how to pinpoint exactly where your team’s skills excel and where they need development, particularly with AI on the rise. You’ll learn to strategically plan for the future, ensuring your organization remains competitive and adaptable to new technologies. You’ll explore how to break down complex skill needs into manageable parts for clear evaluation and growth. Tune in to discover how a skills matrix can transform your approach to talent and future-proof your organization!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, let’s talk about the skills matrix. What it is, or more importantly, why you would care about such a thing and how, in general, we think about skills evaluation in a world where the skills are changing so darn fast. This week on the Trust Insights live stream, we’ll be talking about how to do a skills matrix assessment. But Katie, walk through the basics. What, when we’re talking about evaluating people’s skills, what is it that we’re actually doing?

Katie Robbert – 00:34 We are looking at both hard and soft skills. So hard skills being things that you could write out in a process and follow from A to Z. So math, for example, is a hard skill because it’s something that multiple people can learn from a textbook, from an instructor, that kind of a thing. Programming and development is a hard skill. Project management is a hard skill. Visual design, believe it or not, is a hard skill. Soft skills are more of the human component. So management in terms of communication and empathy and those kinds of things. So, when we talk about a skills matrix, what we’re doing is we are mapping what the company needs to what the people have. And so it helps you see where the gaps are and where you might be perhaps over indexed.

Katie Robbert – 01:41 Because we tend to think about it in terms of let’s find the gaps, but we forget, oh, perhaps we have 10 developers on a project that needs only two. So a skills matrix, you call it a matrix, call it a spreadsheet, call it a checklist, whatever you want to call it, really is, what do we have, what do we need, and where’s the overlap? The reason you would want to put a skills matrix together is to figure out, do you have the right people doing the right things today? You know, ]]> false no 0:00 No no https://www.trustinsights.ai/blog/2025/02/in-ear-insights-generative-ai-reasoning-models-for-scenario-planning/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-reasoning-models-for-scenario-planning Wed, 05 Feb 2025 10:27:13 +0000 https://www.trustinsights.ai/?p=92132 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how reasoning models, a new type of AI, can revolutionize your scenario planning. You’ll discover how these advanced AI models can help you anticipate unforeseen challenges and opportunities for your business. Learn to move beyond reactive panic planning and create robust strategies for any future scenario. You will explore how to prepare your business knowledge for AI and scale your scenario planning efforts effectively across your organization. Tune in to learn how to leverage reasoning models to build a resilient and future-proof business today!

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, life is full of change and unpredictable, unforeseen circumstances of all kinds. And we’ve talked in the past, many times on this show about scenario planning, about coming up with what do we do if this happens, if this happens. However, a new family, a new genre of AI models has come out in the last two months really called reasoning models. First with OpenAI’s 01 and then with Deep Seek and its R1 and now OpenAI’s O3 and Google Gemini 2. Flash thinking. I hate their product naming and so many other models within this class which give us as marketers and as business professionals the ability to have a model that can really think things through. So Katie, when you hear about reasoning models and think about the need for good scenario planning, what comes to mind?

Katie Robbert – 01:05 Well, I guess the first thing is, admittedly, you’ve been talking about reasoning models since they hit the market. I don’t know that I really understand what a reasoning model is versus, you know, an existing open generative AI model. I don’t even know the terminology to be quite honest. When I hear “reason model,” it’s like, “oh, is it sentient now?” What does that mean?

Christopher S. Penn – 01:36 That’s a really good question. There’s three techniques in prompt engineering that we’ve talked about and which you can learn about in our prompt engineering course, Mastering Prompt Engineering at Trust Insights. Those three techniques are called chain of thought, reward functions, and reflection. And when we would do this by hand in the old days of AI three months ago, we would say things in a prompt. Our prompts would say, “think this through step by step. Show me your work, show me this, show me this. Explain this” in our prompts to say, “I want you to explain this.” So for example, I was ]]> https://www.trustinsights.ai/wp-content/uploads/2025/02/In-Ear-Insights-Generative-AI-Reasoning-Models-For-Scenario-Planning.png In-Ear Insights: Generative AI Reasoning Models For Scenario Planning false no 0:00 No no https://www.trustinsights.ai/blog/2025/01/in-ear-insights-ai-strategy-for-how-to-keep-up-with-ai-technology-changes/?pk_campaign=feed&pk_kwd=in-ear-insights-ai-strategy-for-how-to-keep-up-with-ai-technology-changes Wed, 29 Jan 2025 09:37:42 +0000 https://www.trustinsights.ai/?p=92112 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss AI strategy and how to stay sane amidst the whirlwind of constant AI advancements. You’ll discover practical strategies to navigate the overwhelming influx of new AI models and technologies. You’ll learn how to prioritize your business needs and focus your AI efforts for maximum impact. You’ll understand how to use frameworks like the 5Ps to make informed decisions and avoid getting lost in the AI noise. Tune in to learn how to manage AI chaos and keep your sanity!

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Christopher S. Penn — 00:00 In this week’s In-Ear Insights, there is so much happening in the world of AI right now. So, Google has its new reasoning model, Gemini Flash Thinking too. And then in the last week or so, different Chinese companies like ByteDance, the owners of TikTok, released their new models, Dubao. Deep Seek has V3 which came out in December, and R1, which came out last week, which is their reasoning model, which is the same performance as OpenAI’s model, trained at 1/100 of the cost, and for the average user who’s using it in an app or something, is 96% less expensive than OpenAI’s APIs. And then, of course, overnight, yet another new model, a different one called Kimi from China, also came out, which also promises state-of-the-art performance at an extremely low cost.

Christopher S. Penn — 00:53 This can lead to some level of frustration and confusion among users. Over the weekend, we had clients messaging, saying, “What do we do this?” So Katie, when we think about… Because one of your themes for this year is foundation. When we think about everything that is happening and the speed at which is happening in AI, how, if you are, how are you staying sane and keeping sane in the world of ridiculously fast-paced change?

Katie Robbert — 01:26 I mean, that would assume that I was sane in the first place. I mean, come on, we all saw that you set me up for that joke. It’s funny because everything you just said, I have no way of knowing if everything you just said is true or if you just made up a bunch of words and I’m like, “Oh, these are all new things.” And this is where finding those trustworthy sources, those experts, is going to be your best friend. So obviously, Chris, I have you, and as far as I’m aware, it is not in your best interest to make things up and try to convince me of something that isn’t actually t]]> full https://www.trustinsights.ai/wp-content/uploads/2025/01/In-Ear-Insights-AI-Strategy-for-How-To-Keep-up-With-AI-Technology-Changes.png In-Ear Insights: AI Strategy for How To Keep up With AI Technology Changes false no 0:00 No no https://www.trustinsights.ai/blog/2025/01/in-ear-insights-tiktok-marketing-social-media-strategy-and-diversification/?pk_campaign=feed&pk_kwd=in-ear-insights-tiktok-marketing-social-media-strategy-and-diversification Wed, 22 Jan 2025 10:10:26 +0000 https://www.trustinsights.ai/?p=92102 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss Tiktok marketing and social media strategy diversification after the recent TikTok incident. You’ll learn how to create content that thrives regardless of platform changes. You’ll discover strategies to build a direct, trusting connection with your audience, reducing reliance on third-party platforms. You’ll explore how to use generative AI tools to enhance content creation and distribution. You’ll gain insights into building a resilient marketing strategy that leverages both owned and rented channels. Watch this episode to build a future-proof marketing plan that isn’t at the mercy of social media giants.

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Christopher S. Penn – 00:00

In this week’s In-Ear Insights. It was there, then it was gone, now it’s back, it’s TikTok. Of course, there are so many things to talk about on this particular topic, but—and I was saying this to a friend of mine—I said, I have so much to say, but none of it is productive, none of it is helpful, none of it is going to advance the company or my personal brand and stuff. So I’m just going to sit on my hands at Nintendo, tell it to AI. I have an AI chatbot of my own that I just grouse at constantly. It’s like, I understand you’re upset, but it keeps me from saying things in public that are stupid. So. So, Katie, I’m sure you have things that you want to say in public that are not stupid.

Katie Robbert – 00:46

Well, I don’t know if they’re not stupid, but I’ll say them anyway. I think it’s fairly well known that I’m not a heavy social media user in my personal life. And so obviously being in this industry and being at this particular company, I’m well aware of what different social media platforms do, who their audiences are and the utility of them. And so, for weeks that there’s speculation that TikTok was going to be banned in the U.S. I didn’t actually see a lot of people doing anything about it because I don’t think anyone really felt like it was going to happen. So they were like, oh, well, they’re just bluffing.

Katie Robbert – 01:31

And then, lo and behold, I think it was, as we’re recording this, it was just yesterday morning, people, when they logged into or tried to log into TikTok, got various forms of messages. To be honest, mine just said no Internet connection, so I didn’t even get a fun message. But regardless, people started to panic]]> https://www.trustinsights.ai/wp-content/uploads/2025/01/In-Ear-Insights-Tiktok-Marketing-Social-Media-Strategy-and-Diversification.png In-Ear Insights: Tiktok Marketing, Social Media Strategy, and Diversification false no 0:00 No no https://www.trustinsights.ai/blog/2024/12/in-ear-insights-trust-insights-2024-year-in-review/?pk_campaign=feed&pk_kwd=in-ear-insights-trust-insights-2024-year-in-review Wed, 18 Dec 2024 08:36:11 +0000 https://www.trustinsights.ai/?p=92043 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the challenges of 2024 and how they learned to thrive. You will discover how Trust Insights adapted to the rapid changes in AI and found success with their R&D process. Learn why focusing on foundational elements such as people and process is the key to navigating the ever-changing tech landscape. You will also hear how a focus on creating robust customer profiles allows them to develop custom AI solutions.

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, we are winding down. This is the last In-Ear Insights episode of 2024. After this, we will be taking a well-earned holiday break, and I hope you are, too. In this episode, we thought we’d talk about the year in review, specifically for Trust Insights, taking a look back at the year—all the things that happened to the company specifically, for the general. We’re going to talk about that on this week’s live stream. So, if you’re not following along over at Trust Insights AI, YouTube, you can catch that Thursday at 1pm Eastern time. So, Katie, as our fearless leader, how was 2024?

Katie Robbert – 00:41 Oh, well, I have a lot more white hair now. I will say that, it’s when I look back, it feels like both the shortest and longest year that we’ve experienced so far. January seems so far away right now, but at the same time, I’m like, how did it already become December? And in the context of the business, that was very true. Where we started in January is not how we ended in December. So, the beginning of this year for Trust Insights, and I know for a lot of businesses, was tough. It was tough because generative AI had become very mainstream and people were trying to figure out what the heck that meant for their business, so people pulled back budgets.

Katie Robbert – 01:30 It was also an election year, which we also know people tend to get a little bit more conservative and just sort of wait and see. So there were a lot of factors at play at the start of 2024 that really made it challenging for businesses to figure out what was going to happen, especially small businesses like ours that don’t have external funding and investors. Yes, we have backup plans, our finances are fine, all that good stuff. But our business model is we get new clients, that’s our revenue. That’s a pretty standard business model for a lot of people. Ours is no different. And the first quarter of 2024 was admittedly very scary because things were just not moving.

Katie Robbert – 02:20 People were responding, but non-committal, and it made it really hard to ]]> full https://www.trustinsights.ai/wp-content/uploads/2024/12/In-Ear-Insights-Trust-Insights-2024-Year-In-Review.png In-Ear Insights: Trust Insights 2024 Year In Review false no 0:00 No no https://www.trustinsights.ai/blog/2024/12/in-ear-insights-rapid-scenario-planning-with-generative-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-rapid-scenario-planning-with-generative-ai Wed, 11 Dec 2024 09:36:31 +0000 https://www.trustinsights.ai/?p=92020 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss rapid scenario planning for your business with the help of generative AI. You discover methods for using generative AI to plan for multiple potential futures, equipping you to handle various situations. You learn to identify constants in your business, like revenue goals, and how these can guide your planning process. You gain insights into leveraging AI tools for rapid scenario planning, helping you react swiftly to changes. You explore practical steps to prepare for uncertainties, ensuring you stay agile and proactive in a dynamic environment.

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Christopher S. Penn 00:00 In this week’s In-Ear Insights, I got a question recently from a community member on LinkedIn who was asking what my forecast and trends and predictions for 2025 were. In particular, they were asking about the retail sector. As I was crafting my answer, Katie, you were in my head the entire time saying, “Well, there’s not just one forecast, there are many.” Because you have to think about, “What’s the worst-case scenario? What’s the best-case scenario? What if nothing changes?” As I was thinking this through, I was realizing that there is so much uncertainty right now that even understanding what the worst-case scenario is really challenging.

Christopher S. Penn 00:43 So, I wanted to revisit scenario planning and forecasting, and sort of a look back and then a look ahead. I’m not going to make this a politics show, but we know that there are big changes coming in nations around the planet. France is having a change of government right now. Syria just had a change of government. The United States is having a change of government. So, it’s not just one place, it’s all over. The changes themselves are becoming so rapid. Just in the AI and analytics space alone, we have new frontier and foundation models coming out. It feels like every day over the weekend, LG, the Korean appliance maker, released their brand new state-of-the-art model.

Christopher S. Penn 01:26 I didn’t know the refrigerator company made those that are competitive with Alibaba’s Quinn models. So, all of this to say, how do you do scenario planning when you can’t even figure out what the scenarios are that you’re planning for? You say, “Yeah, worst-case scenario, is global thermonuclear war an option? Is a new pandemic an option?” It certainly seems that way. How do you do this?

false no 0:00 No no https://www.trustinsights.ai/blog/2024/12/in-ear-insights-digital-ghosts-ai-ethics-and-employment-agreements/?pk_campaign=feed&pk_kwd=in-ear-insights-digital-ghosts-ai-ethics-and-employment-agreements Wed, 04 Dec 2024 10:49:49 +0000 https://www.trustinsights.ai/?p=91992 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss your digital ghost. You will learn whether employers can put your digital ghost to work, the legal and ethical implications of employers using your digital assets, how to safeguard your digital likeness, and what you need to negotiate with your employer.

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, let’s talk about your digital ghost. This is a topic that I posted about on LinkedIn about a week ago. In the old days, we would go to a company—Katie, you and I worked together at an old agency—and we would do stuff. We’d write, create, send emails, go to meetings, go to more meetings, and then have meetings about the meetings and the pre-meeting for the meeting. And when we left the job, we were gone. The IT department would archive all of our stuff on a backup tape somewhere, maybe, and we were forgotten. Maybe a few coworkers remembered us fondly at the holiday potluck, but that was about it in the era of generative AI.

Christopher S. Penn – 00:50 However, it raises an interesting question: could your digital ghost—that record of everything that you said and did—could it be put back to work by an employer? Could they say, “Okay, I’m going to take all of Katie Robbert’s emails and conference call transcripts and work product and build a custom app powered by generative AI that can do what she did while she was here”? Now, today’s models and technologies can’t do that as well, and it needs a lot of supervision. But we are rapidly on a path towards that being a very straightforward reality.

Christopher S. Penn – 01:29 So the question I have for you, Katie, is—well, I have a bunch of questions for you—but we’ll start with what are the implications of this, particularly for, in your case, a CEO, an owner of a company? And then what does it mean for the workforce that we employ?

Katie Robbert – 01:47 Well, it really comes down to what you, as an individual, agreed to. One of the things that we wanted to talk about in today’s podcast was the ethics of it, specifically. The short answer is yes, it can be done. The longer, more complicated answer is it really depends on what’s in your contract and how much of the IP that you create belongs to the company. That’s going to become increasingly more challenging for people moving forward as they start to figure out what IP really covers. So in my case, that’s anything I created for the company prior to this based on the employment contract that I signed. That]]> full https://www.trustinsights.ai/wp-content/uploads/2024/12/In-Ear-Insights-Digital-Ghosts-AI-Ethics-and-Employment-Agreements.png In-Ear Insights: Digital Ghosts, AI Ethics, and Employment Agreements false no 0:00 No no https://www.trustinsights.ai/blog/2024/11/in-ear-insights-marketing-lessons-from-marketingprofs-b2b-forum/?pk_campaign=feed&pk_kwd=in-ear-insights-marketing-lessons-from-marketingprofs-b2b-forum Wed, 20 Nov 2024 09:29:12 +0000 https://www.trustinsights.ai/?p=91976 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss their key takeaways from the MarketingProfs B2B Forum 2024. You will learn about the surprising reality of AI adoption in marketing. You will discover the challenges of accurately measuring AI usage and the inherent biases in current surveys. You will gain insights into the human element of marketing and how it intersects with AI. You will understand the value of listening in market research and how to extract insights from industry events.

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, we are back from MarketingProfs B2B Forum, and I believe that is our last professional public event for 2024. The calendar is already booking for 2025 and is quite full, which is great. But we wanted to take a few minutes today to talk through some of our takeaways from MarketingProfs B2B Forum because it’s one of our favorite events of the year. The food is always excellent, as Ann Handley likes to point out, as one of the few conferences that feeds you with things that will not make you immediately ill. So, Katie, let’s start with you. You and I did the workshop together. You also had your session on AI readiness, and you went to a bunch of other sessions. We all did. What were your major takeaways from the event?

Katie Robbert – 00:49 The major takeaways were that despite all of the conversation about generative AI, marketers are not as far along with using it as I think they’re being led to believe. That feeling of “I’m falling behind” or “everybody’s doing it” is just not true. I think a lot of companies are still trying to figure out where AI fits. I think they’ve tested a lot of things. I think there’s been a lot of experimentation, but the sense that I was getting was that there wasn’t a lot of “okay, this is how we can use it. This is exactly what it means for us.” I think a lot of marketers are still in the experimentation, exploratory phase of using generative AI. Now, that said, a lot of software that they’re currently using just went ahead and baked it in. So there’s really no “oh, and now I’m using AI and this is what it looks like and this is how it’s different.” It’s “what do you mean? I’m doing what I was always doing and now it’s AI-powered.” So I think there was a lot of eye-opening of what it actually means to be using AI for marketers.

Christopher S. Penn – 02:08 Yeah, it was amazing. In the first few minutes of our wor]]> https://www.trustinsights.ai/wp-content/uploads/2024/11/In-Ear-Insights-Marketing-Lessons-From-MarketingProfs-B2B-Forum.png In-Ear Insights: Marketing Lessons From MarketingProfs B2B Forum 2024 false no 0:00 No no https://www.trustinsights.ai/blog/2024/11/in-ear-insights-ai-readiness-at-marketingprofs-b2b-forum/?pk_campaign=feed&pk_kwd=in-ear-insights-ai-readiness-at-marketingprofs-b2b-forum Wed, 13 Nov 2024 11:37:06 +0000 https://www.trustinsights.ai/?p=91960 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the crucial topic of AI readiness for businesses. Discover the essential steps to determine if your organization is truly prepared to leverage AI effectively. Learn how to avoid common pitfalls and misconceptions surrounding AI implementation. Gain valuable insights into maximizing your AI investment and driving tangible results, rather than just chasing innovation awards. Prepare to rethink your approach to AI and unlock its true potential for your business.

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— START OF FILE E2430563TFZVenkB_tipodcast-building-ai-readiness-mpb2b-mp3-st.txt —

Christopher S. Penn – 00:00 In this week’s In Ear Insights, we’re here at the MarketingProfs B2B forum. Katie, you’re giving a talk on managing the people who manage the machines—a very important topic.

Katie Robbert – 00:13 Yes, it’s about assessing your organization’s AI readiness. This very much includes managing the people who manage the machines, because you need to determine if you have the right people and processes in place. While building this talk, I realized I needed to emphasize that it’s not an anti-AI talk. If you want to experiment, go ahead—do those proofs of concept—but don’t use your entire organization as a proof of concept. It’s about helping people understand which path to take.

Katie Robbert – 00:54 If you’re looking to bring generative AI into your organization to find efficiencies and change how people work, you need to ensure your people are on board. But if you just want to experiment, that’s fine. You don’t need a whole assessment. I constantly run into people asking if they can or can’t use AI. The answer is, you can use it. You can even use AI to do these assessments. However, when you’re asking your teams as a whole to change their work processes and adopt new skill sets, it’s a different story.

Katie Robbert – 01:32 I often think back to when you first introduced me to generative AI. My reaction was, “You want me to *what* now?” It wasn’t that I was unwilling, but I’m not a technologist, so it requires a different skill set. I had to figure out how to make time in my day to learn, adopt, and use it consistently, so it becomes more than a one-off. What have you seen in terms of organizations struggling to use AI?

Christopher S. Penn – 02:07 People are quickly running into scaling problems. Everyone is still using AI individually. They open ChatGPT on their laptop, do their thing, and then copy and paste 18 times. Recent news articles have highlighted how this]]> https://www.trustinsights.ai/wp-content/uploads/2024/11/In-Ear-Insights-AI-Readiness-at-MarketingProfs-B2B-Forum.png In-Ear Insights: AI Readiness at MarketingProfs B2B Forum false no 0:00 No no https://www.trustinsights.ai/blog/2024/11/in-ear-insights-marketing-strategic-planning-for-macro-trends-in-uncertain-times/?pk_campaign=feed&pk_kwd=in-ear-insights-marketing-strategic-planning-for-macro-trends-in-uncertain-times Wed, 06 Nov 2024 10:28:49 +0000 https://www.trustinsights.ai/?p=91954 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss navigating the unpredictable world of marketing strategic planning in uncertain times. You’ll discover valuable strategies for planning and adapting amidst unreliable data and ever-changing trends. Learn how to diversify your revenue streams and control what you can, even when external factors shift. Explore the importance of balancing daydreaming with practicality for long-term business success.

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, no one’s going to listen to this episode. That’s—we’re just going to say that this is dropping Wednesday morning, the day after a US Presidential election. So, I want to start off by saying no matter what happens, this is part of a larger macro trend. That macro trend is increasingly unpredictable events—from climate disasters to crazy elections to AI eating everything. There is no shortage of things that are making life less predictable. On top of that, a conversation we had recently in Analytics for Marketers was, we also have less reliable marketing data. We all know Google Analytics Four is kind of a hot mess internally. We know that privacy restrictions, especially on mobile devices, are increasing to the point where 30, 40, 50% of your traffic may be unknown.

Christopher S. Penn – 00:49 We know that people are using search in very different ways. Recently, ChatGPT released this new search function which allows people to conduct searches from within it, and we have no visibility inside that as a tool. So, if it does take off, that will be a big thorn in our side. So, with this landscape of increasingly unpredictable events and less reliable data, Katie, we’re talking—it’s close to end of year. There’s like five work days left.

Katie Robbert – 01:17 There’s a few more than that, Chris, so don’t check out on us just yet.

Christopher S. Penn – 01:22 Exactly. How are you thinking as a leader in this space? How are you thinking about planning and strategy for an increasingly unstable, unpredictable environment?

Katie Robbert – 01:39 Well, what I was about to say, I realized before I even said it was going to be incorrect. I was going to say the days of having just one plan are long over, but they’ve been long over. You can’t just have one version of a plan. This is where, as you’re asking me what we’re going to do six, 12, 18 months from now, I may not know the exact details, but I]]> full false no 0:00 No no https://www.trustinsights.ai/blog/2024/10/in-ear-insights-the-trust-insights-rappel-ai-prompt-framework-explained/?pk_campaign=feed&pk_kwd=in-ear-insights-the-trust-insights-rappel-ai-prompt-framework-explained Wed, 30 Oct 2024 08:36:15 +0000 https://www.trustinsights.ai/?p=91939 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the new RAPPEL AI prompt framework for AI prompting, designed to simplify and improve your interactions with AI. Discover why priming the model is crucial for accurate results and how this framework helps you avoid common pitfalls. Learn how RAPPEL streamlines the process of creating effective prompts and how to use it to scale your use of AI. Unlock the secret to repeatable AI success by learning from each interaction and building better prompts for future use.

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, the RACE is over—as I was jokingly saying last week with our change in our AI framework. So, previously we had two frameworks: the RACE framework and the PARE framework. We have since upgraded and changed this to what we now call the RAPPEL framework. So Katie, before we dive into what the thing is, you’ve had a chance to at least look at it. What’s your first impression of this? Is it a good idea to throw out something that’s been working for people for two years now?

Katie Robbert – 00:37 That’s a loaded question. I was actually thinking about this over the weekend in terms of frameworks like SWOT, for example. Strengths, weaknesses, opportunities, threats. There’s a reason that framework has stayed tried and true for so long and unchanged because it works. It’s easy to understand. Now, when it comes to AI, it’s changing so rapidly that I think you do have to adapt the frameworks. I think we’re talking about two things. If I use the example of SWOT, you’re talking at that high-level overview versus talking about frameworks like RACE and PARE, and now RAPPEL—you’re talking more tactical and on the ground.

I feel like those frameworks that are more on the ground, that get into the weeds, are the ones that have to continue to adapt. Now, should we continue to change the actual instructions of the framework over and over again? At some point, we should probably say this is going to work regardless of how AI evolves. But I think because AI is still, for all intents and purposes, in its infancy in the consumer space, continuing to adapt as we learn more is not a bad idea. That said, we’re at six steps for a framework. If we start to get beyond that, it’s when I think it’s going to be harder and harder for people to continue to follow along and adopt this framework.

