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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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 – 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 reason, at least from my standpoint, why people don’t want to do the code is because they don’t know what the code says or what it’s supposed to do. Therefore, they’re like, “Let me just avoid that altogether because I don’t know if it’s going to be right.”
The stuff that they’re missing—and this is something that I said on the Doodle webinar that I did with Andy Crestodina: we forget that AI is there to do the work for us. So let the AI not only build the code, but check the code, make sure the code works, and build the requirements for the code. Say, “I want to do this thing.” “What do you, the machine, need to know about building the code?”
Katie Robbert – 02:53
So you’re doing the work to build the code, but you’re not actually coding. And so I think—listen, we’re humans, we’re lazy. We want things that are plug and play.
I just want to press the go button, the easy button, the old Staples button. I want to press the easy button and make it happen. I don’t want to have to think about coding or configuration or setup or anything. I just want to make it work.
I just want to push the button on the blender and have a smoothie. I don’t want to think about the ingredients that go into it. I don’t want to even find a cup. I’m going to drink it straight from the blender.
Katie Robbert – 03:28
I think, at least the way that I interpret it, when people say they want the no code version, they’re hoping for that kind of easy path of least resistance. But no code doesn’t mean no work.
Christopher S. Penn – 03:44
Yeah. And my worry and concern is that things like the software development lifecycle exist for a reason. And the reason is so that things aren’t a flaming, huge mess.
I did see one pundit quip on Threads not too long ago that generative AI may as well be called the Tactical Debt Generator because you have a bunch of people making stuff that they don’t know how to maintain and that they don’t understand. For example, when you are using it to write code, as we’ve talked about in the past, very few people ever think, “Is my code secure?”
And as a result, there are a number of threads and tweets and stuff saying, “One day I coded this app in one afternoon.”
Christopher S. Penn – 04:26
And then, two days later, “Hey guys, why are all these people breaking into my app?”
Katie Robbert – 04:33
It’s— No, it’s true. Yeah, they don’t. It’s a very short-sighted way of approaching it.
I mean, think about even all the custom models that we’ve built for various reasons. Katie GPT—when was the last time her system instructions were updated? Even Katie Artifact that I use in Claude all the time—when was the last time her… Just because I use it all the time doesn’t mean that she’s up to date.
She’s a little bit outdated. And she’s tired, and she needs a vacation, and she needs a refresh.
It’s software. These custom models that you’re building are software. Even if there’s no, quote unquote, “code” that you can see that you have built, there is code behind it that the systems are using that you need to maintain and figure out.
Katie Robbert – 05:23
“How do I get this to work long term?” Not just “It solves my problem today, and when I use it tomorrow, it’s not doing what I need it to do.”
Christopher S. Penn – 05:33
Yep. The other thing that I see people doing so wrong with generative AI—code, no code, whatever—is they don’t think to ask it thinking questions.
I saw this—I was commenting on one of Marcus Sheridan’s posts earlier today—and I said that we live in an environment where if you want to be really good at generative AI, be a good manager. Provide your employee—the AI—with all the materials that it needs to be set up for success. Documentation, background information, a process, your expected outcomes, your timelines, your deliverables, all that stuff.
If you give that to an employee with good delegation, the employee will succeed. If you say, “Employee, go do the thing.” And then you walk off to the coffee maker like I did in your job interview 10 years ago.
Katie Robbert – 06:26
If you haven’t heard it, we’ll get back to it at some point.
Christopher S. Penn – 06:30
That’s not gonna set you up for success. When I say thinking questions, here’s a prompt that anybody can use for pretty much anything that will dramatically improve your generative AI outputs. Once you’ve positioned a problem like, “Hey, I need to make something that does this,” or “I need to fix this thing,” or “Why is this leaking?”…
You would say, “Think through 5 to 7 plausible solutions for this problem.” “Rank them in order of practicality or flexibility or robustness, and then narrow down your solution.” “Set to one or two solutions, and then ask me to choose one”—which is a much better process than saying, “What’s the answer?” Or “Fix my problem.”
Because we want these machines to think. And if you’re saying—when people equate no code with no think and no work— Yes, to your point.
Christopher S. Penn – 07:28
Exactly what you said on the Doodle webinar. “Make the machine do the work.” But you have to think through, “How do I get it to think about the work?”
Katie Robbert – 07:38
One of the examples that we were going through on that same webinar that we did—myself and Andy Crestodina—is he was giving very basic prompts to create personas. And unsurprisingly… And he acknowledged this; he was getting generic persona metrics back.
And we talked through—it’s good enough to get you started, but if you’re using these very basic prompts to get personas to stand in as your audience, your content marketing is also going to be fairly basic. And so, went more in depth: “Give me strong opinions on mediocre things,” which actually turned out really funny.
Katie Robbert – 08:25
But what I liked about it was, sort of to your point, Chris, of the thinking questions, it gave a different set of responses that you could then go, “Huh, this is actually something that I could build my content marketing plan around for my audience.” This is a more interesting and engaging and slightly weird way of looking at it.
But unless you do that thinking and unless you get creative with how you’re actually using these tools, you don’t have to code. But you can’t just say, “I work in the marketing industry. Who is my audience?” “And tell me five things that I should write about.”
It’s going to be really bland; it’s going to be very vanilla. Which vanilla has its place in time, but it’s not in content marketing.
Christopher S. Penn – 09:10
That’s true. Vanilla Ice, on the other hand.
Katie Robbert – 09:14
Don’t get me started.
Christopher S. Penn – 09:15
Collaborate and listen.
Katie Robbert – 09:17
Words to live by.
Christopher S. Penn – 09:20
Exactly. And I think that’s a really good way of approaching this. And it almost makes me think that there’s a lot of people who are saying, somewhat accurately, that AI is going to remove our critical thinking skills.
We’re just going to stop thinking entirely. And I can see some people, to your point, taking the easy way out all the time, becoming… We talked about in last week’s podcast becoming codependent on generative AI.
But I feel like the best thinkers will move their thinking one level up, which is saying, “Okay, how can I think about a better prompt or a better system or a better automation or a better workflow?” So they will still be thinking. You will still be thinking. You will just not be thinking about the low-level task, but you still have to think.
Christopher S. Penn – 10:11
Whereas if you’re saying, “How can I get a no-code easy button for this thing?”… You’re not thinking.
Katie Robbert – 10:18
I think—to overuse the word think— I think that’s where we’re going to start to see the innovation bell curve. We’re going to start to see people get over that curve of, “All right, I don’t want to code, that’s fine.” But can you think?
But if you don’t want to code or think, you’re going to be stuck squarely at the bottom of the hill of that innovation curve. Because if you don’t want to code, it’s fine. I don’t want to code, I want nothing to do with it.
That means that I have made my choice and I have to think. I have to get more creative and think more deeply about how I’m prompting, what kind of questions I’m asking, what kind of questions I want it to ask me versus I can build some code.
Christopher S. Penn – 11:10
Exactly. And you’ve been experimenting with tools like N8N, for example, as automations for AI. So for that average person who is maybe okay thinking but not okay coding, how do they get started? And I’m going to guess that this is probably the answer.
Katie Robbert – 11:28
It is exactly the answer. The 5Ps is a great place to start. The reason why is because it helps you organize your thoughts and find out where the gaps are in terms of the information that you do or don’t have.
So in this instance, let’s say I don’t want to create code to do my content marketing, but I do want to come up with some interesting ideas. And me putting in the prompt “Come up with interesting ideas” isn’t good enough because I’m getting bland, vanilla things back. So first and foremost, what is the problem I am trying to solve?
The problem I am trying to solve is not necessarily “I need new content ideas.” That is the medicine, if you will. The actual diagnosis is I need more audience, I need more awareness.
Katie Robbert – 12:28
I need to solve the problem that nobody’s reading my content. So therefore, I either have the wrong audience or I have the wrong content strategy, or both. So it’s not “I need more interesting content.” That’s the solution.
That’s the prescription that you get; the diagnosis is where you want to start with the Purpose. And that’s going to help you get to a better set of thinking when you get to the point of using the Platform—which is generative AI, your SEO tools, your market research, yada yada.
So Purpose is “I need to get more audience, I need to get more awareness.” That is my goal. That is the problem I am trying to solve.
People: I need to examine, do I have the right audience? Am I missing parts of my audience? Have I completely gone off the deep end?