The purpose of a framework is to keep things very consistent, easy, and not difficult to remember the steps. So, I think you’re at that tipping po]]> https://www.trustinsights.ai/wp-content/uploads/2024/10/In-Ear-Insights-The-Trust-Insights-Rappel-AI-Prompt-Framework-Explained.png In-Ear Insights: The Trust Insights RAPPEL AI Prompt Framework Explained false no 0:00 No no https://www.trustinsights.ai/blog/2024/10/in-ear-insights-data-protection-ai-and-privacy/?pk_campaign=feed&pk_kwd=in-ear-insights-data-protection-ai-and-privacy Wed, 23 Oct 2024 08:36:00 +0000 https://www.trustinsights.ai/?p=91919 In this week’s In-Ear Insights, the TrustInsights.ai podcast, Katie and Chris discuss data protection, AI and privacy. How do you protect your data and client/customer data when using AI tools like ChatGPT? Learn the two major protection classes and three levels of data protection in this episode. Tune in to find out more!

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, we’re answering a question from our free Slack group, Analytics for Marketers, which you can find at TrustInsights.ai. Lauren asks, “How are you keeping client data safe inside generative AI tools like ChatGPT?” There’s a whole range of options and ways of doing this, and we can talk through the entire structure. Katie, just off the top of your head, when you think about this—because this is really a data governance question—what should people be asking?

Katie Robbert – 00:34 Well, that’s exactly it. Choosing the tool first is the wrong way to approach it because you first need to know what, when you say “keep my data safe,” what does that mean? That’s pretty vague. Am I protecting it from the sun and I need to put sunblock on it? In reality, we’re talking about data governance and data privacy. First and foremost, you need to define what data you’re collecting that needs to be protected, and then what protection actually means. Are you anonymizing it and then using it in publicly available tools, or do you need to keep it completely firewalled from the rest of the internet?

Katie Robbert – 01:26 You need to have some sort of an intranet versus an internet. These are the kinds of things you need to define first. Once you know what data protection and data privacy actually mean for your client data and your company, then you can go ahead and choose whatever generative AI tool. Now, in reality—because I’m also a realist—I know you’ve already chosen a tool. I know you’re already using something. I know you’ve already put your client data in it, and you’re like, “Huh, I wonder if I should be doing this. I wonder if I read the terms when I agreed to something, I agreed to the wrong thing.” So I would probably start there.

Katie Robbert – 02:17 I would go back through the terms you agreed to, see how the data is being used, and see if you need to change the settings in the tool you’ve been putting all of your client data into.

Christopher S. Penn – 02:31 There are two major considerations with generative AI tools: training data and human review. Training data means, hey, when you use pretty much any free tool out there, your data is the price of admission. The company c]]> full https://www.trustinsights.ai/wp-content/uploads/2024/10/In-Ear-Insights-Data-Protection-AI-and-Privacy.png In-Ear Insights: Data Protection, AI and Privacy false no 0:00 No no https://www.trustinsights.ai/blog/2024/10/in-ear-insights-generative-ai-for-conference-attendees/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-for-conference-attendees Wed, 16 Oct 2024 08:28:48 +0000 https://www.trustinsights.ai/?p=91904 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss using generative AI for conference attendees. Using the MarketingProfs B2B Forum 2024 (#MPB2B), you will learn how to leverage AI to define your goals, create a detailed plan to achieve them, and optimize your conference experience. You’ll discover how to use AI to select the most relevant sessions based on your professional goals and your ideal customer profile. Finally, you’ll gain practical strategies for improving your networking skills and making meaningful connections with other attendees.

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, we’re in the thick of conference season—which, of course, goes on from basically September to Thanksgiving. We’re at all these different events. The next big one for Trust Insights is the MarketingProfs B2B Forum. Katie, I have a question for you, and I know what you’re going to say. You’re going to say immediately that the question is wrong because I’m leading with the technology—which is intentional, but I know it’s wrong. How, then, why am I even here? Because I want to hear your answer to this question: How would you use generative AI—and again, we’re leading with the technology, which is canonically wrong—to prepare for attending an event like the MarketingProfs B2B Forum?

Christopher S. Penn – 00:48 What are the different ways you could use it to make the most of the event as an attendee?

Katie Robbert – 00:56 I think it depends on your goals as the attendee. If you don’t know what your goals are, perhaps that’s where you start. So, unsurprisingly, I would say start with the 5P Framework—Purpose, People, Process, Platform, and Performance. If you’re attending a conference like MarketingProfs B2B—which happens to be in Boston this year, which is fantastic for you and me, Chris, because the travel is very light—start with what you want to get out of attending a conference. If your goal is networking, or if your goal is professional development, or if your goal is to understand the latest and greatest in B2B marketing, those are great places to start and put those into your purpose.

Katie Robbert – 01:47 So, if you start the 5P Framework but you’re not quite sure where to go from there, I think that’s where I would use generative AI. I would say, “Here’s a framework I want you to help me explore. I know what my purpose is, but I’m not really sure on the rest.” Then, you can iterate with generative AI to say, “The five P’s are Purpose,]]> full https://www.trustinsights.ai/wp-content/uploads/2024/10/In-Ear-Insights-Generative-AI-for-Conference-Attendees.png In-Ear Insights: Generative AI for Conference Attendees false no 0:00 No no https://www.trustinsights.ai/blog/2024/10/in-ear-insights-generative-ai-for-strategic-planning/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-for-strategic-planning Wed, 09 Oct 2024 09:24:40 +0000 https://www.trustinsights.ai/?p=91889 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss using generative AI for strategic planning. You will learn how to leverage AI to translate data into actionable insights, overcoming the common struggle of understanding what to *do* with your marketing data. You’ll discover how to use AI to identify the “why” behind your marketing results, going beyond simple “what’s working/what’s not” analyses. Finally, you will explore how to utilize AI even if you lack readily available data, uncovering creative ways to gather the information you need for effective strategic planning.

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Christopher S. Penn – 00:00 In this week’s in ear insights, it is now firmly Q4 we are, for many folks, either winding down the remainder of the year, trying to squeeze those last deals in because there’s something like 52 working days left in the year. Or if you’re in B2C, you’re about to get into your crazy season where you basically work 24 hours a day for the remainder of the year. Either way, there’s a lot happening at this time of year, and one of the things that people tend to do at the end of the year, regardless of B2B or B2C, is try to do planning and strategy and budgeting and get ready for the year ahead. I realize it’s not even Halloween yet now we’re already talking about the year ahead.

Christopher S. Penn – 00:36 However, from a planning perspective, this is definitely when planning season really kicks into high gear. So Katie, I want to ask you, as someone who is a avid planner, how are you thinking about using generative AI tools for planning season?

Katie Robbert – 01:01 I am planning the plan, no, so what I’m doing with generative AI is one of the I said, I think I said this to you after a live stream, either last week or a couple of weeks ago and we had done something. Of course it’s very important because I can clearly remember what it was. I do remember that we had done some kind of analysis that I remember I said to you and John, this is much more actionable and useful than just looking at a spreadsheet or a dashboard of Google Analytics data and how many people came to our website. I understand what the data means, but I’ve always struggled to figure out what the heck to do with it.

Katie Robbert – 01:52 I personally am going to be taking those usual analyses and dashboards that we typically look at and actually use generative AI to help me figure out the so what. And so I will be using our ideal customer profile, which we use generative AI to build, using that as the foundation and say, this is our ]]> https://www.trustinsights.ai/wp-content/uploads/2024/10/In-Ear-Insights-Generative-AI-for-Strategic-Planning.png In-Ear Insights: Generative AI for Strategic Planning false no 0:00 No no https://www.trustinsights.ai/blog/2024/10/in-ear-insights-gender-representation-event-marketing-and-speaker-selection/?pk_campaign=feed&pk_kwd=in-ear-insights-gender-representation-event-marketing-and-speaker-selection Wed, 02 Oct 2024 08:19:06 +0000 https://www.trustinsights.ai/?p=91870 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris tackle the important topic of speaker selection at events, specifically focusing on inclusivity and representation. Discover the challenges and considerations when selecting speakers for events, particularly when catering to a specific audience, like women in a particular industry. Learn valuable strategies for creating a more inclusive and diverse speaker lineup at your events, ensuring all voices are heard and represented. Gain insights from Katie and Chris’s open and honest conversation about their experiences and perspectives on this crucial issue, and find inspiration for positive change in your event planning.

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, let’s talk about something different for a change and talk about event marketing, conferences, and speaker selection. So recently, I had the opportunity and the privilege to speak about generative AI in the architecture, engineering, construction industry to an event that I didn’t know that I was probably the best fit for, which was the Women in Construction Operations organization—a great organization based in California that is all about supporting and advancing women in the AEC industry. Katie, I know that when this all came through, you had some questions.

Katie Robbert – 00:36 I sure did. I sure did. Well, let me sort of start with the disclaimer that I completely respect your expertise, and I totally understand why events seek you out to be the speaker. And so I just want to sort of make that disclaimer in case anybody thinks that I’m not saying that you’re qualified.

In this example, where it was a women’s event on the subject matter, you are 100% qualified. My question for you is, when they came and said, “Hey, we want you to speak,” because I remember they sought you out specifically and they said, “We’re a women’s organization,” what went through your mind? Did it even occur to you that perhaps you were not the right person?

Christopher S. Penn – 01:26 It did. And we actually had this discussion in our company Slack, which was, “Don’t you need to be a woman to speak there?” Because there’s a parallel organization that you work with, Women in Analytics, where their annual conference essentially has that: if you are a person who identifies as either non-binary or women, you are eligible to speak. If you identify as something else, you are not. This organization did not have th]]> full https://www.trustinsights.ai/wp-content/uploads/2024/10/In-Ear-Insights-Gender-Representation-Event-Marketing-and-Speaker-Selection.png In-Ear Insights: Gender Representation, Event Marketing, and Speaker Selection false no 0:00 No no https://www.trustinsights.ai/blog/2024/09/in-ear-insights-responsible-ai-part-4-implementing-responsible-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-responsible-ai-part-4-implementing-responsible-ai Wed, 25 Sep 2024 09:22:40 +0000 https://www.trustinsights.ai/?p=91857 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss implementing Responsible AI in your business. Learn how to align AI with your company values, establish accountability, ensure fairness in AI outputs, and maintain transparency in your AI practices. By understanding these elements, you can unlock the true potential of AI while avoiding potential pitfalls. Gain valuable insights to navigate the complex world of AI implementation and build a framework for responsible AI usage in your organization.

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, we are finishing up our responsible AI series. This is part four. In part one, we talked about what responsible AI is—talking about the difference between ethics and morals, and how you would decide what your ethical principles for AI use are, even things like intellectual property and licensing. In part two, we talked about bias, the different types of bias, the way that models are biased, and what you should be doing to counteract that. In part three, we talked about data privacy, how to keep your data private, and the considerations you need to be thinking about when you’re working with Generative AI. And so, in this last part, let’s talk about the implementation of responsible AI.

Christopher S. Penn – 00:40 And to absolutely no one’s surprise, the implementation of responsible AI follows the 5P process, which is purpose, people, process, platform, performance, which you can get a copy of at TrustInsights.ai 5PFramework. So Katie, when we think about implementing the responsible AI framework—which, by the way, you can get a copy of at TrustInsights.ai RAFT—where do we start helping people understand and make the concept of generative responsibilities of AI? How do we turn that into reality?

Katie Robbert – 01:18 I mean, you have to start at the top of the 5Ps and really have a clear goal in mind of why are we doing this in the first place. So, a couple of weeks ago, we spoke at MAICON, and I spoke specifically about managing the people who manage AI. And when I talked about AI integration—I didn’t use the word responsible AI, but it was, if you saw the talk, it was very heavily implied, because it’s all about focusing on the people and not the technology itself.

There’s a lot of groundwork you need to do before you bring AI into your organization as a whole, especially if you want to do it responsibly and ethically. And so, you need to make sure that you understand, number one, what it is and why you’re doing it. That’s your purpose. But then you need to get into the people, and really ]]> https://www.trustinsights.ai/wp-content/uploads/2024/09/In-Ear-Insights-Responsible-AI-Part-4-Implementing-Responsible-AI.png In-Ear Insights: Responsible AI Part 4: Implementing Responsible AI false no 0:00 No no https://www.trustinsights.ai/blog/2024/09/in-ear-insights-maicon-2024-key-takeaways/?pk_campaign=feed&pk_kwd=in-ear-insights-maicon-2024-key-takeaways Wed, 18 Sep 2024 08:29:18 +0000 https://www.trustinsights.ai/?p=91828 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss their key takeaways from the Marketing AI Conference (MAICON) 2024. Learn why scaling AI beyond consumer tools like ChatGPT is crucial for businesses looking to leverage its full potential. Discover why process decomposition is essential for successfully integrating AI into your workflow and how it allows you to identify specific tasks where AI can truly shine. Understand the importance of establishing clear business requirements and standard operating procedures before diving headfirst into AI implementation. Gain insights into how to evaluate new AI models as they emerge and learn to differentiate the hype from the practical applications.

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, we are back from a week on the road at the Marketing AI Conference in Cleveland, where both Katie and I spoke. Katie spoke on managing the people who manage AI, and I spoke on the importance of open models from a data privacy and business continuity perspective. Plus, we got a chance to hear all sorts of stuff from many different speakers. And on the very last day, two hours before the closing keynote, OpenAI released its new model, Zero One, a new reasoning model. So, there’s no shortage of things to talk about. Katie, when you look back at the whirlwind week that was Marketing AI Conference, what were some of the things that stuck out to you?

Katie Robbert – 00:41 Well, first and foremost, I want to thank the team over at MAICON for doing such an amazing job. Chris, it’s no secret that traveling to conferences isn’t my favorite thing. It’s not my favorite part of the job. However, I will say that the team who puts on MAICON, the team at the Marketing AI Institute, it’s done—and I gave them this feedback—it’s done in such a thoughtful way that it’s hard not to feel comfortable, regardless of your level of social anxiety. They really thought through how to make everybody included in the experience in a way that was going to be comfortable for them. So, I just really want to give them a huge shout-out because they did such a fantastic job.

There was no detail that they didn’t consider, and it really showed that they were really excited to do this for everybody. So, one of the things that I got from the conference was that it was a really nice validation, because sometimes we kind of live in our own little bubble. We forget that there’s other people out there doing similar things, that there’s other people out there talking about the stuff that we talk about—not in a competitive way, but in a peer-to-peer way. And it’s hard for us in the day to get out and see all ]]> full https://www.trustinsights.ai/wp-content/uploads/2024/09/In-Ear-Insights-MAICON-2024-Key-Takeaways.png In-Ear Insights: MAICON 2024 Key Takeaways false no 0:00 No no https://www.trustinsights.ai/blog/2024/09/in-ear-insights-responsible-ai-part-3-data-privacy/?pk_campaign=feed&pk_kwd=in-ear-insights-responsible-ai-part-3-data-privacy Wed, 11 Sep 2024 08:02:11 +0000 https://www.trustinsights.ai/?p=91801 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the importance of data privacy when using generative AI for marketing. You’ll discover the risks of blindly trusting AI vendors and the critical role of transparency in building trust with your audience. Learn why a robust AI responsibility plan is not just about legal compliance but also about protecting your brand reputation and ensuring the long-term success of your marketing efforts. Discover actionable steps for evaluating AI vendors and establishing clear accountability within your organization to mitigate potential risks associated with data privacy.

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Christopher Penn – 00:00 In this week’s In-Ear Insights, this is part three of our series on responsible AI. Today, we want to talk about one that a lot of people have questions on. This is all about keeping our data safe, protecting sensitive information, and building trust. One of the things that I’ve said for a while is transparency is the currency of trust. The more you can show people what’s happening to them, to their data, etcetera, and how you’re using it, the more easily you can build trust. So, Katie, when you think about the use of generative AI and the questions people have about what’s going on with my data, what have you seen and heard?

Katie Robbert – 00:49 I mean, the things I’ve seen. For those who don’t know, I actually started my career in a highly regulated space where I could at one point recite all 128 encrypted characters of HIPAA because it was such an important part of the work that we did—data privacy. And so you have HIPAA, you have COPPA, you have CCPA, you have GDPR—you have all of these regulations meant to protect someone’s personal information against the internet because it’s ours. We get to choose what we do with it. The challenge that I’ve seen in general is that companies are so hungry to get their hands on that information so that they can target individuals down to their specific behaviors and purchasing patterns. Companies that do that are just irresponsible.

Katie Robbert – 01:52 When you talk about using generative AI, there is a lack of education and understanding about how to use AI responsibly to then also protect. So let’s say you work at a company that does actually deal with personally identifiable information. Let’s say that is what you do and you’re looking to use generative AI. Unfortunately, mistakes happen, and people will sometimes take that PII and put it into an open model—being something that I pull up Google Gemini.com and then I just start giving it my data. That’s an ]]> https://www.trustinsights.ai/wp-content/uploads/2024/09/In-Ear-Insights-Responsible-AI-Part-3-Data-Privacy.png In-Ear Insights: Responsible AI Part 3, Data Privacy false no 0:00 No no https://www.trustinsights.ai/blog/2024/09/in-ear-insights-responsible-ai-part-2-managing-bias/?pk_campaign=feed&pk_kwd=in-ear-insights-responsible-ai-part-2-managing-bias Wed, 04 Sep 2024 08:02:08 +0000 https://www.trustinsights.ai/?p=91800 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris tackle the issue of bias in AI, particularly managing bias in large language models. Discover real-life examples of how bias manifests and the potential consequences for businesses, including reputational damage and the reinforcement of harmful stereotypes. You will learn about a critical framework for responsible AI development and deployment, emphasizing the importance of accountability, fairness, and transparency. Tune in to gain actionable strategies for mitigating bias in AI and promoting ethical practices within your organization.

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Christopher S. Penn – 00:00 In this week’s In-Ear Insights, this is part two of our series on responsible AI. This time, we’re going to talk about—particularly one of the biggest problems of generative AI, one of the biggest risks for our companies—is bias. Bias in the way AI systems and models work, bias in how they produce outputs. And, Katie, actually, just very recently, you were working on some content with Kelsey for our website using the generative models, and it spit out some stuff that was fairly sexist, at least from what I heard. So, do you want to talk about what happened there?

Katie Robbert – 00:39 Yeah. It’s no secret that we use large language models to assist with our writing. We’re a small company, but we’ve done a lot of work to create custom large language models based on our previous writing, based on our data. So we’re not just trying to pull something generic from any old open model. What happened, though? Because it was supposed to be writing as me, Katie Robbert, with a feminine name, the language model recognized Katie as a woman. And so, one of the things that I—Katie, in real life—do in my writing is I give anecdotes and examples to really help the content resonate with people.

Katie Robbert – 01:27 And so what the large language model did was it made decisions on its own about including anecdotes and examples, and everything it included was about fashion, shoe shopping, closets, going to the mall, keeping up on the latest clothing trends, which anyone who knows me knows that I basically dress one step above homeless. And so those are not things that are important to me, nor are they examples that I have ever given in my writing. I’ve not talked about cleaning out my closet to fit in the latest designer fashions.

It’s kind of disappointing that the large language model—despite having probably years’ worth of examples of my writing with no examples of those specific anecdotes—made those choices and assumptions about me because I have a feminine name, that those would be things]]> full https://www.trustinsights.ai/wp-content/uploads/2024/09/In-Ear-Insights-Responsible-AI-Part-2-Managing-Bias.png In-Ear Insights: Responsible AI Part 2, Managing Bias false no 0:00 No no https://www.trustinsights.ai/blog/2024/08/in-ear-insights-product-market-fit-and-the-ideal-customer-profile/?pk_campaign=feed&pk_kwd=in-ear-insights-product-market-fit-and-the-ideal-customer-profile Wed, 21 Aug 2024 08:53:09 +0000 https://www.trustinsights.ai/?p=91765 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to use the power of Ideal Customer Profiles (ICPs) to achieve product market fit and accelerate your marketing success. You’ll learn practical strategies for leveraging ICPs to validate your product ideas, ensuring you are building solutions your target audience truly desires. Discover the dangers of relying solely on your intuition as a marketer and how ICPs can provide invaluable, unbiased feedback, leading to increased revenue and engagement. Don’t miss out on the insights shared in this episode as Katie and Chris delve into the future of marketing with AI.

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, marketing and marketers—and I certainly hold myself to my hand is up for this—tend to be a little self-centered. We tend to think, “Here’s what we think the customer wants.” And there is some precedent for that. Steve Jobs is famously quoted as saying, “I know what the customer wants, even if they don’t.” Henry Ford was famous for saying, “If we’d asked the customer what they wanted, they would have said faster horses.” So there is that aspect to it. But on the other hand, there is also the voice of the customer. And now, in today’s era, we have more data about the customer than we have ever had before.

Christopher S. Penn – 00:39 So, Katie, when we’re thinking about how you balance what you see as sort of the vision of the product, the service, the company as a leader with what the customer says they want and the voice of the customer, how do you find that balance?

Katie Robbert – 00:55 Well, it’s an interesting conversation because what we’re really talking about is innovation versus solution. And sometimes they’re not the same thing. So, Thomas Edison needed to create these inventions, but nobody was necessarily knowing that these were problems. He was just like, “Let me see what this does. Let me see what this does.” Much like his famous last words were probably like, “What does this button do?”

And so, you need those experimental mindsets, those visionaries, those innovators to keep sort of pushing what’s possible. But that doesn’t always translate into, “Okay, I’m sitting here at my computer every day. Do I need—” I’m just going to make this up completely—

Katie Robbert – 01:49 “Like a teleportation device that brings down a robot that actually serves me like a single serving of coffee that I didn’t e]]> full https://www.trustinsights.ai/wp-content/uploads/2024/08/In-Ear-Insights-Product-Market-Fit-and-the-Ideal-Customer-Profile.png In-Ear Insights: Product Market Fit and the Ideal Customer Profile false no 0:00 No no https://www.trustinsights.ai/blog/2024/08/in-ear-insights-change-management-and-generative-artificial-intelligence/?pk_campaign=feed&pk_kwd=in-ear-insights-change-management-and-generative-artificial-intelligence Wed, 07 Aug 2024 08:25:55 +0000 https://www.trustinsights.ai/?p=91707 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss change management, generative artificial intelligence, and how to help your team embrace AI and what to do with team members who are resistant to AI adoption. Learn why the human element remains critical for successful AI implementation and how to best communicate the value of AI to your team. Discover why throwing AI at a problem isn’t the answer and how Katie uses AI to support, rather than replace, her work.

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Christopher Penn – 00:00 In this week’s In Ear Insights, Katie, I have a question for you as a leader, as a CEO, as a change management expert because you are. How do you convince anti-AI people to not be? By this, I mean, we’ve had a lot of companies, a lot of people, ask us to say, “Hey, we really want to do a digital transformation with generative AI,” and some of the use cases are really good use cases. But then you start talking to people who are like staff and things and you get some very—some varied but very firm resistance to it. People saying, “It’s going to take my job, it’s going to create mass unemployment, it undermines my self-worth as a creative person. It can’t produce good results, it’s not creative, it’s built on stolen intellectual properties.”

Christopher Penn – 00:50 All these different things that people have as reasons for objections to AI. There is the tried and true and somewhat ineffective: You’ll either do it the way the CEO says, or you’ll get out. But I suspect that’s more my management style than yours. So I’d like to hear from someone who actually manages people: How do we help people overcome the anti-AI perspective? Can we, should we? And if so, how?

Katie Robbert – 01:24 So, there’s definitely a lot to unpack there. So first, I sort of want to start with why can’t I just tell people, “This is what we’re going to do?” So, you absolutely can. You can say, “This is what we’re going to do.” So Chris, you have kids, teenagers now, young adults now. When you say, “You’re going to do this now,” how well does it go over?

Christopher Penn – 01:51 It depends on the thing. But one of the things I’ve tried to do since they were little is to tell them why. It’s not just because I’m just saying something arbitrarily. It’s because, “Here’s why. Hey, c]]> https://www.trustinsights.ai/wp-content/uploads/2024/08/In-Ear-Insights-Change-Management-and-GEnerative-Artificial-intelligence.png In-Ear Insights: Change Management and Generative Artificial intelligence false no 0:00 No no https://www.trustinsights.ai/blog/2024/07/in-ear-insights-limitations-of-generative-analytics/?pk_campaign=feed&pk_kwd=in-ear-insights-limitations-of-generative-analytics Wed, 31 Jul 2024 08:40:03 +0000 https://www.trustinsights.ai/?p=91692 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the limitations of generative analytics, particularly for casual users. Discover why subject matter expertise is still essential for accurate data analysis, even with these powerful tools. Learn practical strategies for determining if a task is suitable for generative AI and how to incorporate it effectively into your analytics workflow. Finally, understand the importance of process mapping in maximizing the benefits and mitigating the potential challenges of using generative AI for analytics.

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Christopher S. Penn – 00:00 In this week’s In Ear Insights, this is part two, the cliffhanger resolution from last week’s episode on generative analytics. So, we talked last week about generative analytics, the use of generative AI tools to enable your analytics data. We’ve been sitting around for decades with data, and it just sits there. We do nothing with it. Generative AI tools give us the ability to give this data to tools and then have them tell us “so what?” and “now what?”. But, Katie, you said at the end of last week’s episode, therein lies the challenge: thinking to ask a question, knowing what to ask the machine so you’re getting the right information. I think a lot of us have already made the assumption, “Well, generative AI must have the right answer.” How do we find out before we give them all of our data how do we find out whether or not it even has the right information or the right knowledge so that we can use it to do insightful analysis?

Katie Robbert – 00:53 Yeah, and I stand by those questions. It’s interesting. It’s not just for generative analytics; it’s really for anything. But, if we focus in on generative analytics, my concern is — or my question is — if you’re going to start using large language models to help you with your analysis and insights, how do you know it’s right? This goes back to something we always talk about: you have to have some kind of subject matter expertise to validate that the machines are giving you the right information. That’s not something you can just, like, wake up and be like, “Oh, and I’m a subject matter expert.” Like, those are things that take a long time, a lot of practice, a lot of making mistakes, a lot of trial and error, and just a lot of experience. I think what I’m concerned is going to happen is that companies maybe don’t have the budget or aren’t willing to spend the budget on an analytics team, on a data analyst, on data governance — whatever the thing is — and they’re just g]]> https://www.trustinsights.ai/wp-content/uploads/2024/07/In-Ear-Insights-Limitations-of-Generative-Analytics.png In-Ear Insights: Limitations of Generative Analytics false no 0:00 No no https://www.trustinsights.ai/blog/2024/07/in-ear-insights-what-is-generative-analytics/?pk_campaign=feed&pk_kwd=in-ear-insights-what-is-generative-analytics Wed, 24 Jul 2024 07:49:06 +0000 https://www.trustinsights.ai/?p=91677 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative analytics, a new way to use AI to get more from your data. Discover why simply collecting data isn’t enough and how generative analytics helps you uncover actionable insights. Learn practical ways to apply this approach to your marketing efforts and avoid common analysis pitfalls. Finally, understand the importance of good data governance for getting accurate results from any AI-powered tool.

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Christopher Penn – 00:00 In this week’s In Ear Insights, let’s talk about a term you probably haven’t heard yet: generative analytics. Now, before I dig into what that means, Katie, what does that mean to you when you hear that term?

Katie Robbert – 00:19 It sounds akin to predictive analytics, where there’s some kind of machine learning involved. It sounds like perhaps we’re going to be using generative AI to make up data, which is probably incorrect. I don’t know. But yeah, that’s what it sounds like. We’re going to make some stuff up.

Christopher Penn – 00:50 I mean, when it comes to analytics, generally making stuff up is not the first choice in the context of what we do at Trust Insights and what we’d like to see more people do. Generative analytics is the use of generative AI to process analytics data and answer the question “so what?” There is so much data. We are drowning in data. We’ve got Google Analytics and your marketing automation and your CRM and your customer service.

There’s so much data, and we don’t really look at it. Or if we do look at it, we put it in PowerPoint slides and binders and look at it once. We go, “Huh, that looks good.” And then we never do anything with it. It just goes on a shelf.

Christopher Penn – 01:37 Generative analytics—at least in the way I’m defining it in today’s show—is the use of generative AI to answer the question “so what?” And then answer the question “now what?” “Hey, the website went up last week.” “Okay, so what?” “Well, it was probably because of this. What are we going to do about it? Do more of this?” That’s kind of the idea behind it.

Katie Robbert – 02:03 I think the term analysis paralysis is what comes to mind. I was talking with someone recently, and she quoted to me, she’s like, “Back when there was big data and everybody was collecting all of their data.” And I just kind of had to laugh a little bit. I’m like, “That’s just a fancy marketing term for ‘let’s just try to get our h]]> full false no 0:00 No no https://www.trustinsights.ai/blog/2024/07/in-ear-insights-ceo-perspective-on-generative-ai-and-the-future-of-work/?pk_campaign=feed&pk_kwd=in-ear-insights-ceo-perspective-on-generative-ai-and-the-future-of-work Wed, 17 Jul 2024 11:44:02 +0000 https://www.trustinsights.ai/?p=91662 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the future of work and how the capabilities of generative AI are changing how executives think about building a team. Learn why simply cutting staff to save money is not a viable long-term strategy. Discover the questions you should ask to accurately assess your needs and build a plan. Understand the importance of including your team in the decision-making process to foster transparency, ownership, and innovation.

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Christopher Penn – 00:00 In this week’s In Ear Insights, let’s talk about the future of work and generative AI’s impact on it. There is an old adage: fast, cheap, good — choose any two. That applies to pretty much everything. If you want something fast and good, it’s not going to be cheap. If you want something good and cheap, it’s not going to be fast, and so on and so forth.

This adage applies to human endeavors. With generative AI, with enough skill and experience and things like prompting and building systems, you can have all three. You can have something that’s fast, cheap, and good. It may not be great, but it will be good.

Christopher Penn – 00:36 For example, if you wanted to, as we talked about on recent live streams, if you wanted to create things like editorial calendars or content newsletters and podcast outlines, today’s generative AI tools are very skilled at creating those things, either for a human to do or in some cases, just do it outright for you. Depending on how good your data is, this is going to change how executives and stakeholders and CEOs make decisions about hiring, about retention, about the structure of a workforce and the future of work itself.

So, Katie, when you think about a world where fast, cheap, and good is achievable — all three — by machines versus the human limitations of “choose any two,” how does that, how do you…

Katie Robbert – 01:26 Look at that as a CEO? With huge skepticism. And I say that because it, because we’ve only ever seen two of the three. To believe that you can get all three feels really risky. And so I think that executives should be curious, but I think that they should proceed with that bit of skepticism.

So, what I’m seeing a lot of executives do is open their arms and embrace generative AI with no real consideration for the long term ramifications. And so they’re just saying, “Great, let’s bring it in, let’s make some money. Let’s fire half the workforce because they can do it.” So, it’s a l]]> full https://www.trustinsights.ai/wp-content/uploads/2024/07/In-Ear-Insights-CEO-Perspective-on-generative-AI-and-the-future-of-work.png In-Ear Insights: CEO Perspective on Generative AI and the Future of Work false no 0:00 No no https://www.trustinsights.ai/blog/2024/07/in-ear-insights-trust-and-influencer-marketing-strategy/?pk_campaign=feed&pk_kwd=in-ear-insights-trust-and-influencer-marketing-strategy Wed, 10 Jul 2024 08:21:27 +0000 https://www.trustinsights.ai/?p=91643 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the crisis of trust in institutions, influencer marketing strategy, and what it means for your marketing. Learn why Gen Z gravitates toward individual influencers and away from brands. Discover how to adapt your marketing to include influencers, even if you’re not a Fortune 100 company. You’ll also learn how to mitigate the risks associated with building a marketing platform around an individual, such as using joint ventures with clear ownership and performance metrics.

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Christopher Penn – 00:00 In this week’s In-Ear Insights, let’s talk about the maybe less savory side of the way some of the ways AI is being used, the way people are reacting, and what it means for your business. So the World Economic Forum recently had a paper come out that said that essentially generative AI — it’s AI itself — but generative AI, in particular, is the largest single new source of disinformation: willfully incorrect information being used by hostile states to mess with elections and things like that, deep fakes and so on and so forth. It also stated that the world needs to get their arms around this before it causes a new dark age.

That’s one piece of this puzzle. The second piece of this puzzle was a recent piece — I want to say it was on Business Insider, I don’t remember — I saw it on LinkedIn saying that Gen Z’s media consumption, because of a lack of trust in all institutions, is wildly different. The Gen Z media diet now is hyper-focused around individual influencers, as a YouTuber or a TikToker that you follow, and you are all in on that personality because you get value from them. You believe them, and not the corporate media news complex or the politician.

When you put these two things together, what it says for marketers and business folks is we have a substantial crisis of trust in all the traditional institutions. If your marketing — which, spoiler, it is all marketing — is based on trust, if your marketing is being perceived, particularly by younger generations, as not being trustworthy, what do you do? How do you build a strategy?

Christopher Penn – 01:49 Do you have to grow your own influencer so that you have those raving loyalists who follow just that one person? There’s a lot of different angles to attack this, but the fundamental problem is — because of things like generative AI — there is a dramatic lack of trust in all institutions, including us as companies, as marketers. So, Katie, what’s]]> https://www.trustinsights.ai/wp-content/uploads/2024/07/In-Ear-Insights-Trust-and-Influencer-Marketing-Strategy.png In-Ear Insights: Trust and Influencer Marketing Strategy false no 0:00 No no https://www.trustinsights.ai/blog/2024/07/in-ear-insights-generative-ai-for-non-business-use-cases/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-for-non-business-use-cases Wed, 03 Jul 2024 08:28:01 +0000 https://www.trustinsights.ai/?p=91636 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss using generative AI for non-business, fun tasks. You’ll discover how to leverage generative AI to solve everyday challenges like creating a recipe with specific ingredients or remembering what to pack for a trip. You’ll learn how to use AI to enhance your problem-solving skills and unlock creative solutions you might not find on Google. Tune in to discover the surprising potential of generative AI in your daily life!

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Christopher Penn – 00:00 In this week’s In Ear Insights, it is here in America, the 4th of July holiday week. So we’re taking things a little lighter, and I thought this week we’d talk about applications of generative AI that are non-business-related. Specifically, because even though these tools give us such incredible powers of productivity and stuff, more importantly, when we look at non-traditional or I guess non-business uses of them, it can help us understand how to better use them at work in business for productivity by broadening our understanding of their capabilities. So Katie, I’m going to play a very short, 30-second clip that I shot yesterday at the grocery store so that you can see this interesting application. So let’s go ahead and roll this. Today I am cooking bun, the Vietnamese noodle bowl dish. Here’s what I have in my grocery cart so far.

Christopher Penn – 00:59 What have I forgotten to buy for this recipe? So it gave me a long list of, hey, here’s all the things that this recipe requires that you don’t have in your cart, buddy.

Katie Robbert – 01:23 Well, it’s interesting because a couple of things come to mind for me. One is, what if the way that you have things in your cart doesn’t show everything? And two, why wouldn’t you just ask for a list ahead of giving your picture and say, “Hey, what do I need for this recipe? Can you give me a list?” Versus— I don’t know, it just— Using generative AI in my everyday life is just still not something I personally feel the need for. And so when I see this, I’m like, “Okay, that’s fine, but why? Why did you need to do it that way?” So that’s what I need help understanding. I need you to convince me.

Christopher Penn – 02:08 I did it this way because I wanted to entertain myself with it. I mean, if we’re perfectly honest, that’s the reason why there is no— there is, there’s no actual logical reason to do it that way. However, and yes, you’re right, if something’s not in the vision view of the camera, then]]> full https://www.trustinsights.ai/wp-content/uploads/2024/07/In-Ear-Insights-Generative-AI-for-Non-Business-Use-Cases.png In-Ear Insights: Generative AI for Non-Business Use Cases false no 0:00 No no https://www.trustinsights.ai/blog/2024/06/in-ear-insights-generative-ai-data-management-best-practices/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-data-management-best-practices Wed, 26 Jun 2024 08:40:06 +0000 https://www.trustinsights.ai/?p=91623 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative AI data management best practices and how to leverage qualitative data for content marketing. You will learn how to extract valuable customer questions from various sources like webinars and forums using AI. Discover effective methods to prioritize this wealth of information based on your ideal customer profile and keyword strategy. Finally, understand how to use AI tools to quickly generate content by referencing your existing materials and repurposing previous work.

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Christopher Penn – 00:00 In this week’s In Ear Insights, let’s talk about the stuff you’ve got laying around, specifically data. Katie, one of the things you’ve said time and again, which is 100% accurate, is that data isn’t just numbers. Data is any information you have laying around. One of the things we try to do for our own marketing is make use of the data we have because we have so much of it. I’ve been collecting questions from webinars and things that we’ve done. Every time I’m speaking at a virtual event, I copy and paste the chat log because there’s a lot of people who have really good questions. As much as I would like to say, yeah, every question gets followed up afterwards, that doesn’t always happen, particularly if it’s not our event.

Christopher Penn – 00:46 I’ve been looking at our back catalog, and our back catalog of questions that we have left over from events is enormous. Hundreds and hundreds of questions. So, Katie, when you think about content marketing strategy and using AI to make use of the stuff we have laying around, why aren’t more companies, including us, to some degree, why aren’t more companies doing more with this enormous goldmine of data?

Katie Robbert – 01:24 In my experience, the qualitative data feels daunting to try and analyze. When you have the quantitative data, the numeric data, there’s something—at least this is the way that I’ve understood it. When I’ve worked with other data teams, it’s quote-unquote easier to put all of the numbers in a spreadsheet and get them to make sense. It’s not the same process for getting feedback and sentences and unstructured data. That’s the stuff that’s like, yeah, we totally should collect it, but we don’t know how to analyze it because it’s harder to— I guess, what am I trying to—basically, math is a language, but math is a language that isn’t open to interpretation.

There’s no slang, there’s no regional ]]> full https://www.trustinsights.ai/wp-content/uploads/2024/06/In-Ear-Insights-Generative-AI-Data-Management-Best-Practices.png In-Ear Insights: Generative AI Data Management Best Practices false no 0:00 No no https://www.trustinsights.ai/blog/2024/06/in-ear-insights-how-to-use-generative-ai-in-sales/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-use-generative-ai-in-sales Wed, 19 Jun 2024 07:46:46 +0000 https://www.trustinsights.ai/?p=91586 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris tackle the challenge of using generative AI in sales, both B2B and B2C. Discover how generative AI helps you analyze customer data and identify pain points to improve your selling approach. Learn how this technology can bridge the gap between sales, marketing, and product development for a more collaborative and effective strategy. Katie and Chris emphasize the importance of understanding customer needs and aligning your products or services accordingly, highlighting the limitations of AI when it comes to human judgment and decision-making.

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Christopher Penn – 00:00In this week’s In Ear Insights, let’s talk about generative AI for sales, both B2B and B2C. How do we use these tools to do arguably one of the most important functions within a company, which is to help bring on new customers? Katie, when you think about sales as a profession, as a discipline, as a function within a company, what do you see as the major obstacles, period, that sales runs into? If you were like, “Oh, if I had a magic wand, I would fix this.”

Katie Robbert – 00:37That’s such a big question because it’s going to be different for every organization. I guess the common theme, the common deficit I see is collaboration with other teams. When I worked at an organization that had a dedicated sales team, there was a disconnect between the product team and the sales team, and the marketing team and the sales team. Product and marketing worked really close together, and then we would bring sales into it, and somehow sales remained on their own island and then would complain that we didn’t have the latest information: “We don’t know this; we don’t know that. Who’s our target audience? Who are we going after? Has it changed?”

Katie Robbert – 01:30I guess the thing is data sharing and a communication breakdown. From my perspective, sales is the last piece of the puzzle. They’re the last touch. When you’re bringing on a new client, first, you have all of these other pieces in the customer journey. You have your content; you have marketing campaigns. You have maybe some interaction, like webinars, emails, and live shows, but sales isn’t in it yet. Sales is the one who comes in at the end to close it. I think that there’s a disconnect in that process.

Katie Robbert – 02:14I don’t know if that’s the answer you were looking for, but I think that if]]> full https://www.trustinsights.ai/wp-content/uploads/2024/06/In-Ear-Insights-How-to-Use-Generative-AI-in-Sales.png In-Ear Insights: How to Use Generative AI in Sales false no 0:00 No no https://www.trustinsights.ai/blog/2024/06/in-ear-insights-generative-ai-and-professional-development/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-and-professional-development Wed, 12 Jun 2024 07:46:44 +0000 https://www.trustinsights.ai/?p=91585 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative AI and professional development, the importance of subject matter expertise when using AI for marketing tasks, such as analyzing backlink data. You’ll learn why relying solely on AI-generated insights without understanding the underlying data can be risky. Katie and Chris explain why training your team members first, then training your AI, leads to more accurate results and better decision-making. Discover the crucial steps you need to take to ensure your AI is working with you, not against you, and that your marketing efforts are successful.

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Christopher Penn – 00:00In this week’s In Ear Insights, let’s talk about a situation that happened at Trust Insights last week that I want your perspective on, Katie, because you were instrumental in it. For background, we have an account manager, her name is Kelsey, who is an absolutely outstanding, wonderful person to work with. I wrote a prompt that did detailed analysis of some SEO data. It’s like this long and stuff like that, following the Trust Insights RACE Framework and the PAIR Framework and all that stuff. It does a decent job of producing good insights—the prompt does.

One of the things that you had said in response to it was perhaps we should not have the machine doing all the work. Perhaps the human who is assembling the report should have some background knowledge so that they know what they’re looking at.

So, my question—I have a couple of questions for you, Katie. One, why do you think—and I know the answer, I want to hear from you—why do you think it’s important to have that background information as the person operating machinery? And two, as AI becomes more and more capable and goes from today assisting someone with doing, in this particular task, interpreting backlink information to just being able to outright do it, soup to nuts, at what point, if any, does the human need to be involved anymore with something that’s a relatively routine reporting task?

Katie Robbert – 01:29So I think—I mean it’s a great topic. I think this is a really good example. So, for full context, as Chris mentioned, we brought on Kelsey as our account manager. Kelsey comes from a different background. I brought her on for her organizational and managerial skills, knowing that things like marketing, SEO, and all that stuff can be taught. Very similar to my experience when I started working with Chris, I was brought on for my experience as a manager, not as my expe]]> full https://www.trustinsights.ai/wp-content/uploads/2024/06/In-Ear-Insights-Generative-AI-and-professional-development.png In-Ear Insights: Generative AI and Professional Development false no 0:00 No no https://www.trustinsights.ai/blog/2024/06/in-ear-insights-how-to-spot-ai-snake-oil-salesmen/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-spot-ai-snake-oil-salesmen Wed, 05 Jun 2024 07:33:55 +0000 https://www.trustinsights.ai/?p=91563 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to spot an AI snake oil salesman, a vital skill in today’s hype-filled environment. Learn the questions to ask that reveal true expertise and understand the hallmarks of genuine AI knowledge. Discover the red flags to watch out for so you don’t fall prey to fleeting trends and empty promises. Tune in to gain valuable insights that will empower you to navigate the world of AI with confidence.

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about, well, I have a bit of a rant. Ever since November 2022, when ChatGPT came out and the ability for the average non-technical person, the non-technical business person, to be able to use AI tools came out, suddenly everyone and their cousin was an AI expert. All people were hawking crypto and NFTs. So, the AI experts—meanwhile, the folks who have been in the profession for a decade, two decades, their entire careers, etc.—are like, “You don’t know anything about anything. You’re literally just copying and pasting each other’s ChatGPT prompts.”

Even today, I was in my inbox this morning trying to clean it out, and there are people offering all sorts of ChatGPT prompt workshops. You can get this strategic AI consulting workshop, and I look at the outlines and go, “I don’t know that you know what you’re doing.”

So Katie, let me ask you, as someone who has some hands-on experience with general AI, but is not an engineer: How do you detect snake oil when it comes to AI? How do you, when you look at your LinkedIn feed and you see somebody talking about AI as though they were an expert and you know that person is not an AI expert, how do you tell the difference? How would you advise someone else to enhance and improve their BS detector?

Katie Robbert 1:34

Oh, it’s such a—it’s a tricky question, because now you’re talking about people management and sort of like the intangibles. This is something that I get asked a lot, and I struggled to communicate it because it’s a lot of just knowing behaviors and, well, I shouldn’t—I shouldn’t say “struggle”. I’ve spent a lot of time studying human behavior, and I’ve spent a lot of time studying communication and body language and nonverbal cues. But also, the way that people communicate online is a whole practice in and of itself.

I feel like the first thing I look for is how much fluff there is versus how much substance there is. So, if you’re telling me that you have 98 prompts that are going to change my life, like, first, it’s the wording—first of all, that sounds very salesy and catchy. Then I’m like, “Okay, well, let me]]> full https://www.trustinsights.ai/wp-content/uploads/2024/06/In-Ear-Insights-How-to-Spot-AI-Snake-Oil-Salesmen.png In-Ear Insights: How to Spot AI Snake Oil Salesmen false no 0:00 No no https://www.trustinsights.ai/blog/2024/05/in-ear-insights-how-to-help-marketers-adopt-agile-and-prompt-engineering/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-help-marketers-adopt-agile-and-prompt-engineering Wed, 29 May 2024 10:17:26 +0000 https://www.trustinsights.ai/?p=91472 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris explore the human side of how to adopt Agile and prompt engineering. Learn how to build trust within your team and address anxieties surrounding AI and automation. Discover strategies for fostering open communication and encouraging ownership among team members, even those hesitant about adopting new technologies. Finally, understand how aligning individual purposes with company goals is crucial for successful implementation of Agile and AI initiatives.

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Christopher Penn 0:00

In this week’s In-Ear Insights, we’re on part four of our series on agile and AI.

This week, we’re going to put a bow on everything by talking about the one part that we really haven’t talked about so far.

In the five Ps of agile and AI, we’ve talked about what Agile is, we’ve talked about prompt engineering and shown some examples, and we’ve talked about applying Agile to prompt engineering.

What we haven’t talked about is the people.

Katie, how do we create and empower people with both agile and prompt engineering? For a lot of companies, this stuff is still so new, and it feels like implementing both agile and prompt engineering at the same time could be overwhelming.

What’s the pathway to getting an organization or team up to speed on both without causing them to cower under their desks in fear?

Katie Robbert 1:03

Even asking the question is overwhelming because it is a lot.

It’s a culture shift.

Introducing a new technology that you want to infiltrate into everything is a culture shift; introducing a new methodology for getting things done is a culture shift.

And so we absolutely should be talking about the people.

I always start at the top: What is the purpose? Why are we doing this? Anytime I’ve been faced with some kind of change management, I always know that the hardest part of the project is going to be getting people on board.

The reason for that is because people naturally want to know why.

“Why are you asking me to do this? What is in it for me? How does this impact me? What does this mean for my job? Do I have a sense of ownership in this? Can I have an opinion in this?” These are all normal, natural questions.

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Christopher Penn 0:00

In this week’s In-Ear Insights, we’re in part three of our agile prompt engineering/agile AI series.

In episode one, we covered what Agile is; in part two, we covered the basics of prompt engineering, the RACE framework, and where the opportunities are to use generative AI.

Today, let’s talk about agile and prompt engineering—taking the peanut butter and jelly and putting them together—or I guess the peanut butter and chocolate, depending on how old you are.

Katie, how do we put these two great things together?

Katie Robbert 0:37

Personally, I could go for peanut butter and fluff.

I do like peanut butter and chocolate, though.

But I think the point is, how can we mix together Agile methodology and prompt engineering? The answer will shock you: requirements gathering and the five Ps.

You cannot escape it; you cannot get away from doing documentation.

I’m not even sorry.

Prompt engineering, at its core, is a form of development, and development—software development—lends itself to Agile methodology.

That’s why Agile methodology came about.

So the question is, what is the prompt engineering lifecycle? Similar to the development lifecycle? And where does Agile fit into it? First things first, what the heck are you doing? What are you trying to accomplish? And that’s where the five P framework comes in.

The five Ps are purpose, people, process, platform, and performance.

Purpose: What is the question you’re trying to answer? People: Who’s involved? Who’s going to be impacted by this? Process: How are you doing the thing? Platform: What tools do you need? And performance: What is your outcome? Chris, I don’t know if you have it handy, but I’ve sta]]> https://www.trustinsights.ai/wp-content/uploads/2024/04/In-Ear-Insights-How-to-Apply-Agile-to-Prompt-Engineering.png In-Ear Insights: How to Apply Agile Prompt Engineering false no 0:00 No no https://www.trustinsights.ai/blog/2024/05/in-ear-insights-prompt-engineering-101/?pk_campaign=feed&pk_kwd=in-ear-insights-prompt-engineering-101 Wed, 15 May 2024 10:17:24 +0000 https://www.trustinsights.ai/?p=91470 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris provide a practical guide to prompt engineering, prompt engineering 101. Learn how to approach prompt creation like software development, starting with thorough requirements gathering using the 5 P’s framework. Discover the power of the RACE framework (Role, Action, Context, Execute) for crafting effective prompts that produce high-quality outputs. Finally, understand the importance of incorporating feedback and refining your prompts over time to improve accuracy and efficiency.

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about prompt engineering 101, but from a different perspective.

We’ve talked about prompt engineering a ton in terms of writing prompts and what it is, etcetera.

This week, as part of this four-part series, let’s talk about prompt engineering in the context of proper software development and the 101 of how you do that.

I pretty much assume we don’t need to talk about why prompt engineering is important.

ChatGPT has taken care of that for us, and we’ve talked about many of our different frameworks for doing it.

But to start off, Katie, when you’re talking about good software development—because prompt engineering is writing code, you are writing code, you just happen to be writing code in English, or in Danish, or Japanese, and not in Python, or Java, or C++—when you’re doing good software engineering, where do you start to ensure that you get a good result out of your software development exercises, whether it’s a little script or a big one?

Katie Robbert 1:09

Do you want to hear the shocking answer?

Christopher Penn 1:10

I’m sure I’m going to be stunned.

I’m going to guess it’s going to start with something like, I don’t know, requirements gathering?

Katie Robbert 1:19

You would be 100% correct.

Software development is one of those practices that people love to hate, and they hate to love, because developers—in broad strokes—developers don’t like to do documentation, they just want to write code, they just want to make things.

But good software development needs to have a set of requirements, and the requirements don’t have to be cumbersome.

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Christopher Penn 0:00

In this week’s In-Ear Insights, we’re starting the first of a four-part series on the use of agile techniques in marketing and generative AI.

So, this week, let’s talk about what the heck is agile.

You’ve heard this term, and you’ve probably heard this term badly misused.

So Katie, when people are talking about agile—agile methodology, agile marketing, agile development, etc.—what is this?

Katie Robbert 0:26

Agile methodology comes from software development.

In project management, there are two standard types of approaches you take to a project: one is called waterfall, the other is called agile.

Now, there are a bunch of other subcategories, but those are the main parent categories.

Waterfall is the most traditional and most highly used method of getting projects done, until agile came on the scene.

So waterfall—okay, so if you’re listening to this, we’re showing this on our screens, you can catch it on TrustInsights.ai/youtube—so waterfall is a step-wise process where you have to finish the first phase before you move on to the next one.

Therefore, it creates this idea of a staircase or waterfall, where you go from the top down.