Katie Robbert – 13:17
And I’m trying to get everybody, and really that’s unrealistic. So that’s part of it. The Process.
Well, I have to look at my market research. I have to look at my customer—my existing customer base—but also who’s engaging with me on social media, who’s subscribing to my email newsletters, and so on and so forth.
So this is more than just “Give me interesting topics for my content marketing.” We’re really digging into what’s actually happening. And this is where that thinking comes into play—that critical thinking of, “Wow, if I really examine all of these things, put all of this information into generative AI, I’m likely going to get something much more compelling and on the nose.”
Christopher S. Penn – 14:00
And again, it goes back to that thinking: If you know five people in your audience, you can turn on a screen recording, you can scroll through LinkedIn or the social network of your choice—even if they don’t allow data export—you just record your screen and scroll (not too fast) and then hand that to generative AI. Say, “Here’s a recording of the things that my top five people are talking about.” “What are they not thinking about that I could provide content on based on all the discussions?”
So you go onto LinkedIn today, you scroll, you scroll, maybe you do 10 or 15 pages, have a machine tally up the different topics. I bet you it’s 82% AI, and you can say, “Well, what’s missing?” And that is the part that AI is exceptionally good at.
Christopher S. Penn – 14:53
You and I, as humans, we are focused creatures. Our literal biology is based on focus. Machines are the opposite. Machines can’t focus. They see everything equally.
We found this out a long time ago when scientists built a classifier to try to classify images of wolves versus dogs. It worked great in the lab. It did not work at all in production. And when they went back to try and figure out why, they determined that the machine was classifying on whether there was snow in the photo or not. Because all the wolf photos had snow.
The machines did not understand focus. They just classified everything. So, which is a superpower we can use to say, “What did I forget?” “What isn’t in here?” “What’s missing?”
You and I have a hard time that we can’t say, “I don’t know what’s missing”—it’s missing.
Christopher S. Penn – 15:42
Whereas the machine could go, knowing the domain overall, “This is what your audience isn’t paying attention to.” But that’s not no thinking; that’s not no work. That’s a lot of work actually to put that together. But boy, will it give you better results.
Katie Robbert – 15:57
Yeah. And so, gone are the days of being able to get by with… “Today you are a marketing analyst.” “You are going to look at my GA4 data, you are going to tell me what it says.” Yes, you can use that prompt, but you’re not going to get very far. You’re going to get the mediocre results based on that mediocre prompt.
Now, if you’re just starting out, if today is Day 1, that prompt is fantastic because you are going to learn a lot very quickly. If today is Day 100 and you are still using that prompt, then you are not thinking. And what I mean by that is you are just complacent in getting those mediocre results back.
That’s not a job for AI.
Katie Robbert – 16:42
You don’t need AI to be doing whatever it is you’re doing with that basic prompt 100 days in. But if it’s Day 1, it’s great. You’re going to learn a lot.
Christopher S. Penn – 16:52
I’m curious, what does the Day 100 prompt look like?
Katie Robbert – 16:57
The Day 100 prompt could start with… “Today you are a marketing analyst.” “You are going to do the following thing.” It can start there; it doesn’t end there.
So, let’s say you put that prompt in, let’s say it gives you back results, and you say, “Great, that’s not good enough.” “What am I missing?” “How about this?” “Here’s some additional information.” “Here’s some context.” “I forgot to give you this.” “I’m thinking about this.” “How do I get here?”
And you just—it goes forward. So you can start there. It’s a good way to anchor, to ground yourself. But then it has to go beyond that.
Christopher S. Penn – 17:36
Exactly. And we have a framework for that. Huge surprise. If you go to TrustInsights.ai/rappel, to Katie’s point: the role, the action (which is the overview), then you prime it.
You should—you can and should—have a piece of text laying around of how you think, in this example, about analytics. Because, for example, experienced GA4 practitioners know that direct traffic—except for major brands—very rarely is people just typing in your web view address. Most often it’s because you forgot tracking code somewhere.
And so knowing that information, providing that information helps the prompt. Of course, the evaluation—which is what Katie’s talking about—the conversation.