So you do your requirements, you have to finish those completely before you move on to design, then you move on to design, you finish those completely, you sign off on everything, and so on and so forth.

The upside of waterfall is you know what to expect; you have all of your pieces together.

The downside of waterfall is if you want to make a change to something somewhere—if you want to change a design, your business goals have changed, whatever it is—you have to go back and start at the beginning.

Waterfall is really good for projects that are repeatable, the process is cl]]> https://www.trustinsights.ai/wp-content/uploads/2024/04/In-Ear-Insights-Introduction-to-Agile-Marketing.png In-Ear Insights: Introduction to Agile Marketing false no 0:00 No no https://www.trustinsights.ai/blog/2024/05/in-ear-insights-what-are-ai-ethics/?pk_campaign=feed&pk_kwd=in-ear-insights-what-are-ai-ethics Wed, 01 May 2024 09:03:16 +0000 https://www.trustinsights.ai/?p=91487 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris tackle the complex and critical topic of AI ethics. You will explore the fundamental principles of ethics and how they apply to the development and use of artificial intelligence. Discover the importance of transparency, accountability, and minimizing harm when working with AI models. Finally, learn how to navigate the challenges of bias and censorship in AI, and how to choose the right tools and frameworks that align with your company’s values and ethical principles.

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk philosophy, ethics, morality, and all sorts of fun things within the context of artificial intelligence.

Ethics is a hot topic in AI; it’s the pinnacle of the discussion.

What does ethics mean around artificial intelligence, particularly generative AI? And how do we apply an ethical framework? Katie, I want to start you off with the $10 million question: what is ethics?

Katie Robbert 0:35

I knew you were going to ask me that question.

Christopher Penn 0:37

I will preface this by saying there are a lot of schools of ethical thought.

If you go to a search engine, you will see philosophers ruminating on ethics for centuries.

So we’re going to put you on the spot here.

Katie Robbert 0:53

There is a general definition of ethics, which is your guiding moral principle.

But what that means to any individual person is going to be different.

There is no black and white answer to say, “This is ethically responsible.

This is 100% what ethics is.” So the definition, by nature, is vague, and that’s why it’s been debated for centuries, because it’s not like, “What is gravity?” Well, gravity, there’s one definition of gravity, it is what it is.

I’m not, Chris, I’m not getting into analytical nonsense tonight.

Christopher Penn 1:40

Okay, I’ll ask you a follow-up question.

What is the difference, to you, as a CEO, as the responsible business owner, what’s the difference between ethics and morals, because they are different?

Katie Robbert 1:50

https://www.trustinsights.ai/wp-content/uploads/2024/04/In-Ear-Insights-What-Are-AI-Ethics.png In-Ear Insights: What Are AI Ethics? false no 0:00 No no https://www.trustinsights.ai/blog/2024/04/in-ear-insights-how-to-build-an-ai-strategy/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-build-an-ai-strategy Wed, 24 Apr 2024 08:48:22 +0000 https://www.trustinsights.ai/?p=91464 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to build an AI strategy. Discover why focusing on AI as a strategy is misguided and what you should do instead to achieve meaningful business outcomes. You’ll learn how to identify the actual problems you need to solve and how AI tools like generative AI can fit into your existing processes and strategies. Gain insights into the six broad use cases of generative AI and explore how to leverage these capabilities to enhance your business operations. Finally, understand how to effectively communicate and collaborate with stakeholders who may have unrealistic expectations about AI.

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Christopher Penn 0:00

In this week’s In-Ear Insights, one of the things that Katie, you and I have talked about—and you have been correctly adamant about—is that AI is a tool.

It is a set of technologies.

It is not a strategy.

It is not something you lead with.

We lead with the five P’s: purpose, people, plan, process, platform.

First, what is the thing you’re trying to do? And is AI a good fit for it? But we do have to acknowledge the reality out in the jungle that is the modern business, which is that AI is the shiny object, and everyone and their cousins are like, “What is our AI strategy? How do we build our AI strategy?” You know, marketing director, whatever, “We need an AI strategy, go get me an AI strategy.” And we’re all like, “That’s, that’s really a bad idea.” But that’s reality.

So with that in mind—this is inspired by an actual customer inquiry we got this morning—when someone says, “I have to have an AI strategy, even if there is absolutely no purpose for it, but I need to make the corner office or the board happy.

I need to come up with a strategy.” Katie, what do we, what do we do to help that person deliver something that is an AI strategy, even if it is not relevant to the situation?

Katie Robbert 1:15

So you light some candles, you lay out some, you know, your herbs, and you start to chant and say, “Please make this go away.” And you hope for a celestial event or a full moon.

In reality, you know—and you’re right, you’re absolutely right—just because we know (you and I and, you know, a handful of other people) that AI is not a strategy does not mean that that’s not what’s happening in the market.

full https://www.trustinsights.ai/wp-content/uploads/2024/04/In-Ear-Insights-HOW-TO-BUILD-AN-AI-STRATEGY.png In-Ear Insights: How to Build an AI Strategy false no 0:00 No no https://www.trustinsights.ai/blog/2024/04/in-ear-insights-prompt-development-life-cycle/?pk_campaign=feed&pk_kwd=in-ear-insights-prompt-development-life-cycle Wed, 17 Apr 2024 08:03:48 +0000 https://www.trustinsights.ai/?p=91447 In this week’s In-Ear Insights, Katie and Chris discuss how the software development life cycle (SDLC) applies to prompt engineering in generative AI, why the prompt development life cycle (or prompt engineering life cycle) is a good idea, and a real-life application of it.

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Christopher Penn: This week on In-Ear Insights, let’s discuss prompt engineering and software development—two seemingly disparate concepts that surprisingly converge. Here’s why: programming involves writing a sequence of text instructions for a machine to produce a repeatable, reliable result. You write code in C++, Python, or Java to perform a task and generate an output. Ideally, good code yields good results.

When you write prompts for generative AI and large language models, you’re essentially programming, just in a language like English, French, or Danish instead of C++. This means adopting good software engineering practices becomes crucial. You’re writing code, so you should protect it, audit it, and manage it effectively.

Katie, you’ve managed development teams—including me, bless your heart—when it comes to prompt engineering, development, internal sharing, and debugging. Many people, especially marketers, seem to just wing it instead of having a scalable, structured approach. So, when managing the prompt development lifecycle, what should people consider?

Katie Robbert: Requirements are paramount. Let’s take a step back. I was just checking our website for our version of the SDLC (software development lifecycle). We probably have it somewhere, maybe as an In-Ear Insight.

Chris, this topic arose last week when you drew a parallel between the SDLC and prompt engineering, highlighting the significant overlap. The question is, can we adapt the SDLC for prompt engineering? Absolutely!

The SDLC, like any project management or development lifecycle, has a few key phases: requirements gathering (business goals, development goals, needs, and approach), process definition (tools and methods), execution, testing, and iteration. It aligns surprisingly well with the five Ps framework: purpose, people, process, platform, and performance.

In this context, the purpose is to write a prompt for a specific task. People involve gathering stakeholder requirements. Process encompasses the prompt’s content, research needs, iterative testing, and validation methods. Platform seems straightforward—the environment for writing the prompt—but it might also involve data extraction from various platforms, feeding back into the process. Finally, performance measures the success of the task. While the SDLC is well-established in development communities, the five Ps framework provides a versatile lens.

There’s substantial overlap ]]> full https://www.trustinsights.ai/wp-content/uploads/2024/04/In-Ear-Insights-Prompt-Development-Life-Cycle.png In-Ear Insights: Prompt Development Life Cycle false no 0:00 No no https://www.trustinsights.ai/blog/2024/04/in-ear-insights-4-power-questions-to-ask-generative-ai-language-models/?pk_campaign=feed&pk_kwd=in-ear-insights-4-power-questions-to-ask-generative-ai-language-models Wed, 10 Apr 2024 08:52:40 +0000 https://www.trustinsights.ai/?p=91432 In this week’s In-Ear Insights, Katie and Chris review the new Trust Insights PARE Framework and 4 power questions to ask generative AI language models when you’re working with them. These four questions will dramatically improve the performance of generative AI. Get the free download, no strings attached, at:

https://www.trustinsights.ai/powerquestions

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Christopher Penn 0:00

This week’s In-Ear Insights, I have something new Katie is something that I built because one of the things that we were talking about recently was making content more accessible to people, particularly useful tips and things.

Because as much as I like to geek out on the intricacies of like, the latent space and the language model, most people don’t care.

Most people like just just tell me what to do, within reason.

Is that a fair statement?

Katie Robbert 0:24

That is, I would say, that is completely a fair statement.

And that sort of, you know, that’s the challenge that I know that, you know, you run into, and I run into is that, you know, we want to make sure people understand the why.

And they want to know the why until we actually tell them, and then they’re like, great, I don’t care.

So they think they want to know, but they really just want to get to doing the thing.

And it does, it is a challenge because you and I are so like, it is so much in our DNA to really understand how the thing works.

Before we do the thing that we feel like we also then have to get everybody else on the same page as us before they can do the thing.

And they’re just like, great, what button do I push? Exactly.

Christopher Penn 1:05

So one of the most popular things that we’ve produced along those lines is our race strategy for prompting for generative AI, right, so roll action, context, execution.

And if you follow this framework for using gender of AI, it’s a good starting point.

It’s not by any means the be all end all the founding, but it’s a good place to start, especially if you’re new to language models.

https://www.trustinsights.ai/wp-content/uploads/2024/04/In-Ear-Insights-4-Power-Questions-to-Ask-Generative-AI-Language-Models.png In-Ear Insights: 4 Power Questions to Ask Generative AI Language Models false no 0:00 No no https://www.trustinsights.ai/blog/2024/03/in-ear-insights-ai-empathy-and-the-future-of-work/?pk_campaign=feed&pk_kwd=in-ear-insights-ai-empathy-and-the-future-of-work Wed, 27 Mar 2024 09:24:38 +0000 https://www.trustinsights.ai/?p=91385 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the potential impact of AI on empathy and society. You’ll learn about the risks when AI creators lack empathy and the importance of compassion in a tech-driven world. Discover how democratizing technology and prioritizing human concerns can combat these risks. Find out how you can be part of the solution and shape the ethical use of AI.

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about AI, specifically generative AI, and empathy.

And here’s, here’s the perspective that, Katie, I want your take on.

There was a statement on LinkedIn as a video of Sam Altman from OpenAI, talking about how the race is on in Silicon Valley, to who can be the first to build the first billion dollar company, with one employee, and no one else, everything else be handled by AI by machines, by AI agents, etc.

And that got me thinking, on the one hand, as a as a nerd, that’s cool, right as just just from the pure nerd perspective, that’s cool.

But on the other hand, it struck me that that was a absurdly non empathetic point of view, about how to use this technology to, to better humanity, which theoretically is open AI is mission, building a billion dollar company that employs no one, but you strikes me as I want to say selfish, but it is kind of selfish, in the sense of, like, yeah, I want all the money, and I don’t want to give it to anyone else.

And that, to me, is highlights something that we’ve seen for the last 30 years, but really badly in the last five, you look at papers and research on the topic of just the absolute decline in our overall ability to be to practice empathy, sort of an empathy deficit.

So what that taught me thing is, you have a bunch of people who are building these systems, who have no sense of empathy, and no sense of connection to like regular people.

And that does not bode well for how this technology is going to be used at a societal level.

What’s your take on this?

Katie Robbert 1:54

I mean, that’s a lot to unpack.

So, you know, I have a lot of questions, as you would imagine, number one, what kind of business? Are we talking about? You know, number two, you know, is this assuming that the one person who builds the business is the one person who’s goin]]> full https://www.trustinsights.ai/wp-content/uploads/2024/03/In-Ear-Insights-AI-Empathy-and-the-Future-of-Work.png In-Ear Insights: AI, Empathy, and the Future of Work false no 0:00 No no https://www.trustinsights.ai/blog/2024/03/in-ear-insights-generative-ai-limitations-in-marketing/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-limitations-in-marketing Wed, 20 Mar 2024 08:20:07 +0000 https://www.trustinsights.ai/?p=91371 In this week’s In-Ear Insights, Chris and Katie discuss generative AI limitations in marketing and why focusing on generative AI might not be the answer to your woes. You’ll learn the best ways to analyze your data to find the true cause of the problem and get actionable steps to turn things around.

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Christopher Penn 0:00

In this week’s In-Ear Insights, a follow up from last week’s episode on predictive analytics in generative AI, we got a comment on YouTube folks who said, Hi, I thought it was an interesting topic but didn’t get anything from the episode that was actionable to go experiment with, it’d be helpful to analyze an example of how to solve data, sell funnel leakage problems with generative AI, they give an example of a family law firm for traffic’s going up, and and prospects are going up.

But sales qualified leads and retainers has stayed flat.

And obviously, they’re kind of wondering, I’m guessing they’re kind of wondering why some of the funnels moving upwards, some of it isn’t.

So Katie, when we tackle problems like this for paying clients, we obviously spent a lot more time doing requirements gathering much more than you would get in a YouTube common.

If this person were to say, onboard as a client, what would our first steps be?

Katie Robbert 0:52

I would, I would probably run them through the five P’s first and sort of understand like, what is the purpose? Actually, no, I would step back from there probably and say, you know, I would want to undo? Well, gosh, it’s hard, because there’s so many things that I want to know more about.

And so let me see if I can sort of like restructure it.

So yeah, let’s start with the five piece, because that’s always a really good place to start.

And so, you know, what is the purpose? So the purpose sounds like they are trying to understand, you know, to your point, why some of the funnel is moving, and some of the funnel is stagnant.

And so, you know, so the question they want to answer is, how do I get the rest of the funnel to start moving again? The people so you know, are there salespeople involved? Are there content marketers involved? You know, whoever’s involved? What is the process? I think this to me sounds like where we woul]]> full https://www.trustinsights.ai/wp-content/uploads/2024/03/In-Ear-Insights-Generative-AI-Limitations-in-Marketing.png In-Ear Insights: Generative AI Limitations in Marketing false no 0:00 No no https://www.trustinsights.ai/blog/2024/03/in-ear-insights-predictive-analytics-and-reducing-churn/?pk_campaign=feed&pk_kwd=in-ear-insights-predictive-analytics-and-reducing-churn Wed, 13 Mar 2024 07:06:56 +0000 https://www.trustinsights.ai/?p=91351 In this week’s In-Ear Insights, Katie and Chris discuss predictive analytics and applying it to reducing churn throughout the customer journey. Learn what types of predictive analytics apply, how generative AI fits in the mix, and steps you can take to reduce churn.

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Christopher Penn 0:01

In this week’s in In-Ear Insights, let’s talk about reducing churn.

How do we reduce churn using things like predictive analytics and generative AI.

So to set the stage we should probably talk about what churn is churn is essentially when you lose customers faster than you can gain them.

So if you were a B2B company, you have subscribers, for example, with with Trust Insights, if you people were subscribed to our generative AI course, and it would happen if more people stopped being customers than we were able to acquire them.

If you’re a company like Agorapulse, it’s the number of monthly recurring revenues as customers you have in retail.

It’s how fast those customers churn, right? How fast do you lose customers? So Katie, when you think about churn as an executives, particularly a financial stakeholder, what is top of mind for you, obviously, you know, less is better.

But how do you think about attacking this problem? Oh,

Katie Robbert 0:56

it’s complicated, really? Well, because first, I mean, first and foremost, you have to recognize that it’s happening.

And a reduction in revenue is usually a good indicator.

But that’s not always apparent, because sometimes the churn happens in such a way that, you know, there’s like a lag between what you see in the numbers and the churn actually happening.

And so that’s where I like to pay attention to, I keep an eye on both top of funnel and bottom of funnel, because I feel like as the top of funnel, so that’s your awareness, that’s people coming to your website, for the first time, people may be checking out your product for the first time.

That’s, to me a really good indicator of if your overall churn bottom of funnel is going to go up or down.

And so if your top of funnel starts to reduce, then you can expect that trickle down effect for the bottom of the funnel.

So I like to ]]> full https://www.trustinsights.ai/wp-content/uploads/2024/03/In-Ear-Insights-Predictive-Analytics-and-Reducing-Churn.png In-Ear Insights: Predictive Analytics and Reducing Churn false no 0:00 No no https://www.trustinsights.ai/blog/2024/02/in-ear-insights-lessons-learned-from-social-media-marketing-world-2024/?pk_campaign=feed&pk_kwd=in-ear-insights-lessons-learned-from-social-media-marketing-world-2024 Wed, 28 Feb 2024 12:16:25 +0000 https://www.trustinsights.ai/?p=91326 In this week’s In-Ear Insights, Katie and Chris reflect on lessons learned from Social Media Marketing World 2024. With generative AI taking center stage both literally and figuratively, what does that mean for social media marketers? Tune in to find out!

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Christopher Penn 0:00

In this week’s In-Ear Insights we are back from days and days and days upon the road.

Katie, you were at Social Media Marketing World 2024 with me and I just finished the world tour going all over the place.

And we wanted to take some time to revisit some of the key takeaways and things that we experienced at Social Media Marketing World, particularly as the the landscape has changed so much.

The one thing that I noted that Social Media Marketing World, this is should be is and should be no surprise to anyone was like every other session was had something to do with generative AI huge surprise, right.

Yeah,

Katie Robbert 0:33

it was.

It was interesting.

I was.

I don’t know why I was surprised by that.

I think I was surprised.

Because you and I, Chris were in what was considered the AI track.

So there were speakers brought into the event specifically to talk about AI.

And then there was supposed to be the rest of the speakers talking about, you know, all of the different aspects of social media marketing.

And what I noticed instead was that every talk was about generative AI.

So then I almost kind of felt like, well, why are we here? And the reason we were there was to go deeper into generative AI and not sort of surface level it like the other speakers were going to do, because their focus was more of the social media tools and the tactics.

But I was a little concerned because of the potential conflicting messages, because of the overlap of sessions.

And because of how overwhelming it was, as an attendee, you had no breath, no break from hearing about generative AI, you walked out the door and somebody brought up generative AI, you went to a separate off site event? And oh, my god, somebody brought up generative AI? And can we please just stop talking about generative]]> full https://www.trustinsights.ai/wp-content/uploads/2024/02/In-Ear-Insights-Lessons-Learned-from-Social-Media-Marketing-World-2024.png In-Ear Insights: Lessons Learned from Social Media Marketing World 2024 false no 0:00 No no https://www.trustinsights.ai/blog/2024/02/in-ear-insights-predictive-analytics-if-youre-data-poor/?pk_campaign=feed&pk_kwd=in-ear-insights-predictive-analytics-if-youre-data-poor Wed, 21 Feb 2024 10:09:34 +0000 https://www.trustinsights.ai/?p=91296 In this week’s In-Ear Insights, Katie and Chris discuss how to do practices like predictive analytics and classical AI/machine learning when you’re data-poor. What data is available to forecast and work with? How do you create data when you don’t have it, and what strategic advantage might this confer? Tune in to find out!

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Christopher Penn 0:00

In this In-Ear Insights, what do you do with your analytics when you are a data poor? So this is a question that came up when I was on the road recently, I was in Canada at a at a speaking gig doing a talk on predictive analytics, which was kind of fun, because I’ve done that one in a while.

And one of the folks said, I love this, this is this is great.

This makes sense.

Canada as a whole is a data poor nation.

We don’t have the same federal agencies like the US does that provides all this great data.

And it’d be nice to be able to forecast, you know, this industry stuff.

What do we do when we’re data poor, not the individual company, but like the consortiums, the public agencies, they just don’t have data.

So Katie, what do you do when your data poor?

Katie Robbert 0:46

That’s such a good question.

Because I feel like, you know, we take for granted the fact that in the United States, we are not data poor.

If anything, we are overwhelmed with the amount of data out there, our challenge is finding the good quality data that we can really trust.

But there’s no shortage of data that we can use.

So I would say to someone who feels like they are data poor, the first place to start is to try to find something similar enough.

So you know, Can Can these Canadian resources use a proxy? So a proxy being like, can they look at European data or United States data or Australian data? Is it close enough? And so there’s, there’s obviously a lot to unpack with that.

But that would sort of be my first instinct.

You know, Chris, what would you say?

Christopher Penn 1:32

So, a couple of answers I gave was, one, there are data sources that are geographically agnostic that y]]> full https://www.trustinsights.ai/wp-content/uploads/2024/02/In-Ear-Insights-Predictive-Analytics-If-Youre-Data-Poor.png In-Ear Insights: Predictive Analytics If You’re Data-Poor false no 0:00 No no https://www.trustinsights.ai/blog/2024/02/in-ear-insights-generative-ai-impact-on-search-engine-marketing/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-impact-on-search-engine-marketing Wed, 14 Feb 2024 10:02:54 +0000 https://www.trustinsights.ai/?p=91295 In this week’s In-Ear Insights, Katie and Chris discuss generative AI impact on search engine marketing. What is​ the impact of services like ChatGPT, Google Gemini, Perplexity, and Microsoft Copilot on traditional SEO and search? Tune in to find out, and what steps you should take to prepare for the future.

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Christopher Penn 0:00

In this week’s in your insights, search is changing more people are using tools like Bing, Google Gemini or perplexity to do searches to ask language models.

Hey, what’s the best place to get a burger around here? Or how do I do this thing? And increasingly, these generative AI models powered by search databases are doing a better and better job.

This is changing the way people search.

This is changing this the SEO world in general.

And one of the questions that came up at a recent event I was at was, what do we do about this folks like random Rand Fishkin, and Andy Crestodina, and Aleta solace, are all talking about how users prefer generative AI searches over regular searches.

In many cases, because there’s less garbage, right, there’s less clicking through to 48 websites, you just get an answer, and maybe a few links to click, and it’s a better user experience plus a consent the size things that you might have to go to five or six search results to get.

And so the question to you, Katie, is, how do we think about the way search is evolving into general AI being the way people search? And what should marketers and company owners be doing to plan for a future where there’s even more intermediaries between you and the customer?

Katie Robbert 1:23

All right, so that was a lot of questions in one.

So let me take a step back for a second.

I’m not using generative AI to search.

And so when as you’re saying, like, everybody’s using it can edit this isn’t to be like a sarcastic thing.

But like, where’s the data on that? Who’s everybody? Like, is it? You know, is it you and other, you know, AI specialists, who that’s your world? So you’re feeling like everybody’s doing it? Or is there data that supports that says consumers are turning to ChatGPT and Google Gemini and Microsoft Bing? To do their sear]]> full https://www.trustinsights.ai/wp-content/uploads/2024/02/In-Ear-Insights-Generative-AI-Impact-on-Search-Engine-Marketing.png In-Ear Insights: Generative AI Impact on Search Engine Marketing false no 0:00 No no https://www.trustinsights.ai/blog/2024/02/in-ear-insights-data-privacy-and-generative-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-data-privacy-and-generative-ai Wed, 07 Feb 2024 08:58:53 +0000 https://www.trustinsights.ai/?p=91262 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the complexities of data privacy in the era of generative AI. They offer practical tips on safeguarding personal and company data, emphasizing the importance of understanding terms of service and the implications of sharing information online. You’ll gain insights into how different AI models handle data and the significance of opting for secure, private AI solutions for sensitive information. Tune in to navigate the challenges and opportunities presented by generative AI in a data-driven world.

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about data privacy and generative AI.

It is no secret that for many of the tools that are out there, those tools are looking for more data to train on to get better feedback from users to say this was a good response.

This was a bad response, sometimes with not dubious consent, but with consent that is deeply buried or implicit.

So for example, about a year ago now, x, the service formerly known as square changed his Terms of Service and said, Hey, we’re going to use your data to train our AI on and the only way to opt out is to cancel your account.

There are many, many other services out there, the golden rule that we have said for 20 years now is, if you are not paying, you are the product.

And that has never been more true in the age of generative AI.

So Katy, last week, you did a seminar with MarketingProfs.

And one of the questions folks had was like, what should I be thinking about? When it comes to data privacy and generative AI? So you want to recap sort of how things went with that in terms of your perspective on it? Yeah, absolutely.

Katie Robbert 1:06

And so it’s a really good question.

I’m glad that people are asking it.

Because if you’re not asking about your data, privacy and security, then to your point, Chris, you are the product.

So the question was around the GPT model, so ChatGPT, anthropic, Claude, so and so forth, and they were asking, you know, what do I need to know about privacy and data security? How do I protect my information.

And so it’s a layered response, because it really depends on your specific use case.

But at]]> full https://www.trustinsights.ai/wp-content/uploads/2024/02/In-Ear-Insights-Data-Privacy-and-Generative-AI.png In-Ear Insights: Data Privacy and Generative AI false no 0:00 No no https://www.trustinsights.ai/blog/2024/01/in-ear-insights-strategic-value-of-analytics/?pk_campaign=feed&pk_kwd=in-ear-insights-strategic-value-of-analytics Wed, 24 Jan 2024 09:32:33 +0000 https://www.trustinsights.ai/?p=91234 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the crucial role of analytics in digital marketing and how it’s often seen as the bearer of bad news. They explore the common pitfalls companies face when they only sporadically review data, leading to missed opportunities for timely adjustments. Katie emphasizes the importance of integrating a thoughtful and focused measurement plan from the onset of marketing campaigns. The conversation also delves into the challenge of making effective decisions when data is not utilized properly, highlighting the importance of iterative and agile program development for better outcomes. This episode offers valuable insights into transforming analytics from a dreaded task to a strategic asset.

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about analytics, the sort of forgotten redheaded stepchild of digital marketing, particularly in the era of generative AI, where everyone is always talking about it all the time.

One of the things that and this was something that Katie, you and I talked about at the end of last year, and we’re sort of setting the course for the company as a whole, as we’ve been doing.

analytics, I’ve been working in the analytics field since really 2005.

And it has always been the Department of bad news, right? It is, it is very rarely ever something people look forward to.

And a lot of stakeholders are in the mindset of, I only want to hear good news, I don’t want to hear things that are going to get me promoted, that are gonna get me a bonus, etc.

And a lot of the time analytics tells you what’s happening.

It doesn’t tell you why.

But it tells you what’s happening and a good chunk of the time, it’s not good news.

So it’s not the the crazy news that you would that people want.

How should companies how should marketing executives and absolutely, you know, executives in general, people like us, the the corner office, folks get to a mindset where yes, analytics will tell you, sometimes things are not great.

And see the value in bad news as well as good news.

Katie Robbert 1:18

I think it has a lot to do with the frequency of the information.

So if you are looking at the data every single ]]> full https://www.trustinsights.ai/wp-content/uploads/2024/01/In-Ear-Insights-Strategic-Value-of-Analytics.png In-Ear Insights: Strategic Value of Analytics false no 0:00 No no https://www.trustinsights.ai/blog/2024/01/in-ear-insights-unlocking-workforce-potential-with-generative-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-unlocking-workforce-potential-with-generative-ai Wed, 17 Jan 2024 08:53:40 +0000 https://www.trustinsights.ai/?p=91206 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss unlocking workforce potential with generative AI and the evolving landscape of artificial intelligence in the workforce. They delve into the capabilities and limitations of AI, contrasting its performance against human tasks and creativity. The conversation highlights the importance of human critical thinking, decision-making, and creativity in the era of AI. You will gain insights into how AI and humans can coexist in the workforce, maximizing the potential of both. This episode is a must-watch for anyone interested in the future of AI and its impact on jobs.

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about the workforce potential of artificial intelligence.

We spent a lot of time and I know I spent a lot of time personally digging into the technology, what it is and how it works.

And today, let’s talk about the applications.

How is this stuff being used? So Katie, you found some stuff from the folks over gardener? Want to recap what you found?

Katie Robbert 0:24

Yeah.

So over the weekend, I was scrolling through LinkedIn, like everybody does, when they’re, you know, waiting for their tires to be rotated, or, you know, in line at the grocery store.

And what caught my eye was someone posting about the Gardner, a new video that they posted, unlock the potential of your workforce with AI, from their think, again series, and that I was reading through the comments of, you know, the person who posted it.

And so the whole premise of this research, we have it up on our screen, if you’re not watching our podcast, is that they have from 2023 to 2033, their predictions of people versus AI, essentially.

So what tasks will AI outperform people? And what tasks do the human outperform the AI counterpart? And unsurprisingly, the thing that AI is going to do better than human.

At the top of the list is weather prediction.

Well, no kidding.

Our meteorologists have been wrong for years, I would be shocked if AI could start to get it right, given all the crappy data that we have to give it.

But then you start to get into things that are really around development versus human tasks. full https://www.trustinsights.ai/wp-content/uploads/2024/01/In-Ear-Insights-Unlocking-Workforce-Potential-with-Generative-AI.png In-Ear Insights: Unlocking Workforce Potential with Generative AI false no 0:00 No no https://www.trustinsights.ai/blog/2024/01/in-ear-insights-custom-gpt-considerations/?pk_campaign=feed&pk_kwd=in-ear-insights-custom-gpt-considerations Wed, 10 Jan 2024 09:33:38 +0000 https://www.trustinsights.ai/?p=91198 In this week’s In-Ear Insights, Katie and Chris talk through some of the considerations that Custom GPT makers and consumers should keep in mind as OpenAI’s Custom GPT Store opens for business this week. Learn what pitfalls to avoid!

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Christopher Penn 0:00

In this week’s In-Ear Insights gonna expand on a topic that we talked about before on our live stream.

And I also talked about an impersonal newsletter about custom GPT-3.

Because for those people who have built a custom GPT with OpenAI service, you got an email last week that said, Hey, we’re launching the GPT.

Store next week, if you’re interested in sharing your custom GPT that you built in the store, you know, there’s a little laundry list of to do items.

But this means that people who have built custom GPT is like, for example, the Katy GPT that we have built, you’ll be able to offer to the public a belief for money.

So Katie, are you excited to sell access to Katie’s UPC at become a digital a synthetic influencer? Absolutely

Katie Robbert 0:45

not? Well, you know, it’s fun.

On the one hand, I’m like, Yeah, everybody needs more Katie in their life.

On the other hand, my very pragmatic brain starts thinking through all the risks involved with offering up your custom GPT to people besides yourself.

So you know, I’ll be interested to see how this plays out.

I feel like it’s going to be a mess when it first rolls out, because we don’t know how other people have structured.

They’re accustomed GP Ts, and now they’re just going to be up for use.

So yeah, I think that there’s I think it’s an interesting idea.

But I think there’s a heck of a lot of risk involved with this kind of move from OpenAI.

Christopher Penn 1:27

I agree.

So I figured what we could do is talk through five qualifying questions to ask whether you should if you’re a GPT, maker, whether you should put yours up for sale? And more importantly, if you are an end user, should you even buy one of these things? And huge surprise, the five questions are based on the five key framework.

]]> https://www.trustinsights.ai/wp-content/uploads/2024/01/In-Ear-Insights-Custom-GPT-Key-Considerations.png In-Ear Insights: Custom GPT Considerations false no 0:00 No no https://www.trustinsights.ai/blog/2024/01/in-ear-insights-generative-ai-in-2024/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-in-2024 Wed, 03 Jan 2024 09:43:57 +0000 https://www.trustinsights.ai/?p=91191 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative AI in 2024 and the rapid advancements in generative AI over the past year, including significant developments in various AI models. They explore how these technologies impact businesses and the importance of understanding their capabilities and limitations. The conversation also delves into the role of data quality and organization in leveraging AI effectively and the potential risks and ethical considerations in AI implementation. Tune in to gain insights into navigating the evolving landscape of AI in 2024.

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Christopher Penn 0:00

In this week’s In-Ear Insights, Happy New Year, it is 2024.

We are almost a quarter of the way through the century, which is mind boggling when you think about it.

And the topic du jour unsurprisingly, remains generative AI and all the absolutely insane year that 2023 was just a quick recap.

We saw OpenAI go from GPT-3, 2.5 and ChatGPT to GPT-4 to four v four v and four turbo, so three major releases within a year including the ability for it to see as well as read.

We saw Google go from bar to bar to to Bard with Gemini, although most recent benchmarks is that Gemini is not any better than OpenAI is free ChatGPT With three GPT-3 point five.

We saw anthropic go from Claude declawed to to cloud 2.1, before the end of the year having the largest context window 200,000 tokens, although it is not great.

And we saw the open source community developing some models that are absolutely mind blowing going from Metis first Lama release in early 2020 to Loma two, and then Miss draw the French company going from nothing to Mr.

l seven to the to MC straw, that mixture of experts model which is industry leading performance for models that you can run on your laptop, which when you think about you don’t need a server room full of tools to have generative AI working for you.

On the image side, we saw stable stability AI go from Stable Diffusion to Stable Diffusion, XL to SDL turbo, all within a year’s time, which allows for now real time animation, which is mind bending, with video clips up to 10 seconds long.

And on the regulatory side, we saw the first actual legislation regulating the use of generative AI come from the EU, the EU is AI Act, which was passed at the very, very tail end of 2023.

The big revision there for marketers to pay attention to of course, is disclos]]> full https://www.trustinsights.ai/wp-content/uploads/2024/01/In-Ear-Insights-Generative-AI-in-2024.png In-Ear Insights: Generative AI in 2024 false no 0:00 No no https://www.trustinsights.ai/blog/2023/12/in-ear-insights-2023-year-in-review/?pk_campaign=feed&pk_kwd=in-ear-insights-2023-year-in-review Wed, 20 Dec 2023 09:00:25 +0000 https://www.trustinsights.ai/?p=91147 In this week’s In-Ear Insights, Katie and Chris look at 2023 in the rearview mirror in their 2023 year in review. What a year, from GA4 to Generative AI – what did we learn? What’s changed, what stays the same, and what can we look forward to in 2024? Tune in to find out!

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Christopher Penn 0:00

In this week’s In-Ear Insights, In-Ear Insights holiday special as we round out the last show of 2023.

And as we coast into 20/24, okay, it’s been a heck of a year.

What are the things that for you really stood out about the absolutely zany in this? That was 2023? We definitely

Katie Robbert 0:21

didn’t end where we started, for sure.

Well, you know, it’s funny because where we were, you know, so we can sort of go from a different few different layers.

In terms of Trust Insights, we started the year strong, doubling down on everything, Google Analytics 4, because Google Analytics 4 was the thing we were all talking about.

Everybody was nervous about it.

With Google had set these arbitrary, you know, deadlines and all kinds of stuff for like moving the data.

Now the data is not going to be available.

Now you have to use this new system.

So that’s we were all in on Google Analytics 4.

Fast forward 12 months later, and I don’t remember the last time we’ve talked about Google Analytics 4, with a client or just in a conversation, it’s been all generative AI.

Now here’s the interesting thing.

Generative AI is not new, but it’s new, in a mainstream way.

And like somewhere along the line, I would say maybe, end of q1 beginning of q2, people are like, well forget Google Analytics 4, what is this generative AI thing I need to worry about.

And now that is all consuming.

And that is all we talk about to the point where we’ve even pivoted the services in our own business to keep up with what people care about.

So sure, we still do Google Analytics, 4 audits, but primarily what we’re doing is educating on generative AI.

So that’s a big part of it.

The the industry that we’re in, mostly B2B marketing and other industries are completel]]> full https://www.trustinsights.ai/wp-content/uploads/2023/12/In-Ear-Insights-2023-Year-in-Review.png In-Ear Insights: 2023 Year in Review false no 0:00 No no https://www.trustinsights.ai/blog/2023/12/in-ear-insights-quality-quantity-productivity-and-generative-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-quality-quantity-productivity-and-generative-ai Wed, 13 Dec 2023 09:51:37 +0000 https://www.trustinsights.ai/?p=91106 In this week’s In-Ear Insights, Katie and Chris debate the importance of quality work vs. quantity work, measuring employee productivity, and how generative AI may impact the way you think about staffing and hiring.

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Christopher Penn 0:00

In this week’s In-Ear Insights, there’s a lot of talk right now about the impact that generative AI is having on tasks on careers and things like that.

And I wanted to take a step back today and talk about quality and quantity.

So Katie, as a people, managers, people expert, one of the things that we’ve always talked about in the past, when we had a much larger team, for example, was Yeah, sort of a, b, and c players.

But there’s nuance to that there’s people who are extremely skilled, right? And then there’s people who are highly productive.

And so I guess the question I have for you is, how do you find that balance is a a person who has B player skills, but a player reliability, better than someone who has a player skills, but C player reliability? The reason I asked that is because it is very clear that jannard AI is a big player for skills, right? It’s not a Pulitzer Prize winner, it’s not, you know, gonna be winning all this stuff right now.

But it is an A plus player on reliability, you go to it, you tell it the thing, and then seconds, boom, you’ve got your thing.

It may not be exactly what you want the first time around.

But it’s not like waiting for, you know, your your ghost rider to take six weeks to turn around a piece of content, even though they’re brilliant writer, but they take forever to get stuff out.

So how do you think about managing people with that reliability for skill? And then how do you see general AI impacting that?

Katie Robbert 1:25

So you asked a lot of questions in that intro, but you also answered a lot of your own questions in that intro, you may have not realized I will always choose reliability over ambition.

You know, and so when we talk about the A, B, and A, B, and C type players, so you have the A players who are basically you consider those, you know, your people who go above and beyond who are always looking for the next thing, the so that sort of like the good of the ]]> https://www.trustinsights.ai/wp-content/uploads/2023/12/In-Ear-Insights-Quality-Quantity-Productivity-and-Generative-AI.png In-Ear Insights: Quality, Quantity, Productivity, and Generative AI false no 0:00 No no https://www.trustinsights.ai/blog/2023/12/in-ear-insights-can-generative-ai-replace-the-ceo/?pk_campaign=feed&pk_kwd=in-ear-insights-can-generative-ai-replace-the-ceo Wed, 06 Dec 2023 09:14:19 +0000 https://www.trustinsights.ai/?p=91070 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris delve into the intriguing topic of artificial intelligence in leadership roles, specifically discussing whether generative AI can replace the CEO role. You’ll learn about the balance between automation and human leadership, questioning what roles can be automated and the implications of AI in management. They cover the emotional and practical aspects of leadership that AI might struggle to replicate, highlighting the unique qualities you bring to an organization as a human leader. Tune in to hear their insights on the future intersection of AI and executive leadership, offering you a thought-provoking perspective on how technology might reshape corporate management.

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Christopher Penn 0:00

In this week’s in ear insights, we’re going to chat a little bit of office space this week, if you haven’t seen the movie, it’s a classic from the 90s, highly recommended great popcorn movie, and specifically gratis channel, the Bob’s the consultants that come in to evaluate all of the employees, in the context of artificial intelligence now updated.

One of the questions the Bob’s ask every employee is So, Peter, what exactly would you say you do here? And this topic is from a comment I made on threads.

A little while ago, I said, when you look at the roles that are going to have partial or potentially full automation, with AI, the most expensive employee, at most corporations is the corner office, right? Particularly in publicly traded corporations, where you see, if you look at SEC filings, you’ll see the CEO makes like 880 times more than the lowest level employee in the organization.

And so if a company is thinking about using AI to save time, save money and make money.

One of the questions that I had is, is that role, something that could be when should be or should not be partially or fully automated? So and now generative AI sort of asking the question of the Bob’s, which is, what exactly would you say you do here? So Katie, as a CEO, admittedly, not a publicly traded CEO is making 880 times what the lowest paid employee makes.

But yet, what would you say you do here in the context of gender of AI? Is that is the role of a CEO in different organizations, something that can be partially or fully automated?

Katie Robbert 1:42

I mean, any? Yeah, I think any role has aspects of it, that could and should be autom]]> full https://www.trustinsights.ai/wp-content/uploads/2023/12/In-Ear-Insights-Can-Generative-AI-Replace-the-CEO.png In-Ear Insights: Can Generative AI Replace the CEO? false no 0:00 No no https://www.trustinsights.ai/blog/2023/11/in-ear-insights-social-media-marketing-basics-for-food-beverage/?pk_campaign=feed&pk_kwd=in-ear-insights-social-media-marketing-basics-for-food-beverage Wed, 29 Nov 2023 09:51:51 +0000 https://www.trustinsights.ai/?p=91012 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss social media marketing and rebuilding a restaurant’s social media presence. They focus on establishing basic consistency first before considering more advanced tactics. They cover the importance of predictability for building trust and meeting audience expectations. You’ll learn an effective approach for realigning your social media with your ideal customer journey.

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about block and tackle social media marketing.

Everyone in social media marketing has spent an awful lot of time focusing on the worst term to use since 2020.

of going viral, which, and instead, what does it look like to have a sustainable, not necessarily like doorbusters crazy social media program, but a program that does the block and tackle but delivers what the audience wants, and is something that is reliable and dependable.

So Katie, you’ve been doing a lot of work with a local food and beverage client.

And you’ve seen really good success from that block of tackle.

So talk about what the strategy is behind it, and then how you’ve been executing on that strategy? Yeah, absolutely.

Katie Robbert 0:47

So let me take a step back and give a little bit of context.

So this particular establishment is one that my husband and I have been going to for years, we’ve become really good friends with the owner and the general manager.

And just through the course of like, talking, just sort of checking in, I had asked them one day, a couple years ago, what was going on with their social media, because they had the post that we’re going out, we’re very confusing.

And full disclaimer, I am not a social media marketing expert.

But I have been doing it long enough to at least feel competent to do the basics.

And so I asked them, I was like, you know, why are you posting pictures of dishes that you don’t currently serve? And why is the copy weird gibberish haikus? Like what’s going on, just out of curiosity, and the general manager told me that they had hired an agency out of California.

So they’re ba]]> full https://www.trustinsights.ai/wp-content/uploads/2023/11/In-Ear-Insights-Social-Media-Marketing-Basics-for-Food-Beverage.png In-Ear Insights: Social Media Marketing Basics for Food & Beverage false no 0:00 No no https://www.trustinsights.ai/blog/2023/11/in-ear-insights-generative-ai-business-continuity-planning/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-business-continuity-planning Mon, 20 Nov 2023 15:08:46 +0000 https://www.trustinsights.ai/?p=90991 In this early episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative AI business continuity, the recent drama at OpenAI and what it means for businesses relying on their AI services. We talk about the importance of having backup plans and avoiding putting all your eggs in one basket when it comes to new, unproven technologies. We give tips for building business continuity plans for AI tools, emphasizing focusing on your core processes first before getting distracted by specific tools. We also discuss the pros and cons of some alternative generative AI options beyond the main players.

Normally, the show publishes on Wednesdays, but because of the Thanksgiving holiday and the timeliness of the news, it’s in your ears two days early this week.

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Christopher Penn 0:00

In this week’s In-Ear Insights, this is going to be sort of a top of the news attention piece today.

Over this past weekend, and this is being recorded in late November 2023, OpenAI.

Their board essentially staged a coup and ousted the CEO Sam Altman, and then appointed the CTO, Marathi as the interim CEO, which then had Greg Brockman, who was the chairman of the board, and also a member of the executive staff also quit the next day.

The fit the feedback from their largest investor, Microsoft and the Microsoft CEO, Satya Nadella.

They’ve given OpenAI $10 billion in funding and I’ve owned 49% of the company said, Guys, what are you doing? And there was a round of negotiations that fell through.

And so what has happened as of Monday morning after this, this insanity is that the board had instead of stepping down, which is what their largest investor was telling them to do.

The board has appointed the twitches, CEO as the CEO of that of OpenAI.

Sam and Greg took an offer from Satya Nadella and are now heading up a new division at Microsoft advanced AI research.

And it appears that anywhere from a third to a half of OpenAI, senior staff are going with them.

So you have this massive brain drain, that the the future of OpenAI itself is very unclear.

So that brings me, you know, that’s the sort of the topical news that brings me to the topic I want to talk to you about Katie, which is scenario planning and disaster planning are not just for natural disasters.

They’r]]> full https://www.trustinsights.ai/wp-content/uploads/2023/11/In-Ear-Insights-Generative-AI-Business-Continuity-Planning.png In-Ear Insights: Generative AI Business Continuity Planning false no 0:00 No no https://www.trustinsights.ai/blog/2023/11/in-ear-insights-custom-gpt-software-development-best-practices/?pk_campaign=feed&pk_kwd=in-ear-insights-custom-gpt-software-development-best-practices Wed, 15 Nov 2023 09:02:31 +0000 https://www.trustinsights.ai/?p=90975 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss Custom GPT setup, the pros and cons of using AI chatbots like ChatGPT to automate content creation, and best practices for training them effectively, such as the TrustInsights.ai 5P framework and the SDLC.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, the hot topic of the moment, is OpenAI new tools called Custom GPT-4.

These are essentially a version of ChatGPT that you can take and customize, you can give it your own custom prompts, you can give it your own data.

And you can have create these instances of ChatGPT, that you can then go eventually open assets and sell access to in the in the GPT store, which will be kind of like an app store.

A lot of people have been running full speed ahead trying to get these things up and running, I set up a couple of my own over the weekend just for fun, because that’s what I do on Saturday.

And there are some gotchas, there are some things that you need to know before you do this.

If you’re, if you’re doing more than just messing around.

If you’re just messing around with fine, have a great time.

If you are thinking about I want to use this for myself, in AD in production, I want to use it for work, I want to use this maybe to offer client access to or maybe to just sell on the store.

There’s some stuff that people aren’t thinking through because this is app development.

This is software development.

And as a result, it’s not just hey, let’s let the AI do it.

It and the built in tutorials that it has are not sufficient, either.

So let’s talk through Katie’s today, the three things that you need to do to make custom GPT work.

One would be user stories and the 5g process to be figuring out what your data is.

And three is diversifying the ideas that you have around it.

So when you hear software development, and you hear app development, Katie, what are the things that immediately springs to mind that people who are not software developers or project managers? What are the things they’re going to do wrong? First, everything, full https://www.trustinsights.ai/wp-content/uploads/2023/11/In-Ear-Insights-Custom-GPT-Software-Development-Best-Practices.png In-Ear Insights: Custom GPT & Software Development Best Practices false no 0:00 No no https://www.trustinsights.ai/blog/2023/11/in-ear-insights-add-ai-to-everything-ceo-ai-strategy/?pk_campaign=feed&pk_kwd=in-ear-insights-add-ai-to-everything-ceo-ai-strategy Wed, 08 Nov 2023 09:35:46 +0000 https://www.trustinsights.ai/?p=90957 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss CEO AI strategy and whether businesses should add AI to everything just because it’s trendy. They explain why following directives to “AI-ify everything” can backfire, and share a smarter approach using processes like creating user stories and the Trust Insights 5P Framework.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, is AI, like Nutella doesn’t go on everything.

Should it be in everything.

And there’s two different perspectives on this.

Katie, you saw something on? I think it was threads.

Right? The which is it? Which is a replacement for the long more on Twitter? What do you want to set the stage as to what it is that people are discussing about AI?

Katie Robbert 0:28

Yeah, absolutely.

So over the weekend, I was just sort of scrolling through.

And, you know, what’s interesting about threads is it’s a blend of personal and professional conversation, at least for me, I usually try to keep all my social platforms separate.

And this one, this one thread came up where the poster said, two of my friends at different companies reported leadership literally saying, quote, put AI in everything was the new mission, not based on what, on what it’s good for, what their products need, just AI a phi, everything because investors like it.

And I grabbed it, because, you know, we, Chris, you and I often talk about, you know, the prop, like the five P’s.

And, you know, I’ve mentioned that AI isn’t the solution to everything.

And so it’s a little concerning to me now that, you know, in very broad strokes, leaders are saying, without doing like a five p or something like that, they’re just saying, Put AI in everything.

And to me, that’s very much a panic move.

It’s a short sighted move.

And it’s also an ignorant move, because they don’t know what that means.

So where do you want to start with that, Chris?

Christopher Penn 1:44

So I sort of agree with that.

It here’s why not because investors like]]> full https://www.trustinsights.ai/wp-content/uploads/2023/11/In-Ear-Insights-ADD-AI-TO-EVERYTHING-CEO-AI-STRATEGY.png In-Ear Insights: Add AI To Everything? CEO AI Strategy false no 0:00 No no https://www.trustinsights.ai/blog/2023/11/in-ear-insights-predictive-analytics-productivity-and-management/?pk_campaign=feed&pk_kwd=in-ear-insights-predictive-analytics-productivity-and-management Wed, 01 Nov 2023 09:43:19 +0000 https://www.trustinsights.ai/?p=90936 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to better manage people and marketing through frameworks, predictive analytics, and anticipation. We talk about how anticipating questions, needs, and potential issues can help smooth communications between managers and employees. Chris shares how predictive analytics and AI assistants can help managers plan for situations and employee interactions. Katie suggests using frameworks to set context and clarify purpose when starting potentially tough conversations. We explore emotional barriers to anticipation and how to create psychological safety. Tune in to learn communication tips for improving management and marketing outcomes through anticipating needs.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, one of the most popular sayings when flying a plane is a three word catchphrase.

aviate, navigate, communicate, fly the plane, keep falling out the sky, navigate, make sure you go and we’re supposed to go and then communicate.

However, there are all these types of frameworks for other kinds of work management, SEO, content, marketing, etc.

So, Katie, when you think about the kinds of frameworks that are helpful to you, and particularly for managing people and for marketing, what is what are some that come to mind for you that would help you as a stakeholder, manage someone better?

Katie Robbert 0:40

You know, it’s funny, I, I don’t have one.

And that’s, that’s part of the problem.

So there’s, you know, there’s frameworks around, you know, communication in terms of is it helpful? Is it necessary? I don’t remember exactly what that one is.

But, uh, there’s a framework.

And, you know, it’s interesting, because it’s actually taught like, all the way down to like preschool and kindergarten, in terms of communication.

And it’s the idea is, if you have something to say, you know, is it relevant? Is it helpful? Is it necessary? Like, is it timely, and I forget what the acronym is.

But that’s a framework that has always stuck with me as an adult, that even down to, you know, kids were five years old need to learn or need to understand how to participate helpfully in a conv]]> full https://www.trustinsights.ai/wp-content/uploads/2023/10/In-Ear-Insights-Predictive-Analytics-Productivity-and-Management.png In-Ear Insights: Predictive Analytics, Productivity, and Management false no 0:00 No no https://www.trustinsights.ai/blog/2023/10/in-ear-insights-cmo-survey-ai-and-2024-marketing-budgets/?pk_campaign=feed&pk_kwd=in-ear-insights-cmo-survey-ai-and-2024-marketing-budgets Wed, 25 Oct 2023 08:37:51 +0000 https://www.trustinsights.ai/?p=90914 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the peculiar budget cuts that CMOs are making for 2024 marketing budgets and how it will impact marketing teams. We talk about the surprising reductions in spending for CRM, customer experience, and brand building. We analyze the disconnect between using AI to improve productivity while severely cutting staff. Katie and Chris examine the different types of creative thinking needed on marketing teams and the risks of letting go of divergent thinkers. We explain why AI alone can’t magically fix poor data tracking or replace most marketing roles. Katie and Chris provide helpful perspective on AI’s capabilities and limitations that decision makers should understand before slashing budgets. Tune in to gain insight into crafting budgets and strategies that balance AI and human skills.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about marketing, budgeting 2024.

And some very peculiar decisions that CMOs are making around their spending.

So Katie, the new cmo survey just came out not too long ago, according to what you were just saying.

And there’s a whole bunch of really interesting things.

In this biannual presentation, one of the ones that really stood out to me that I was just absolutely shocked at was the demos decided, in terms of where they’re going to be changing, they’re spending in 9%, reduction, CRM, okay, I can understand that a 90% reduction in customer experience spending, which seems counterintuitive since customer experiences while the less differentiators you have.

And then a whopping 43% reduction in spending on brand building, which to me, is quite possibly the stupidest thing you could do in 24.

So Katie, what were your takeaways? As you were looking through this deck, particularly on where it’s where people are spending their budgets for 2024? Well,

Katie Robbert 1:04

there’s a couple of things.

So it looks like budgets are budgets are basically still going down, but it’s dropped near pre COVID levels.

And this is what we’re talking three, four years ago now.

Budgets are getting slashed left and right.

I mean, we’re seeing it in the job market, we’re seeing people get laid off.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about adding to your marketing tech stack.

So your Mar tech stack is the collection of tools, software, services, things like that, that power, your marketing.

And lots of companies have lots of technology.

But not lots of companies have good governance around it.

In fact, even though the CMO spends more on technology services than the CIO does, the marketing function when it comes to technology tends to be a little disorganized.

And as a result, companies can end up spending a lot more and having lesser results from their marketing tech than they do the regular it.

In fact, a few years ago, Katie, you and I were working on a client, where they had, we had purchased three different instances of Salesforce that were not connected together, and were conflicting with each other.

And they lost like the a billion and a half dollars of pipeline was missing.

So we’re talking about changing up our own tech stack here at Trust Insights.

So Katie, you said you have some questions? What kinds of questions have you got?

Katie Robbert 1:06

Well, before I get into the specifics, you know, I think that one of the things that a lot of companies, you know, and you know I include us in this conversation can potentially do wrong, is just stacking, literally stacking more tools on top of the stack.

And so you get all of these layers of tools.

A]]> full https://www.trustinsights.ai/wp-content/uploads/2023/10/In-Ear-Insights-CHANGING-YOUR-MARTECH-STACK.png In-Ear Insights: Changing Your MarTech Stack false no 0:00 No no https://www.trustinsights.ai/blog/2023/10/in-ear-insights-marketingprofs-b2b-forum-2023-takeaways/?pk_campaign=feed&pk_kwd=in-ear-insights-marketingprofs-b2b-forum-2023-takeaways Tue, 10 Oct 2023 09:45:53 +0000 https://www.trustinsights.ai/?p=90872 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss key takeaways from the recent MarketingProfs B2B Forum conference. We cover topics like AI, digital marketing tactics, and generative content. Katie shares insights on non-AI focused sessions and the importance of remembering that technology is just a tool. Chris discusses legal implications around AI and copyright. We talk about the risks of over relying on AI, forgetting basic marketing skills, and striking a balance between technology and fundamentals. Overall, we provide an informative recap of B2B Forum while emphasizing the need to use AI wisely and maintain core competencies.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, we are back from a whirlwind tour at the MarketingProfs B2B forum and 2023.

Katie, you spoke, I spoke, and we had a bunch of really great sessions.

So let’s start off Katie with what are some of the things from the sessions and things you went to that you that you’ve learned some lessons learned, that you think are worth sharing?

Katie Robbert 0:23

I think the big thing, I mean, obviously, everyone is obsessed with generative AI right now, that was probably one of the larger themes that I noticed at the event, including with the opening remarks, and then the two follow up sessions to that we’re also focused on AI.

But the focus was more so how to make AI work for you not to be afraid of it and not to let it sort of take over everything.

As in, it’s just going to do your job.

No, it’s just going to do this.

But it was more around really understanding it.

That being said, because it was the thing that everybody was focused on.

The sessions that didn’t folk didn’t feature anything AI tended to be less attended.

Because everybody wanted to know the tools.

Everybody wants to know all the shiny objects and gadgets.

And so if I look objectively from the outside, I think that that is a risk.

Because the tools change so fast.

And it means that you’re not focusing on all of the other skills and things that go along with generative AI.

What was your takeaway?

https://www.trustinsights.ai/wp-content/uploads/2023/10/In-Ear-Insights-MarketingProfs-B2B-Forum-2023-Takeaways.png In-Ear Insights: MarketingProfs B2B Forum 2023 Takeaways false no 0:00 No no https://www.trustinsights.ai/blog/2023/09/in-ear-insights-how-to-vet-and-hire-an-analytics-agency-or-employee/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-vet-and-hire-an-analytics-agency-or-employee Wed, 27 Sep 2023 10:06:22 +0000 https://www.trustinsights.ai/?p=90846 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to vet and hire the right analytics expert or analytics agency for your business. We cover the importance of having your processes and goals clearly defined before bringing on help, so you can assess if a potential hire or agency is truly a good fit. We explain the types of strategic questions an analytics expert should ask in the interview process to demonstrate their experience. We also compare the pros and cons of hiring an individual contractor versus an entire analytics agency. Finally, we offer suggestions on evaluating skills and certifications when deciding between candidates. The key takeaway is doing your homework on your business needs first allows you to hire the right level of analytics talent.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s go back to our roots.

I know we’ve been talking a lot about artificial intelligence lately, but one of the things that is so important is things like AI feed off of the data we already have, right? We have analytics systems of all kinds.

We have Adobe analytics, Google Analytics pathoma, we have Salesforce and Hubspot, all these different marketing systems that contain tons of data.

It’s valuable data.

And very often, it’s data that is not in very good condition.

So sometimes we need to bring in outside help, we need to bring in a new team member, maybe a consultant, maybe an agency, and one of the questions that we get a lot is how do I know who to to bring on? And the sort of the unvoiced question is, how do I not know that they’re not full of as the Spanish what’s the excrement? Are they Toto?

Katie Robbert 0:55

I’ve heard that one in a while.

You know, and it’s such an interesting question.

And it it goes under, you know, why you should have a really strong hiring manager, which isn’t for this episode, but you know, for to get it to get a good understanding of what someone can and can’t do.

It’s not enough to just say, hey, so you’ve worked with data before, right? And they say, Yeah, of course I have you, you need to dig deeper.

And so the way that I like to approach that is to ask more sc]]> https://www.trustinsights.ai/wp-content/uploads/2023/09/In-Ear-Insights-How-to-Vet-and-Hire-an-Analytics-Agency-or-Employee.png In-Ear Insights: How to Vet and Hire an Analytics Agency or Employee false no 0:00 No no https://www.trustinsights.ai/blog/2023/09/in-ear-insights-generative-ai-and-the-future-of-work/?pk_campaign=feed&pk_kwd=in-ear-insights-generative-ai-and-the-future-of-work Wed, 20 Sep 2023 09:53:19 +0000 https://www.trustinsights.ai/?p=90831 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the impact of generative AI on businesses, especially hourly businesses like agencies. They explore the problems with time tracking and utilization rates, and how it breeds inefficiency. Katie shares her experience being forced to innovate on command. Chris talks about the importance of prompting generative AI with creative ideas. They discuss how to better align employee and employer incentives through value-based compensation. Ultimately, they conclude that the key is to leverage generative AI to automate repetitive tasks so people can focus on innovation.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about the impact that generative AI is having on businesses.

We know even just from what’s already happened in the last 18 months, that gender of AI obviously impacts, things like content creation, it impacts image creation, if you are running a clipart, or stock photography businesses is obviously a significant problem.

But over the weekend, and last week, I was chatting with some folks about hourly businesses.

And Katie, you and I, we worked at an agency for five ish years, and which is an hourly business with timesheets and time tracking and productivity and utilization and all that fun.

And I wanted to get your perspective on how you see things like generative AI impacting those businesses, because for a lot of C level folks, it’s a, it’s not just a technology thing, it is a this is going to fundamentally change the nature of your organization.

Katie Robbert 1:01

Well, let me take a step back for a second.

So you know, when we worked with the agency, we were definitely billing like that.

And then prior to that, when I worked at the other company, I was responsible for the overall product budget.

And every week when people would submit their timesheets, you know, I was me and the other product owners were chasing people around, trying to get them to reallocate their time, because when, you know, they didn’t know where to build their time, they would bill it to our products just as like, you know, general overhead, which would put all of our product budgets over.

And a lot of times, it was a software development team who were doing, you know, cleaning up technical debt, ]]> full https://www.trustinsights.ai/wp-content/uploads/2023/09/In-Ear-Insights-Generative-AI-and-the-Future-of-work.png In-Ear Insights: Generative AI and the Future of Work false no 0:00 No no https://www.trustinsights.ai/blog/2023/09/in-ear-insights-who-owns-your-data/?pk_campaign=feed&pk_kwd=in-ear-insights-who-owns-your-data Wed, 13 Sep 2023 08:22:28 +0000 https://www.trustinsights.ai/?p=90820 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the important issue of who owns your data. They talk about why companies need to be aware of who has access to their data, especially when using tools like Google Analytics or generative AI models. Katie and Chris explain how you may be giving competitors access to your data indirectly through models trained on your data. They offer suggestions for ensuring you maintain control and ownership of sensitive company data when leveraging new technologies like AI. Tune in to hear their examples and recommendations for governance, collaboration, and setting up the right data architecture.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about data specifically, who owns your data and who’s using your data.

We’ve had a couple of interesting things happen recently one, we’ve had one client who wants to do a specific kind of tracking on their website and Google Analytics 4 simply cannot do it, you would have to make some significant modifications to the code of your website to accommodate GA for as opposed to making the tool work for you.

That’s one aspect.

Second aspect ChatGPT recently announced its ChatGPT Enterprise Edition, which we’re pretty sure it’s going to be reassuringly expensive.

And the promise there is that your data will not be used by OpenAI to train their models.

This goes in tandem with a huge number of lawsuits and questions and things going on.

about who’s who owns the data that these these models are being trained on.

That also goes with questions we’ve had in our Slack groups like our analytics market, a Slack group, which you can get to at trust insights.ai/analytics, for marketers, someone asking, Hey, I have some interest in a custom model for this.

And our first question is, do you have the data because if you don’t have the data, you can’t tune a model.

And you probably don’t want to be making part of the secret sauce of your business based on data that you don’t own that you didn’t put into the model yourself.

So all of this to say that right now, companies are at an inflection point, if they if you don’t own your data, or at least a good portion of it.

You may be at risk not only from just not beyond do basic things like analytics tracking, but may not be able to take a]]> full https://www.trustinsights.ai/wp-content/uploads/2023/09/In-Ear-Insights-Who-Owns-Your-Data.png In-Ear Insights: Who Owns Your Data? false no 0:00 No no https://www.trustinsights.ai/blog/2023/09/in-ear-insights-the-c-suite-guide-to-generative-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-the-c-suite-guide-to-generative-ai Wed, 06 Sep 2023 11:05:02 +0000 https://www.trustinsights.ai/?p=90796 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris provide a guide to generative AI for the C-Suite and discuss how CEOs should approach understanding and using large language models and AI, starting with identifying business problems first before considering technology solutions.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this episode of In-Ear Insights, we are talking about large language models in AI, but specific to a corner office, the big chair, the CEO, and that, of course, Katie is you.

When you look at the landscape of all the things that we talk about in AI, and you think of it with your CEO hat on, what are the things that you as a CEO need to know about? In order in order to be effective in order to understand the opportunities, the risks stuff? What is what’s on your mind, when you think about all the stuff that I foam at the mouth that regularly?

Katie Robbert 0:43

You know, the first question I always ask is, tell me when I need to pay attention? And, you know, that’s a loaded question, because it depends on what the business is about what the CEO cares about.

So for us, you know, my questions more specifically would be, well, you know, we’ll generative AI, will large language models make our business better? What does that mean? Will we have a competitive advantage? If we integrate large language models into our business? What does that mean for my bottom line? Is there going to be a lot of cost upfront to get them set up? But then in the longer term, I’m going to save much more money than I invested into this thing? What does that mean? For me needing to skill up myself? What does that mean, for me needing to skill up my team? What kinds of resources do I need to consider? Are there legal implications for using this kind of technology? That’s a question I have about any kind of tech that we bring on of what are the legal implications? What are the security considerations? Do? You know we personally at Trust Insights don’t deal with HIPAA data protected health personally identifiable? We just that’s thankfully, not the nature of our business.

But those are, that doesn’t mean that I don’t need to be aware of what that means.

So I guess so to sort of sum up, the first question I would ask is, what information do I need as a CEO? Right now, today? What do I need to be paying attention to? Do I need to be paying attention to all the little startups who have their own ]]> full https://www.trustinsights.ai/wp-content/uploads/2023/09/In-Ear-Insights-The-C-Suite-Guide-to-Generative-AI.png In-Ear Insights: The C-Suite Guide to Generative AI false no 0:00 No no https://www.trustinsights.ai/blog/2023/08/in-ear-insights-gender-bias-and-fairness-in-generative-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-gender-bias-and-fairness-in-generative-ai Wed, 30 Aug 2023 10:42:29 +0000 https://www.trustinsights.ai/?p=90781 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to mitigate gender bias in AI systems by assessing risk, implementing human review processes, and building an equitable company culture.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about gender bias and fairness, specifically, in the context of artificial intelligence, it is no secret, obviously, that large language models and diffuser models both have biases in them on things like gender, and race or ethnicity, background, religion, etc.

Because these models are cobbled together from the entirety of the public’s content on the internet.

And the reality is, these are in many ways, mirrors of humanity itself, and humanity contains biases and things to look at when we talk about gender bias and fairness, if you wouldn’t mind start off, in your mind, what is fairness? What does that mean to you, particularly since you’re of a different gender than I am? And how do you see that manifesting in the uses of artificial intelligence.

Katie Robbert 0:58

So at its core, fairness, is giving equal opportunity to any one person to any one gender to any one background, ethnicity.

So Chris, if you and I were, you know,

both presented with a speaking opportunity.

To be fair, we would both be given equal, you know, opportunity to, you know, apply for it to, you know, when the opportunity, whatever the thing is, unfortunately, what tends to happen is an unconscious bias or conscious bias of, well,

Chris is a man and Chris has been doing this longer.

And Chris has data scientist in his title, so therefore, he must be more well suited for this.

And I’ve never heard of Katie, even though she’s spoken at a bunch of events and doesn’t have scientist in her title.

So I’m going to pick Chris, because I know him versus Katie.

So that’s sort of where the unfairness comes in.

When you start to create gender bias.

That’s when you’re like, well, this audience is going to be more men.

So they probably respond better to a man speaking versus a woman.

And that’s, and so and then therefore, Chris, youR]]> full https://www.trustinsights.ai/wp-content/uploads/2023/08/In-Ear-Insights-Gender-Bias-and-Fairness-in-Generative-AI.png In-Ear Insights: Gender Bias and Fairness in Generative AI false no 0:00 No no https://www.trustinsights.ai/blog/2023/08/in-ear-insights-analytics-use-cases-for-large-language-models/?pk_campaign=feed&pk_kwd=in-ear-insights-analytics-use-cases-for-large-language-models Wed, 23 Aug 2023 08:02:56 +0000 https://www.trustinsights.ai/?p=90768 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to leverage large language models like ChatGPT with your business analytics data. We talk through real examples of using AI to analyze CRM data for buyer personas, extract tables from PDFs, summarize PowerPoints into executive briefs, transform your data into actionable insights, classify social media comments, and answer questions about your data. We explain that while generative AI is exciting, your analytics data should still be the backbone of business decisions. Tune in to learn creative ways analytics and AI can work together to enhance your marketing strategy.

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Machine-Generated Transcript

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Christopher Penn 0:01

In this week’s In-Ear Insights, we have been talking and everyone has been talking about large language models, the tools that power things like ChatGPT, for ever and ever.

And one of the things that we want to be cognizant of, is not losing sight of the fact that the workhorse of machine learning and AI is typically around good old, almost boring analytics data.

How do we use these these newfangled fashionable tools with data that still runs our businesses? So Katie, when you think about analytics, and what’s the what are the kinds of data that you might want to be able to ask a language model about? And then we can talk through some of the use cases, I did a talk recently with our friends over at martec martec.org, about using language models with analytics data?

Katie Robbert 0:50

Well, I’m glad we’re talking about analytics data, because I feel like large language models and generative AI have overtaken the conversations in a way that we have been distracted and almost forgotten.

That things like our analytics data are what power the business are what help us make decisions, and that without it, these other pieces, the shiny objects are just that.

So when I think about analytics data, you know, obviously, I think about my website data, things like a Google Analytics, or Adobe analytics or a matomo, telling me what’s happening within my website in that ecosystem.

But then I also think about like my CRM data, what are people opening and what are people ignoring and what’s causing someone to unsubscribe or decide that they want to stick around.

And then the third piece is my sales and revenue data.]]> https://www.trustinsights.ai/wp-content/uploads/2023/08/In-Ear-Insights-Analytics-Use-Cases-for-Large-Language-Models.png In-Ear Insights: Analytics Use Cases for Large Language Models false no 0:00 No no https://www.trustinsights.ai/blog/2023/08/in-ear-insights-how-to-use-generative-ai-for-competitive-analysis/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-use-generative-ai-for-competitive-analysis Wed, 16 Aug 2023 07:49:11 +0000 https://www.trustinsights.ai/?p=90761 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how large language models with perfect memory can assist with competitive analysis. They explain how these tools can quickly synthesize vast amounts of public data to provide insights into competitors’ offerings, differentiation, and performance. Katie and Chris note the importance of keeping your own data fresh so tools have current info. They give examples of how to leverage these models, from summarizing competitors’ businesses to analyzing website design and content. Katie and Chris emphasize combining machine learning with human expertise to ask the right questions. They advise using AI for competitive analysis as one more tool, along with classic techniques like regression analysis.

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Christopher Penn 0:00

In this week’s In-Ear Insights, competitive analysis is nothing new, we have been doing competitive analysis since the first stall opened at a bazaar in the Middle East.

And when guys look at the other guy’s booth going, hey, those figures look better than mine.

However, we are in a totally new environment now, where you have massive databases, which are essentially what large language models are with perfect memory, meaning that they know so much about an entire landscape.

It with that perspective, knowing that these things have perfect memory and a much bigger point of view than any one person is going to carry around.

Katie, what do you think about how that changes? Competitive analysis? What do you think about how we should be thinking about these tools as a, as a assistant, to helping us be better at competitive analysis?

Katie Robbert 0:52

So John, and I were talking about this at a very high level on last week’s live stream.

And the thing that struck me was that there is an opportunity to think about how do we include artificial intelligence as a set of data points in the competitive analysis.

And so you know, so you have to step back first and figure out where artificial intelligence works in your organization.

And then what it’s actually doing.

And so, you know, for example, if we’re at a very high level, you know, let’s say Trust Insights is generating 90% of their content, using generative AI, does our main competitor want to be a]]> full https://www.trustinsights.ai/wp-content/uploads/2023/08/In-Ear-Insights-How-To-Use-Generative-AI-For-Competitive-Analysis.png In-Ear Insights: How To Use Generative AI For Competitive Analysis false no 0:00 No no https://www.trustinsights.ai/blog/2023/08/in-ear-insights-which-ai-tool-should-i-be-using/?pk_campaign=feed&pk_kwd=in-ear-insights-which-ai-tool-should-i-be-using Wed, 09 Aug 2023 08:10:02 +0000 https://www.trustinsights.ai/?p=90741 In this episode of In-Ear Insights, Katie and Chris discuss how to pick the right AI tools, answering the common question, “Which AI tool should I be using?” We explain starting with the problem, not the tool. Katie recommends the 5 P’s to evaluate AI readiness. We emphasize choosing AI intentionally, testing tools against requirements.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, one of the most common questions that we have all the time heard this a bunch at the most recent marketing AI conference, MAICON in Cleveland is the perennial.

Which AI tool should I be using? Which I actually will should I use? There’s so many of them.

There’s hundreds of new ones every single week.

So Katie, when somebody says to you, hey, which AI tool should I be using? What do you say to them?

Katie Robbert 0:25

I say that is absolutely the wrong question to ask.

I counter with, what problem are you trying to solve? Period? Not? What problem are you trying to solve with AI? So, you know, we’ve talked about this a few times AI, at least in this context is like any other shiny object technology.

Now AI definitely has staying power in terms of how it’s going to be integrated into our culture, our society our work.

But putting that aside for a second, if you are leading with, what problem am I trying to solve with AI? You’re already going to come up with the wrong answer.

And so people who are asking what tool should I use? It’s not going to solve a problem, it’s not going to integrate into their process nicely, because they first need to understand what is the question they’re trying to answer.

And so Chris, unsurprisingly, I’m gonna throw the five P’s at them.

I’m gonna say, what is the purpose? What is the problem you’re trying to solve? Who are the people involved? What is your existing process look like? What platforms are you currently using to solve the problem? And what is your measure of performance? How do you know you were successful in answering the question? And then you can take a look at all of those pieces, and figure out where AI fits in.

And then from there, you can start to look at okay, AI fits into this particular spot right here, what tools are available? That answer that question, and then you ca]]> https://www.trustinsights.ai/wp-content/uploads/2023/08/In-Ear-Insights-Which-AI-tool-should-I-be-using-.png In-Ear Insights: Which AI Tool Should I Be Using? false no 0:00 No no https://www.trustinsights.ai/blog/2023/08/in-ear-insights-marketing-ai-conference-2023-in-review/?pk_campaign=feed&pk_kwd=in-ear-insights-marketing-ai-conference-2023-in-review Wed, 02 Aug 2023 08:44:24 +0000 https://www.trustinsights.ai/?p=90718 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss takeaways from the recent Marketing AI Conference, MAICON 2023. We talk about the growth of the event, Chris’s generative AI keynote, and share highlights from other speakers on AI regulation, ethics, and impact on jobs. We debate whether agile development processes are still needed with AI. Katie challenges the idea that QA processes can be fully automated. We speculate on an artisanal future where automation and human craft co-exist. Join us for a lively discussion on where AI is taking marketing and business.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights we are back from MAICON, the marketing AI conference, the conference that we’ve been a part of since its very first event.

In fact, Katie, you and I spoke at the 2019, the inaugural event.

And this year, it was bigger and better than ever, with more than doubled the number of participants from the inaugural year, almost more than actually more than triple the number of people from last year.

And from what Paul Rhett’s or the founder was saying, they’ve had so much interest in everything they have to offer that they are bursting at the seams.

Because of generative AI.

Every talk was generative AI.

In fact, traditional machine learning stuff barely even mentioned was all general AI all time, mostly large language models.

I did the opening keynote, which seems to be well received and things but in terms of the themes, and sort of the big picture for where marketers think generative AI is really much I would say most of the crowd was still in the What is this stuff phase? Or how do I use this productively? Or is this going to be this you know, the singularity and Skynet? Sort of those?

Katie Robbert 1:18

Well, can I just first I want to say, you know how proud I am of Paul and Kathy and Mike and the rest of the team, because they have been beating this drum for years trying to get people to pay attention.

Even, I wouldn’t say even more so than you, Chris.

But just in a different like, I mean, they’re putting a whole event together, they’re building their whole company around marketing AI is literally called the marketing AI Institute.

And so to see them have their moment in the sun wa]]> https://www.trustinsights.ai/wp-content/uploads/2023/08/In-Ear-Insights-Marketing-AI-Conference-2023-in-Review.png In-Ear Insights: Marketing AI Conference 2023 In Review false no 0:00 No no https://www.trustinsights.ai/blog/2023/07/in-ear-insights-is-chatgpt-getting-dumber/?pk_campaign=feed&pk_kwd=in-ear-insights-is-chatgpt-getting-dumber Wed, 26 Jul 2023 09:50:57 +0000 https://www.trustinsights.ai/?p=90700 Since its release in November 2022, ChatGPT has been lauded as a groundbreaking AI chatbot. However, recent research indicates its capabilities may be declining over time.

In this episode of the Trust Insights podcast In-Ear Insights, hosts Christopher Penn and Katie Robbert ask the question: is ChatGPT getting dumber? They discuss findings that ChatGPT appears to be getting worse at certain tasks it previously handled well. This includes mathematical reasoning, code generation, and visual puzzles.

There is speculation that the declines are due to OpenAI opening up access to ChatGPT’s premium GPT-4 model, which overwhelmed their systems. The increased demand likely required reducing capabilities to manage traffic.

Whatever the cause, the changes have big implications for businesses relying on ChatGPT’s API in their products and services. When the AI model drifts substantially in just a few months, it can break assumptions made during development.

Penn and Robbert emphasize the importance of clearly defining your purpose and requirements for implementing AI. Is convenience more important or reliability? What happens if the system goes down or its capabilities change?

For mission-critical uses, they recommend exploring open source AI models you can run on your own servers. This provides more control and avoids being subject to vendors altering public APIs.

The key takeaway is to carefully weigh the tradeoffs and have backup plans in place when utilizing third-party AI services. Model drift may not matter for minor uses but could seriously impact products dependent on certain functionality. Do your due diligence upfront to prevent disruptive surprises down the road.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights ChatGPT has been the darling of everyone’s everything since November 22, when it first came out, and since then it’s gone through a number of evolutions from the original version to a new model.

And then to the big model that OpenAI came out with earlier this year, the GPT-4 model, which is supposedly the best in class, biggest, fanciest, it’s the it’s the Porsche 911 of AI models for large language models.

However, new research has come out that is corroborated in many ways by many people’s experiences that seems to be getting worse.

Over time.

It seems to not be as smart it seems to not be as as clever it seems to be running into more and more difficulties.

And a research paper came out recently that summarize]]> https://www.trustinsights.ai/wp-content/uploads/2023/07/In-Ear-Insights-Is-ChatGPT-getting-dumber.png In-Ear Insights: Is ChatGPT Getting Dumber? false no 0:00 No no https://www.trustinsights.ai/blog/2023/07/in-ear-insights-workplace-communication-and-productivity/?pk_campaign=feed&pk_kwd=in-ear-insights-workplace-communication-and-productivity Wed, 19 Jul 2023 10:40:14 +0000 https://www.trustinsights.ai/?p=90682 In this episode of In-Ear Insights, the Trust Insights podcast, Katie Robbert and Christopher Penn tackle a common workplace challenge – how to keep teams focused on priorities instead of getting distracted by drama or interpersonal issues.

As Katie explains, acknowledging that some level of gossip or emotional venting will happen is key. Trying to forbid it entirely tends to backfire. Instead, managers should set aside appropriate times and guardrails for indulging in that talk, while firmly redirecting it the rest of the time.

Chris and Katie discuss tactics like:

• Having regular check-ins on team expectations and ground rules
• Helping individuals pinpoint milestones and tasks to stay focused
• Setting boundaries on what is and isn’t appropriate for a manager to handle
• Respecting limitations and avoiding overstepping into therapy/counseling
• Bringing in HR or consultants when needed

The bottom line? Handling people is complicated. But with empathy, emotional intelligence, and healthy boundaries, managers can acknowledge feelings while keeping teams productive. This insightful episode is essential listening for anyone managing teams or dealing with workplace interpersonal challenges.

(Summary generated by AI)

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about communication and productivity.

One of the things that is constantly a challenge for managers and for individual contributors, I’m raising my both my hands on this one is staying focused on what’s important.

One of the things that I’ve observed in my work, and we talked about this on the, on the previous livestream about with Save Warrior Nun folks is getting people to focus on the things that are important that are going to move the needle and push the ball down the field, whatever analogy you want to use, and not get distracted by either shiny objects or things that are emotionally impactful, but not results impactful, you know, drama and gossip and the rumor mill things that you’ve experienced in the office all the time.

So Katie, when it comes to managing people, when it comes to being a good leader? How do you help people stay focused, particularly if you have all this emotionally interesting drama over here, but you have the dry but boring, but yet incredibly important results oriented work over here?

Katie Robbert 1:05

Well, I do h]]> full https://www.trustinsights.ai/wp-content/uploads/2023/07/In-Ear-Insights-Workplace-communication-and-productivity.png In-Ear Insights: Workplace Communication and Productivity false no 0:00 No no https://www.trustinsights.ai/blog/2023/07/in-ear-insights-what-is-threads-by-meta/?pk_campaign=feed&pk_kwd=in-ear-insights-what-is-threads-by-meta Wed, 12 Jul 2023 09:29:06 +0000 https://www.trustinsights.ai/?p=90647 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the latest buzz in the social media marketing sphere: Threads. Threads is a Twitter clone built on the Instagram database and architecture. It has experienced rapid growth, surpassing 100 million users in just a few days. Katie shares her frustration with Threads being tied to Instagram, as she treats her Instagram account differently from her Twitter account. The conversation revolves around the reasons behind Threads’ success, including its ease of use and the ability to import Instagram networks. Chris explains the concept of proximity of power, which Threads aims to replicate by providing users with the perception of being in close contact with influential figures. The discussion delves into the similarities and differences between Threads and Twitter, highlighting the lack of features such as desktop access, APIs, analytics, and advertising on Threads. Chris advises taking a cautious approach, maintaining both company and personal profiles, and cross-promoting Threads on Twitter. They also emphasize the importance of data privacy and being aware of the data being collected by Meta. The episode concludes with a reminder to experiment, track data effectively, and prioritize platforms that you own or have more control over.

Summary generated by AI.

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about threads.

The topic that everyone in the social media marketing sphere has been a buzz about for the last four or five days.

It is July 10.

As we were recording this, just yesterday, threads past 100 million users the fastest growth of any social media marketing app ever in history.

If you are unfamiliar, this is meta, formerly Facebook metas.

Twitter clone, if we’re perfectly honest, is a Twitter clone that is built upon the Instagram database and architecture.

So it is essentially Twitter.

But using Instagrams database and for people who have been active on Instagram, when you join threads, one of the things you’re allowed to do is immediately just copy over the people that you were following on Instagram into threads, which has given the app it’s remarkably rapid growth, it would not have achieved that level of growth otherwise.

So Katie, I saw that you have you have also joined threads? What’s your take on it? What’s your perspective on it? What are you thinking about?

https://www.trustinsights.ai/wp-content/uploads/2023/07/In-Ear-Insights-WHAT-IS-THREADS-BY-META.png In-Ear Insights: What is Threads by Meta? false no 0:00 No no https://www.trustinsights.ai/blog/2023/06/in-ear-insights-limitations-of-data-science-skills/?pk_campaign=feed&pk_kwd=in-ear-insights-limitations-of-data-science-skills Wed, 28 Jun 2023 09:56:00 +0000 https://www.trustinsights.ai/?p=90598 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the limitations of data science skills. They explore the various aspects of data science and what it truly means to be a data scientist. They touch upon the importance of understanding the scientific method and how it applies to data science. The conversation also delves into the misconception that data science is the sole focus of a data scientist’s work, highlighting the significant role of data engineering, data analysis, and programming skills. They emphasize that data science cannot exist in isolation and requires a strong foundation in other disciplines. The episode concludes with advice for individuals considering a career in data science, encouraging them to focus on their interests and strengths. Overall, the discussion sheds light on the complexity and interdisciplinary nature of data science, challenging common misconceptions about the field.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about the limitations of data science skills, what you can and can’t do with data science.

So, Katie, when you hear this topic, what is it that that comes to mind? What are the things that you want to know, particularly someone who is not by trade a data scientist?

Katie Robbert 0:19

You know, it’s interesting, because when I hear limitations of data science skills, I immediately think soft skills.

And I’m guessing you’re thinking hard skills.

And so I guess it’s a conversation, I guess there’s a couple of conversations we can have is, you know, what should and should you not use data science for? You know, do you need to be a data scientist to set up your Google Analytics? Probably not? Do you need to be a data scientist to set up your large learning model? Probably.

And so understanding the skill sets of a data scientist, but sort of like, when I hear the question, I’m thinking beyond the standard data science skill sets of, you know, is the limitation of a data scientist, that typically they’re not a great communicator, or typically, a data scientist isn’t a leader, which I know is not true.

But you know, it’s not the rule.

So that’s sort of what I’m thinking.

But what do you think of when you hear that question?

https://www.trustinsights.ai/wp-content/uploads/2023/06/In-Ear-Insights-Limitations-of-data-science-skills.png In-Ear Insights: Limitations of Data Science Skills false no 0:00 No no https://www.trustinsights.ai/blog/2023/06/in-ear-insights-how-to-market-boring-products/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-market-boring-products Wed, 21 Jun 2023 09:47:36 +0000 https://www.trustinsights.ai/?p=90595 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the challenge of marketing products and services that are considered boring or mundane. They emphasize the importance of identifying the right audience for such products and reframing the marketing approach to make it more appealing to that specific audience. They provide examples like industrial concrete and ball bearings, highlighting the need to showcase the practical applications and benefits of these seemingly dull products. Katie and Chris also discuss the significance of differentiation and storytelling in marketing, emphasizing the need to communicate specific value propositions and address the pain points of potential customers. They mention the Heaven and Hell exercise, where businesses should articulate the positive and negative experiences customers can expect from their product or service. They also stress the importance of understanding the customer’s perspective and stepping out of the curse of knowledge to develop effective marketing strategies. Tune in to the full episode for more insights and strategies on marketing “boring” products successfully.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, we as marketers love to tell great stories, we love to have a great product that is exciting, revolutionary world changing, you know, everything from cool AI tools to things like Apple’s new goggles, everybody loves to talk about a great product.

But sometimes a product is important, and is useful.

That is boring.

For example, industrial concrete, very hard to get excited about a new mixture of industrial concrete, even though it’s stronger, and it makes for safer bridges and buildings.

It’s tough to market a boring product.

So Katie, when you think about products and services that are commodities, products and services that are not the most exciting things, well, even things like Google Analytics, how do you? What do you do to help make a product less boring when you’re marketing it?

Katie Robbert 0:56

Well, and so I feel like it, the whole beauty is in the eye of the beholder kind of thing.

And so you might find industrial concrete to be incredibly boring.

But the right audience, the right market, engineers, contractors, construction workers, are going to be really excited to learn about these things, you know, c]]> https://www.trustinsights.ai/wp-content/uploads/2023/06/In-Ear-Insights-How-To-Market-Boring-Products.png In-Ear Insights: How to Market Boring Products false no 0:00 No no https://www.trustinsights.ai/blog/2023/06/in-ear-insights-buy-or-build-marketing-technology/?pk_campaign=feed&pk_kwd=in-ear-insights-buy-or-build-marketing-technology Wed, 14 Jun 2023 09:32:24 +0000 https://www.trustinsights.ai/?p=90592 In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the age-old question of whether to buy or build marketing technology. They explore various aspects of this decision, including business strategy, in-house skills, regulatory requirements, long-term considerations, and the value of owning versus renting. They also touch on the growing importance of large language models and AI in the marketing technology landscape. Tune in to gain insights into the factors to consider when deciding whether to buy or build a solution and how it aligns with your business goals and needs.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about buy or build.

Now, this is a topic that has come up many, many times over the years from the earliest days of whether you should even run your own email server, versus using a commercial service through marketing technologies, open source products, for example, like matomo, and Mautic, versus commercial properties like Hubspot, and even to the in artificial intelligence, which is where there’s a lot of discussion happening today with open source models like the llama family and the Mosaic Family versus commercial services like ChatGPT.

So the question is, should you buy? Or should you build? So Katie, in you’re looking at this from a governance perspective, a corporate responsibility perspective and a hey, that thing vanished, that I was paying for a perspective? What’s your take on buy versus build? Well,

Katie Robbert 0:51

unsurprisingly, there’s a lot of dependencies.

And one of those dependencies is your overall business strategy.

Where do you want to be with your business? You know, in a month, six months, two years, five years, and so that will start to educate everyone on whether or not you should build your own thing and own it? Or if it’s just a small part of moving the business forward, and it’s okay to buy it.

The second piece is, you know, do you have the skills in house? Or do you have to build out that skill set first to maintain this thing? And so on and so forth? It almost kind of sounds like we’re talking through the five P’s? What is the purpose? Who are the people? What is the process? What platform? And then how do you measure performance? It’s almost crazy how, you know, you can always tie it back to the five Ps.

S]]> https://www.trustinsights.ai/wp-content/uploads/2023/06/In-Ear-Insights-Buy-or-Build-Marketing-technology.png In-Ear Insights: Buy Or Build Marketing Technology? false no 0:00 No no https://www.trustinsights.ai/blog/2023/06/in-ear-insights-in-ear-insights-what-qualifies-someone-as-an-expert/?pk_campaign=feed&pk_kwd=in-ear-insights-in-ear-insights-what-qualifies-someone-as-an-expert Wed, 07 Jun 2023 09:49:31 +0000 https://www.trustinsights.ai/?p=90583 In this week’s In-Ear Insights, Katie and Chris discuss the six dimensions of expertise, and what constitutes someone being an expert or not. Learn what the 6 dimensions are and whether or not someone should be considered an expert.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, there is so much happening in the world so many places and things and events that people look to experts for guidance for help on how to do something, everything from your Google Analytics, stopping working on July 1 to the latest trends in AI, to whatever talking head is on your favorite news channel.

Today, let’s talk about what makes someone an expert.

I asked our preeminent in house experts, the GPT-4 model, what what things the average American considers when evaluating someone’s expertise and machines spit out this list of things.

And so Katie, I want to bounce this off to you and then get your take on whether or not think these things make someone an expert credentials in education.

Okay, experience, published works in media appearances, peer recognition, reputation, and alignment with trusted sources are the six things that the robots think make someone an expert to you? What makes someone an expert?

Katie Robbert 1:08

Well, when I started working, when I started my career, two of those things were the only things that mattered one was education, and credentials, and the other was peer reviewed.

To be clear, I started in academia.

And so if you had, you know, an MD, or a PhD, next year name, and you were published in a peer reviewed, you know, scientific magazine, then you were considered an expert, it didn’t matter.

If you actually were like, in doing the work every day, or if you were just theorizing about it, you were considered an expert if you had an advanced degree.

And this was something that I had to learn the hard way.

Because I was told that in order to move up in my career, I had to have advanced degrees.

So I bit the bullet, I went back to grad school, and very quickly realized that none of the information I was being taught was going to be helpful in my every day.

And so I now have a master’s degree that is completely useless to me.

https://www.trustinsights.ai/wp-content/uploads/2023/06/In-Ear-Insights-What-qualifies-someone-as-an-expert-.png In-Ear Insights: In-Ear Insights: What Qualifies Someone As An Expert? false no 0:00 No no https://www.trustinsights.ai/blog/2023/05/in-ear-insights-tiktok-and-marketing-mix-modeling/?pk_campaign=feed&pk_kwd=in-ear-insights-tiktok-and-marketing-mix-modeling Wed, 31 May 2023 12:19:12 +0000 https://www.trustinsights.ai/?p=90541 In this week’s In-Ear Insights, Katie and Chris review marketing mix modeling, media mixed modeling, and whether Tiktok’s Marketing Mix Modeling study has any insights marketers can learn from. Tune in to find out more!

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Machine-Generated Transcript

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Christopher Penn 0:00 In this week’s In-Ear Insights, suppose that you’re starting over or you’re trapped on a desert island, or any number of things where you don’t have all the resources that you’re used to for building and growing a business, what three things? would you say are absolutely essential to starting a business and or completely rebooting one, Katie, as our CEO? Where do we start? What’s its Day Zero of the next iteration of whatever? Where do we start? What are your three things?

Katie Robbert 0:32 I would start with users stories, I’m always I think I would always start there.

Because my first question is, what the heck am I doing? Why am I doing it? Who am I doing it for? You know, so I would say, number one, start with a user and a user story.

And I’ve written about this before, a user story can be a stand in business plan.

You know, if you don’t have a fully baked, you know, you know, deck and plan and all that good stuff that people expect you to have, you can have a user story.

So you can say, as a, you know, small business, I want to serve, you know, marketers who struggle with their analytics so that they can have better insights and focus on customer relationships.

That’s essentially what we do.

And that could be our whole business plan.

That’s our business model.

That’s our mission statement, that sort of becomes the driving force behind everything we do.

And so we have stated who we are, we’re a small business, we didn’t say we’re an enterprise sized business.

And so that sort of dictates sort of the next steps of how do we set ourselves up as a small business? I want to serve marketers.

So that’s our audience.

And so do I have that audience already? Do I need to go find that audience who are struggling with their analytics? So that’s a more specific type of audience.

So there’s marketers and then there’s marketers who are measuring and struggling.

So that and then here’s the services that we provide, so that they can focus on taking in taking action and customer relationship.

So that then says, Well, that means we’re going to handle the data.

So I would start there, start with my user story, because it’s going to tell me a lot about what I need to do next.

Christopher Penn 2:26 Okay, so that’s one th]]> full https://www.trustinsights.ai/wp-content/uploads/2023/05/IN-EAR-INSIGHTS-TIKTOK-AND-MARKETING-MIX-MODELING.png In-Ear Insights: Tiktok and Marketing Mix Modeling false no 0:00 No no https://www.trustinsights.ai/blog/2023/05/in-ear-insights-three-necessities-for-a-new-business/?pk_campaign=feed&pk_kwd=in-ear-insights-three-necessities-for-a-new-business Wed, 24 May 2023 08:28:53 +0000 https://www.trustinsights.ai/?p=90508 In this week’s In-Ear Insights, Katie and Chris explore the three necessities for starting a new business. What three things, if you had to start over from scratch, would you need to make your business successful? You’ll learn what those necessities are, why they matter, and how to apply them to your business, whether it’s new or not.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn 0:00

In this week’s In-Ear Insights, suppose that you’re starting over or you’re trapped on a desert island, or any number of things where you don’t have all the resources that you’re used to for building and growing a business, what three things? would you say are absolutely essential to starting a business and or completely rebooting one, Katie, as our CEO? Where do we start? What’s its Day Zero of the next iteration of whatever? Where do we start? What are your three things?

Katie Robbert 0:32

I would start with users stories, I’m always I think I would always start there.

Because my first question is, what the heck am I doing? Why am I doing it? Who am I doing it for? You know, so I would say, number one, start with a user and a user story.

And I’ve written about this before, a user story can be a stand in business plan.

You know, if you don’t have a fully baked, you know, you know, deck and plan and all that good stuff that people expect you to have, you can have a user story.

So you can say, as a, you know, small business, I want to serve, you know, marketers who struggle with their analytics so that they can have better insights and focus on customer relationships.

That’s essentially what we do.

And that could be our whole business plan.

That’s our business model.

That’s our mission statement, that sort of becomes the driving force behind everything we do.

And so we have stated who we are, we’re a small business, we didn’t say we’re an enterprise sized business.

And so that sort of dictates sort of the next steps of how do we set ourselves up as a small business? I want to serve marketers.

So that’s our audience.

And so do I have that audience already? Do I need to go find that audience who are struggling with their analytics? So that’s a more specific type of]]> https://www.trustinsights.ai/wp-content/uploads/2023/05/IN-EAR-INSIGHTS-THREE-NECESSITIES-FOR-A-NEW-BUSINESS.png In-Ear Insights: Three Necessities for a New Business false no 0:00 No no https://www.trustinsights.ai/blog/2023/05/in-ear-insights-will-ai-make-us-lazy-and-stupid/?pk_campaign=feed&pk_kwd=in-ear-insights-will-ai-make-us-lazy-and-stupid Wed, 17 May 2023 08:49:13 +0000 https://www.trustinsights.ai/?p=90498 In this week’s In-Ear Insights, Katie and Chris discuss a thought-provoking question raised during a recent talk: Will AI Make Us Lazy and Stupid? Is there a genuine risk with the use of AI? Will it make us lazy and reduce the quality of our work? We explore the impact of AI on human behavior and the potential consequences of over-reliance on automated systems. We delve into examples where AI can both improve and diminish service experiences, and the importance of strategic decision-making when implementing AI. Join us as we examine the complex relationship between humans and technology in the realm of AI. Watch the video to gain insights into the risks and considerations surrounding AI adoption in various contexts.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about people for a bit.

We’ve been talking about a lot of process, and certainly a lot of technology recently with things around artificial intelligence and all these crazy cool things it does.

But at a talk I was doing last week, one of the attendees asked, is there genuine risk with the use of AI? That it’s going to make us lazy and stupid and reduce the quality of work that we do as human beings? And will it basically make us as bad as untrained people? Because we’re going to be over reliant on the system.

So Katie, are we on a path to lazy and stupid?

Katie Robbert 0:43

I mean, here’s, here’s the thing, there is no path people are already lazy and stupid.

And the second they find even more excuses to be lazy and stupid.

They’re all over it.

And so there’s, you know, it’s the whole, there’s two kinds of people.

And I feel like artificial intelligence is not a new, it’s not solving a new problem, it’s just a new technology, you’re always going to have people who are looking for shortcuts, people who are looking for the easy way to do things, people who can do the least amount of work.

And so artificial intelligence is now just a new version of that.

And so if you have team members who are already prone to working that way, that’s it.

I mean, that’s just going to be a problem.

Humans, by nature, you know, if we don’t want to do something, we’re not going to do somethin]]> https://www.trustinsights.ai/wp-content/uploads/2023/05/IN-EAR-INSIGHTS-WILL-AI-MAKE-US-LAZY-AND-STUPID-1.png In-Ear Insights: Will AI Make Us Lazy and Stupid? false no 0:00 No no https://www.trustinsights.ai/blog/2023/05/in-ear-insights-what-is-social-scheduling-software/?pk_campaign=feed&pk_kwd=in-ear-insights-what-is-social-scheduling-software Wed, 10 May 2023 09:09:42 +0000 https://www.trustinsights.ai/?p=90486 In this week’s In-Ear Insights, Katie and Chris talk social scheduling software. What is social media scheduling software, how is it different than social media management software, and what are the things to look for when choosing a social scheduling vendor? Tune in to find out!

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about social scheduling, and specifically social scheduling tool.

These are tools that allow you to, among other things, schedule social media posts on major public social networks in advance so that you can schedule the next day’s content for next week’s content and not have to be sitting there at specific times of day, pushing the send button on your LinkedIn profile, your YouTube profile, your Tiktok account, and so on and so forth.

So, Katie, when you think about social scheduling, obviously, this is a very mature market, social media scheduling tools have been around for, what, 15 years now? Probably, yeah, about 15 years.

What are you What is it that that you find valuable about valuable about them? And what do you look for when you’re trying to figure out is this a good tool that I should use?

Katie Robbert 0:50

You know, it’s interesting, because social scheduling tools, they’re one of those kinda like, it’s a given that you would use one.

But I don’t think that that’s necessarily true.

And I think that the way in which they’re used, you know, is maybe underutilized a bit, I know that we under utilize our social scheduling tools.

And so what I look for is something that has an easy to use interface, something that’s easy to bulk upload.

And so this is sort of like, I feel like it’s different parts of the conversation, you know, how do you use it? And so for us, you know, we can curate, you know, upwards of 100 posts at a time of our own stuff, to then put into a scheduling tool.

But I also sometimes, like on a whim, just want to, you know, post something.

And so, do I go through a social scheduling tool? Or do I just post it natively to that particular platform.

And so these are the kinds of things that I feel like a social scheduling tool helps create more discipline, versus the random posting of a CEO, who’s just like, I feel like posting th]]> https://www.trustinsights.ai/wp-content/uploads/2023/05/IN-EAR-INSIGHTS-WHAT-IS-SOCIAL-SCHEDULING-SOFTWARE.png In-Ear Insights: What is Social Scheduling Software? false no 0:00 No no https://www.trustinsights.ai/blog/2023/05/in-ear-insights-what-is-b2b-influencer-marketing/?pk_campaign=feed&pk_kwd=in-ear-insights-what-is-b2b-influencer-marketing Wed, 03 May 2023 09:15:12 +0000 https://www.trustinsights.ai/?p=90453 In this week’s In-Ear Insights, Christopher Penn and Katie Robbert discuss B2B influencer marketing, which is becoming more prominent in the B2B marketing space. B2B influencer marketing is an endorsement where a B2B marketer uses their influence to endorse a product or service because of the reputation they have. Unlike B2C, B2B influencer marketing is less transactional and more about building trust. To use influencer marketing in B2B, it’s essential to start with purpose and find the right influencer who orbits around the ecosystem close enough that the work they do is complementary. When identifying influencers, laterals with a similar audience but not doing the same thing can be useful. It’s also essential to look for influencers whose audience is the decision-makers, which is who you are trying to reach. Tune in to learn more!

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about B2B influencer marketing.

So influencer marketing has been a hot thing since social media really came about.

Although the idea of people being influential and influencing decisions is as old as time itself.

B2B marketing has typically been a little further behind in B2C marketing on influences obviously, you’d have people like you know, Kardashian talking whatever random, you know, consumer goods that they they’ve been asked to do.

But B2B marketing is also seeing this become more prominent once just the domain of analysts like Gartner and Forrester and the Forrester Wave and stuff in G to crowd.

Now, individuals are participating as strong influences in a B2B influencer marketing.

So Katie, as the CEO of a B2B company, and as someone who is involved in the influencer marketing space in the social media space and things what what is B2B influencer marketing to you?

Katie Robbert 1:03

To me, it’s an endorsement.

It’s so you know, let’s say Chris, you and I didn’t work together.

But if you said, You know what this company that Katie has, they know what they’re doing, I would work with them.

That is you as a B2B marketer, using your influence to endorse my thing, because of the reputation that you felt because of the following that you have, you would be considered a B2B influencer in the analytics and data science and digital]]> full https://www.trustinsights.ai/wp-content/uploads/2023/05/IN-EAR-INSIGHTS-WHAT-IS-B2B-INFLUENCER-MARKETING.png In-Ear Insights: What Is B2B Influencer Marketing? false no 0:00 No no https://www.trustinsights.ai/blog/2023/04/in-ear-insights-what-is-a-large-language-model/?pk_campaign=feed&pk_kwd=in-ear-insights-what-is-a-large-language-model Wed, 26 Apr 2023 10:50:51 +0000 https://www.trustinsights.ai/?p=90437 In this week’s In-Ear Insights, Katie and Chris answer the big question that people are afraid to ask for fear of looking silly: what IS a large language model? Learn what an LLM is, why LLMs like GPT-4 can do what they do, and how to use them best. Tune in to learn more!

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn 0:00

In this week’s In-Ear Insights, today, we are talking all about large language models.

Now, the ones you’ve probably heard of most are models like GPT-3, point five, and GPT-4, which are from open AI.

But there are many of these things.

There is the GPT Neo X series from Eleuther.ai AI there stable LM from stability AI, there’s POM from Google.

So there’s many, many, many of these language models out there.

And today, we figured we’d talk about what they are, why you should probably know what they are, and maybe a little bit about how they work.

So Katie, where would you like to start?

Katie Robbert 0:35

I think we need to start with some basic definitions, because you just said a bunch of words that basically made my eyes glaze over.

And so I think we need to start first is what is a large language model.

And before you give me an overcomplicated definition, let me see if I can take a stab at this in a non like sort of technical see if I’m even understanding.

So my basic understanding of a large language model is that it is basically like, if you think of like a box or a bucket, and you put all of your content, your papers, your writing your text, your data, whatever, into that bucket, then that’s sort of like the house, the container for all of the language that you want to train the model on.

And so, you know, the more the more stuff you put in it, the bigger the bucket you need, hence the large because you can’t just have like, this tiny little handheld bucket have like two documents in it and say, Okay, that’s my large language model.

That’s not enough.

That’s not enough examples to give the model to train on, you need to keep giving it more information.

And the more information you put in the bucket, the more refined you can make the model.

Am I even close?

https://www.trustinsights.ai/wp-content/uploads/2023/04/IN-EAR-INSIGHTS-WHAT-IS-A-LARGE-LANGUAGE-MODEL.png In-Ear Insights: What Is A Large Language Model? false no 0:00 No no https://www.trustinsights.ai/blog/2023/04/in-ear-insights-ai-will-take-your-job/?pk_campaign=feed&pk_kwd=in-ear-insights-ai-will-take-your-job Wed, 19 Apr 2023 10:38:12 +0000 https://www.trustinsights.ai/?p=90429 In this week’s In-Ear Insights, Christopher Penn and Katie Robbert discuss the impact of artificial intelligence (AI) on jobs. They explore whether generative AI will take over jobs, especially for junior members of an organization. They discuss the aspects of jobs that AI can and cannot replace, such as repetitive tasks versus human creativity and accountability. They also examine the potential management and leadership problems arising from the inverted pyramid of employees that AI can replace, and the future of junior-level employees in organizations. Ultimately, they suggest that individuals should identify their interests and skills and explore how they can supplement and collaborate with AI, rather than seeing it as a threat to their job security. Watch the video to learn more about the evolving relationship between humans and AI in the workplace.

Disclosure: summary generated by AI from the show’s transcript.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about artificial intelligence, particularly generative artificial intelligence, and whether or not it will take your job and in the headlines and you look at how it’s being deployed, the answer increasingly seems to be, I will take your job, or at least a good chunk of it, particularly if you are more junior in an organization, although the technology is applicable to every level of the organization.

So Katie, how’s it looking for the CEO Trust Insights? Is AI going to take a job? Am I going to be reporting to Katy GPT anytime soon?

Katie Robbert 0:38

You know, it’s interesting, I think, in some ways you will be, I think there’s certain aspects of the job that I do, that artificial intelligence will take over.

So we were having a conversation with a colleague of ours last week, and he was asking if generative AI could create a crisis comms plan.

And so typically, those kinds of plans are created by more senior team members.

And the answer is, yeah, with enough information, then the artificial intelligence can create a plan, which means it can also create a marketing strategy, a business plan, all of those things that are typically reserved for, you know, the big brains of the company, I’m putting this in big, big, big air quotes.

Because it’s really, it’s not big brain stuff.

But the thing is that we typically think that only executives can do, artificial intelligence will]]> https://www.trustinsights.ai/wp-content/uploads/2023/04/IN-EAR-INSIGHTS-AI-WILL-TAKE-YOUR-JOB.png In-Ear Insights: AI Will Take Your Job false no 0:00 No no https://www.trustinsights.ai/blog/2023/04/in-ear-insights-what-is-generative-ai/?pk_campaign=feed&pk_kwd=in-ear-insights-what-is-generative-ai Wed, 12 Apr 2023 11:01:29 +0000 https://www.trustinsights.ai/?p=90408 In this week’s In-Ear Insights, Katie and Chris discuss generative AI, one of the three major branches of artificial intelligence. This includes tools like ChatGPT, Google Bard, and Microsoft Copilot. They start by defining artificial intelligence and the three big categories within it: regression, classification, and generation. Generative AI makes things and allows people to interact with artificial intelligence in a way that they don’t have to be an expert to do so. However, it can’t create something truly unique that has never been seen before, and it doesn’t do well with vagueness. The models are also being used unethically by creating misinformation, disinformation, and deep fakes at a massive scale to create the appearance of credibility. They advise to use generative AI ethically and be specific when using it.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about generative AI.

It is one of the three major branches of artificial intelligence.

And it is the one that people can’t stop talking about.

This is tools like ChatGPT, and Google Bard and Microsoft copilot.

So Katie, where do you want to start with generative AI?

Katie Robbert 0:21

Well, I think a general definition would probably be helpful, I think there’s a couple of things that we need to set the stage for, like, first of all, what is and what isn’t artificial intelligence? And then more specifically, what is generative AI? And so, you know, at a at a very, very, very, very high level, artificial intelligence is math.

It’s math equations that are, you know, learning and running.

But I think that there’s a misunderstanding that, especially with generative AI, as you are interacting with systems like ChatGPT, there is this feeling that it’s becoming sentient and alive and learning, and it’s actually talking to you as a person.

But really, it is just, you know, learning based on the information that’s been given.

So can you give us the two definitions? What is artificial intelligence in layman’s terms? And then what is generative AI?

Christopher Penn 1:22

Sure.

So artificial intelligence is an umbrella term, that is the discipline of trying to get computers to perform intelligence tasks that hu]]> https://www.trustinsights.ai/wp-content/uploads/2023/04/IN-EAR-INSIGHTS-WHAT-IS-GENERATIVE-AI.png In-Ear Insights: What is Generative AI? false no 0:00 No no https://www.trustinsights.ai/blog/2023/04/in-ear-insights-what-is-marketing-mix-modeling/?pk_campaign=feed&pk_kwd=in-ear-insights-what-is-marketing-mix-modeling Wed, 05 Apr 2023 11:06:36 +0000 https://www.trustinsights.ai/?p=90392 In this week’s In-Ear Insights, Katie and Chris talk through marketing mix modeling. What is marketing mix modeling? Why should we care about it? How does it work? You’ll learn the difference between marketing mix modeling (sometimes called media mix modeling) and attribution modeling. Tune in to find out more!

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about marketing mix modeling, also known as media mix modeling the sort of the the, what is it? Why Does anyone care about it? And what you should be doing to get set up for it.

So Katie, where do you want to start on this very automatic px named tech,

Katie Robbert 0:19

this year thrown a lot of tongue twisters at me this morning.

You know, I want to start with what it is because I feel like, at least for me, and you know, maybe I’m not alone in this, maybe I am, when you said that you wanted to start doing marketing mix modeling, I personally got a little confused, because there’s marketing mix modeling, which is a machine learning technique.

But then there’s also the marketing mix.

So let’s just start by breaking down the differences between the two things.

Christopher Penn 0:49

Sure.

The marketing mix is pretty straightforward.

That’s just what marketing channels are using.

So what strategies, tactics and channels are you using? And a lot of the time at companies? It’s kind of arbitrary, right? It’s like, oh, let’s do Facebook.

Oh, there’s Tiktok.

Let’s do Tiktok.

You know, that that sort of thing? Like, what are billboards? You know, should we do billboards of bus wraps or direct mails, and that’s the marketing mix.

Marketing Mix modeling is a statistical method to figure out of all these things you’re doing what’s working? And what should you be spending more money on? And what should you be spending less money on?

Katie Robbert 1:24

I’m trying to dust off the cobwebs in my brain, but isn’t the marketing mix the four Ps of Marketing, it’s the product placement price and something else promotion promotion.

https://www.trustinsights.ai/wp-content/uploads/2023/04/IN-EAR-INSIGHTS-WHAT-IS-MARKETING-MIX-MODELING.png In-Ear Insights: What is Marketing Mix Modeling? false no 0:00 No no https://www.trustinsights.ai/blog/2023/03/in-ear-insights-how-to-improve-prompt-engineering-with-the-software-development-lifecycle/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-improve-prompt-engineering-with-the-software-development-lifecycle Wed, 22 Mar 2023 11:11:01 +0000 https://www.trustinsights.ai/?p=90321 In this week’s In-Ear Insights, Katie and Chris talk through how to improve your prompt engineering for large language models like ChatGPT, GPT-4, and other services through the use of the software development lifecycle. Learn how to apply the SDLC to your individual work with AI tools, and why it’s so important.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, we’re talking about prompt engineering, which is the art and science of writing prompts to talk to large language model AI systems, and how prompt engineering is really actually, software development.

This past weekend, I was doing a bit of ruminating on it, and realizing that with things like Microsoft copilot, and Google palm and GPT, four, and all the galaxy of acronyms out there, when we roll out the ability for a, an office worker to talk to PowerPoint, or Excel with a prompt, and access to GPT, for the large language model and have it do something like create a PowerPoint presentation from this Excel spreadsheet, we are really talking about how a human being talks to a computer, right how human being gives instructions to a computer, which is software development.

So the art of prompt engineering is how we write those programs to talk to computers.

And now that this is coming to Microsoft Office and Google Docs and stuff.

Everyone, every employee who works in office productivity software, is going to become a software developer, right? Because when they write prompts, they’re writing software.

So Katie, I want to ask you about the software development lifecycle and how when you think about prompt engineering, how it’s going to evolve.

So as a refresher, for folks who don’t remember, the software development cycle looks like this.

Okay.

Do you want to step through this real quick?

Katie Robbert 1:32

Yeah, absolutely.

So the software development lifecycle is exactly that.

It’s a lifecycle.

And it’s meant to be repeated over and over and over again.

So you start with your purpose? What’s the question we’re]]> full https://www.trustinsights.ai/wp-content/uploads/2023/03/IN-EAR-INSIGHTS-IMPROVE-YOUR-PROMPT-ENGINEERING-WITH-THE-SOFTWARE-DEVELOPMENT-LIFECYCLE.png In-Ear Insights: How To Improve Prompt Engineering With the Software Development Lifecycle false no 0:00 No no https://www.trustinsights.ai/blog/2023/02/in-ear-insights-third-party-relationship-management/?pk_campaign=feed&pk_kwd=in-ear-insights-third-party-relationship-management Wed, 08 Feb 2023 13:52:27 +0000 https://www.trustinsights.ai/?p=90217 In this week’s In-Ear Insights, Katie and Chris talk vendor and partner management, aka third party relationship management. What is it? How do you choose vendors and partners who fit your requirements best? How do you position yourself as the vendor of choice? Tune in to find out!

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about third party relationship management.

Now, Trust Insights is very often a third party vendor or a partner to other companies.

And we in turn also have partners and vendors.

And in the current economic environment, particularly in B2B, and in tech, there’s tons of layoffs and things a lot of people starting up their own things, a lot of people trying to figure out what to do next.

A lot of companies are in a position of, hey, we just laid off everybody.

And now look, there’s still a lot of work to be done.

No one’s here to do it.

Well, I guess we better bring in some partners to do the workforce.

So Katie, for people who are particularly who are on the brand side, who suddenly find themselves with all the work, but none of the people, what should they be doing what should be thinking about when they bring vendors in, because I know the the instinct is crap, just bring some companies get the work done now.

And obviously, it’s like anything, you don’t want to bring in the first thing that comes along, like, you know, you’re the adoption agency for the for your dog, just grab the first dog, you see, you test out the different personalities, take it for a little walk and stuff and see if they gel with you.

When you have a when you’re evaluating a third party vendor of some kind, how do you take them for a little walk?

Katie Robbert 1:16

Well, let me backtrack a little bit and clarify that this is why I’m not allowed to go to the shelters and adoption agencies because I would just take the first one, and then the second one, and then the third one, and then all of them.

But, you know, when you’re bringing on a vendor, or an agency, or a contract, or any third party, that you have to do relationship management with you, really, it’s an interview, you know, and so, you know, Chris, you and I come from the ag]]> full https://www.trustinsights.ai/wp-content/uploads/2023/02/THIRD-PARTY-RELATIONSHIP-MANAGEMENT.png In-Ear Insights: Third Party Relationship Management false no 0:00 No no https://www.trustinsights.ai/blog/2023/01/in-ear-insights-secrets-of-the-c-suite/?pk_campaign=feed&pk_kwd=in-ear-insights-secrets-of-the-c-suite Wed, 04 Jan 2023 13:25:33 +0000 https://www.trustinsights.ai/?p=90104 In this week’s In-Ear Insights podcast episode, Katie and Chris talk about one of the secrets of the C-Suite, based on a recent LinkedIn article. What does one set of executives do to be more productive that others don’t, and what lessons we can learn from it? Tune in to find out!

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Machine-Generated Transcript

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In this week’s In-Ear Insights, it is a new day, a new year, and perhaps a new view. Who knows? And as part of that, Katie, you wanted to talk about a piece of content you saw on LinkedIn called ‘Dirty Secrets of C-Suite Women’. The summary being that two-thirds of people who identify as women at the manager level or above use assistance of some kind – house cleaners, nannies, etc., even if they don’t want to admit it. So, A, what makes this a dirty secret? And B, it seems like there’s more than one angle of discussion on this topic in general?

Katie Robbert: Yeah, I saw this article last week and I had strong reactions to it. My first thought was ‘why should we have to do it all?’, why am I the one who’s responsible for doing every single one of those things, by myself, with a smile on my face, without complaining? And the answer is no, I can’t do all of that. There are not enough hours in the day. And so, people often say things like ‘you have the same 24 hours as Beyonce has, for example.’ Well, Beyonce also has a team of people around her doing things like grocery shopping, cleaning her house, nannying her children, driving her around, and keeping her schedule. She is the epitome of what we’re talking about, she is able to focus on the thing she does best – entertaining. So the same should be true of any human, but in this case, women in a C-Suite position. Why can’t we hire help to clear our time so we can focus on what we should be focusing on?

So I started to think about it in the context of ‘okay, if we really break it down, it’s just like outsourcing anything else?’ If I don’t have time to make coffee, I go to the coffee shop, should I be shamed for having a barista make me a coffee? No, millions of people do it every single day, regardless of what role they are in. If the guy who cleans my gutters stops at Dunkin Donuts to get coffee on his way to my house, is he being shamed for not being able to make his own coffee? Absolutely not. He’s just getting coffee. If I have someone walk my dog while I’m out of town, am I being shamed for not being in two places at once? Yes, it’s a total double standard.

So, Chris, my first question to you is, have you ever been asked questions along those lines as a man? How do you balance it all – a family, a career, everything?

Christopher Penn: Not w]]> full https://www.trustinsights.ai/wp-content/uploads/2023/01/SECRETS-OF-THE-C-SUITE.png In-Ear Insights: Secrets of the C-Suite false no 0:00 No no https://www.trustinsights.ai/blog/2022/11/in-ear-insights-how-important-is-accuracy/?pk_campaign=feed&pk_kwd=in-ear-insights-how-important-is-accuracy Wed, 30 Nov 2022 09:11:36 +0000 https://www.trustinsights.ai/?p=89870 In this week’s In-Ear Insights, Katie and Chris tackle how to present analytics and data to stakeholders in a world where opinion and emotion increasingly triumphs over data and basic facts. How important is accuracy? How do we stay true to our commitments to be data-driven when decisions are made with emotions? Tune in to find out.

Key takeaways:

– The world is becoming more post-factual, meaning that people are increasingly basing their decisions on emotions and opinions, rather than facts and data. – This makes it harder for data-driven marketers to provide value, as people are less likely to take action based on accurate data. – One way to combat this is to help people understand the emotional state they will be in with or without the accurate data. – Another way is to make data-driven reports more appealing, by telling the full story of why and how certain decisions will impact the business in the long term. – Finally, it is important to continue to provide accurate data, even in a post-factual world, as this is the foundation upon which sound decisions can be made.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about accuracy, factual data and reality.

One of the things that Jay Baer said recently in one of his newsletters was that we now live in a post factual reality.

His explanation was that we live in a world where facts and data don’t matter to an increasing number of people who, for whom opinion and emotion are much more important.

He points out, of course, the usual things in politics and stuff.

But even just agreement or disagreement about basic scientific facts, is on the table.

So this got me thinking, Katie, and I want your opinion on this as somebody who is both more emotionally mature than me.

Katie Robbert 0:50

Throw out the big guns today.

Christopher Penn 0:53

And add rooted in in the work that we do.

How important is accuracy and data and facts from from the perspective of the work that a company like Trust Insights does when we think about the reports that we produce for clients, for example, we know some of our clients don’t use some of our reports, they just kind of wing it.

And I know we’ve talked in the past about it because the reports not useful.

But I got Jays newsletter ]]> full https://www.trustinsights.ai/wp-content/uploads/2022/11/HOW-IMPORTANT-IS-ACCURACY.png {PODCAST} In-Ear Insights: How Important Is Accuracy? false no 0:00 No no https://www.trustinsights.ai/blog/2022/11/in-ear-insights-what-should-my-salary-be/?pk_campaign=feed&pk_kwd=in-ear-insights-what-should-my-salary-be Wed, 23 Nov 2022 09:37:20 +0000 https://www.trustinsights.ai/?p=89862 In this episode of In-Ear Insights, Katie and Chris answer one of the most common questions for jobseekers: “What should my salary be?” Learn the two different methods for determining how much compensation to ask for, and ways to handle salary negotiations in interviews.

Key points from this episode:

1. The first step in determining your salary is to assess your monthly expenses and calculate your monthly target income after taxes.

2. The second step is to research the average salary for your position and location using a tool like salary.com.

3. The difference between your monthly target income and the average salary for your position is your negotiating zone.

4. It is important to be prepared to negotiate your salary in a job interview, as this can be a very intimidating thing.

5. Consider using data to back up your negotiation, as this can be very helpful in getting the salary you deserve.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about what salary should you ask for it? This is a question that came up in the previous so what episodes you might check out that out on the YouTube channel.

But let’s let’s dig into how do we even know what kind of salary to ask for? So Katie, on the show, you want to recap what you and John have talked about, in terms of where where you’d started to think about compensation.

Katie Robbert 0:25

Yeah, so the so what episode Chris, that you’re referencing, John and I did last week, which was the 17th of November, if you’re looking for that specific episode, it was, you know, how to determine red flags for from a potential employer.

And one of the things that came up was if they’re not willing to discuss the salary range with you like, so if you’re talking with the hiring manager, and they said, Well, I’m not authorized to discuss salary with you, or I can’t tell you what other people at your level are making competitively, then that’s a huge red flag, because I hate the term in this day and age, but in this day and age, salaries no longer as taboo or confidential.

It’s pretty wide open in terms of it’s pretty transparent in terms of like, here’s what I make, here’s what you should be making.

And there’s really no rules around when it can and cannot be discussed anymore.

You know, I feel like it’s a very outdated, antiquated, you know]]> full https://www.trustinsights.ai/wp-content/uploads/2022/11/WHAT-SHOULD-MY-SALARY-BE.png {PODCAST} In-Ear Insights: What Should My Salary Be? false no 0:00 No no https://www.trustinsights.ai/blog/2022/11/in-ear-insights-how-to-adapt-for-the-cookieless-future/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-adapt-for-the-cookieless-future Wed, 16 Nov 2022 09:04:49 +0000 https://www.trustinsights.ai/?p=89845 In this week’s In-Ear Insights, Katie and Chris tackle the question on every advertiser’s mind: how to adapt for the cookieless future? What should we do about the cookieless future? What things should marketers know – what’s going away? What’s still available? What strategies should marketers pursue to no longer rely on third party data about customers? Tune in to find out!

Key points from this episode:

– The cookieless future refers to the declining use of third party cookies by marketers, due to privacy regulations. – Without cookies, marketing agencies will no longer be able to retarget or collect data from users without their consent. – To adapt to the cookieless future, marketers should create things that people are willing to sign up for, and focus on driving awareness. – A cookieless future presents an opportunity for contextual advertising, which does not require personal identifying information. – To avoid a cookieless future, companies should create good quality content and be honest with their customers about how their data will be used.

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Machine-Generated Transcript

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## Christopher Penn 0:00 In this week’s In-Ear Insights, let’s talk about the cookieless.

Future and how you as a marketer should be adapting to it.

So Katie, where do you want to start with the the declining use of third party cookies by marketers and the associated ad tech that goes with it?

## Katie Robbert 0:20 I mean, I think that in itself is a great place to start.

So just sort of like a quick recap as to what the cookieless future really means.

You know, my understand, excuse me, my understanding is that basically, privacy regulations have become stricter in terms of people just not wanting their data to be shared with any old person, any old company.

So basically, marketing agencies, marketing companies that have been reliant on cookies to retarget.

And sort of collect a certain amount of data from users basically, without consent.

It’s not that it’s without consent, but consumers aren’t super savvy as to where cookies are, what kind of data they collect.

And so moving forward, those cookies will no longer be available for marketers to use in terms of targeting their audience by interest by other websites that they visited, and collect any kind of identifying information.

And so that’s sort of in a nutshell, what the future of mercury marketing cookies would look like, third party cookies.

And so now comes the question of, well, what are marketers supposed to do?

## Christopher Penn 1:36 And what are what are they supposed to do?

## Katie Robbert 1:39 Well, you know, cry ]]> https://www.trustinsights.ai/wp-content/uploads/2022/11/HOW-TO-ADAPT-FOR-THE-COOKIELESS-FUTURE.png {PODCAST} In-Ear Insights: How To Adapt For The Cookieless Future false no 0:00 No no https://www.trustinsights.ai/blog/2022/11/in-ear-insights-how-to-customize-content-without-the-data/?pk_campaign=feed&pk_kwd=in-ear-insights-how-to-customize-content-without-the-data Wed, 09 Nov 2022 08:27:35 +0000 https://www.trustinsights.ai/?p=89808 In this episode of In-Ear Insights, Katie and Chris talk about personalization and customization of content. In an era when privacy regulations are throttling data marketers can obtain and the media environment is in total chaos, how do we still manage to customize and personalize?

In order to customize your content without data about people, you need to be willing to talk to people and collect feedback about their likes, dislikes, and needs. This takes time and effort, but it is the only way to truly personalize content for your audience.

1. Companies need to prioritize collecting customer data in order to personalize content effectively. 2. Behavioral data can be used to identify which pages nudge people towards conversion the most. 3. Asking people directly is always the best way to get data about how to personalize content.

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Machine-Generated Transcript

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## Christopher Penn 0:00 In this week’s In-Ear Insights, privacy regulations are cracking down social media is in total disarray.

There’s chaos and controversy everywhere.

And yet, we are still being asked as marketers to be able to offer personalization customization provide a, a single view of the customer show a great customer experience.

But our ability to rely on on data that is clean that is working, that is useful continues to diminish.

So cainy, how do we customize our content, without data about people to build on that?

## Katie Robbert 0:41 Well, I think that we should, you know, be clear about what that means.

And so, you know, this is something that, you know, I’ve always said, like, you know, the cookieless future, it’s going to be a problem for like, the lazy marketers who only rely on that information.

But if you are talking to your customers, if you’re doing feedback surveys, if you’re mining your CRM data, for support information, then you’re not going to have a problem.

And so when we talk about the data, the data exists, it’s just a matter of whether or not you’re going to put in the effort to from people.

And that I think is then becomes the difference.

So if you want to, you know, customize your content.

So we as consumers, now with, you know, the where technology is, have this expectation of personalization of everything, we don’t want to give up our data.

But somehow, we also want to make sure that it’s an experience that’s unique to only us.

And so the way in which that happens is by the consumers being willing to give feedback about themselves, what they care about what they like, what they don’t like.

And then yo]]> full https://www.trustinsights.ai/wp-content/uploads/2022/11/HOW-TO-CUSTOMIZE-CONTENT-WITHOUT-THE-DATA.png {PODCAST} In-Ear Insights: How to Customize Content Without the Data? false no 0:00 No no https://www.trustinsights.ai/blog/2022/11/in-ear-insights-can-i-believe-what-i-read/?pk_campaign=feed&pk_kwd=in-ear-insights-can-i-believe-what-i-read Wed, 02 Nov 2022 05:56:53 +0000 https://www.trustinsights.ai/?p=89775 In this week’s episode, Katie and Chris tackle news media’s usage of data. Is what you read believable? How would you go about proving it? We examine some recent claims in Bloomberg and Business Insider about racial slurs on Twitter and the process for verifying that claim, extending it to the role data-savvy organizations should play in ESG and social good. Tune in to learn more!

Key points:

There is still merit to the idea of the citizen analyst, but it is more difficult to achieve in the post-factual world. People are more likely to share information without fully reading or understanding it.

If you see data being used heavily in a news story, your first step should be to validate whether or not the news source itself is trustworthy.

If you have the capability, or if you have friends or colleagues who have the capability, it might be worth commissioning your own extraction and analysis of the data to see if the news story is accurate or even close to accurate.

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, can you believe what you read in the news? So this past weekend, there was a news story that I saw in I think was either Bloomberg or Business Insider saying that on Twitter in particular, the increase in the usage of various racial and and other slurs was up.

I think the new stores had 500%, since the takeover of Twitter and new under under the new management.

And as soon as I read that, I said, is that true? Like? Could we verify it? Because see, yeah, I mean, sometimes you see a lot of stuff in the news being very performative sort of click Beatty stuff.

And so I asked the question, is that true? So Katie, when you see a story in the news, particularly story where there’s a big data point as the headline, what do you think?

Katie Robbert 0:58

Oh, I mean, the news like, I usually err on the side of I don’t know that that’s true until I can see other similar or authority, authoritative sources reporting the same kind of information.

And so obviously, I saw, you know, your analysis over the weekend, Chris.

But I also saw that same analysis, being picked up by larger publications from a quote unquote, third party, Social Media Research Agency, which I had never heard of before, which isn’t just saying anything, I haven’t heard of everything.

But it was one of those, like, if t]]> full https://www.trustinsights.ai/wp-content/uploads/2022/10/CAN-I-BELIEVE-WHAT-I-READ.png {PODCAST} In-Ear Insights: Can I Believe What I Read? false no 0:00 No no