Christopher S. Penn – 18:17
And then at the very end, the wrap-up where you say, “Based on everything that we’ve done today, come up with some system instructions that encapsulate the richness of our conversation and the final methodology that we got to the answers we actually wanted.” And then that prompt becomes reusable down the road so you don’t have to do it the same time and again.
One of the things we teach now in our Generative AI Use Cases course, which I believe is at Trust Insights Use Cases course, is you can build deep research knowledge blocks. So you might say, “I’m a marketing analyst at a B2B consultancy.” “Our customers like people like this.” “I want you to build me a best practices guide for analyzing GA4 for me and my company and the kind of company that we are.”
Christopher S. Penn – 19:09
“And I want to know what to do, what not to do, what things people miss often, and take some time to think.” And then you have probably between a 15- and 30-page piece of knowledge that the next time you do that prompt, you can absolutely say, “Hey, analyze my GA4.” “Here’s how we market. Here’s how we think about analytics. Here’s the best practices for GA4.”
And those three documents probably total 30,000 words. And it’s at that point where it’s not… No, it is literally no code, and it’s not entirely no work, but you’ve done all the work up front.
Katie Robbert – 19:52
The other thing that occurs to me that we should start including in our prompting is the three scenarios. So, basically, if you’re unfamiliar, I do a lot of work with scenario planning.
And so, let’s say you’re talking about your budget. I usually do three versions of the budget so that I can sort of think through. Scenario one: everything is status quo; everything is just going to continue business as usual.
Scenario two: we suddenly land a bunch of big clients, and we have a lot more revenue coming in. But with that, it’s not just that the top line is getting bigger.
Katie Robbert – 20:33
Everything else—there’s a ripple effect to that. We’re going to have to staff up; we’re going to have to get more software, more server, whatever the thing is. So you have to plan for those.
And then the third scenario that nobody likes to think about is: what happens if everything comes crashing down? What happens if we lose 75% of our clients? What happens if myself or Chris suddenly can’t perform our duties as co-founders, whatever it is?
Those are scenarios that I always encourage people to plan for—whether it’s budget, your marketing plan, blah blah. You can ask generative AI. So if you spent all of this time giving generative AI data and context and knowledge blocks and the deep thinking, and it gives you a marketing plan or it gives you a strategy…
Katie Robbert – 21:23
Take it that next step, do that even deeper thinking, and say, “Give me the three scenarios.” “What happens if I follow this plan?” “Exactly.” “What happens if you give me this plan and I don’t measure anything?” “What happens if I follow this plan and I don’t get any outcome?”
There’s a bunch of different ways to think about it, but really challenge the system to think through its work, but also to give you that additional information because it may say, “You know what? This is a great thought process.” “I have more questions for you based on this.” “Let’s keep going.”
Christopher S. Penn – 22:04
One of the magic questions that we use with generative AI—I use it all the time, particularly requirements gathering—is I’ll give it… Scenarios, situations, or whatever the case may be, and I’ll say… “The outcome I want is this.” “An analysis, a piece of code, requirements doc, whatever.” “Ask me one question at a time until you have enough information.”
I did this yesterday building a piece of software in generative AI, and it was 22 questions in a row because it said, “I need to know this.” “What about this?” Same thing for scenario planning.
Like, “Hey, I want to do a scenario plan for tariffs or a war between India and Pakistan, or generative AI taking away half of our customer base.” “That’s the scenario I want to plan for.”
Christopher S. Penn – 22:52
“Ask me one question at a time.” Here’s—you give it all the knowledge blocks about your business and things. That question is magic. It is absolutely magic.
But you have to be willing to work because you’re going to be there a while chatting, and you have to be able to think.
Katie Robbert – 23:06
Yeah, it takes time. And very rarely at this point do I use generative AI in such a way that I’m not also providing data or background information. I’m not really just kind of winging it as a search engine.
I’m using it in such a way that I’m providing a lot of background information and using generative AI as another version of me to help me think through something, even if it’s not a custom Katie model or whatever. I strongly feel the more data and context you give generative AI, the better the results are going to be.
Versus—and we’ve done this test in a variety of different shows—if you just say, “Write me a blog post about the top five things to do in SEO in 2025,” and that’s all you give it, you’re going to get really crappy results back.
Katie Robbert – 24:10
But if you load up the latest articles from the top experts and the Google algorithm user guides and developer notes and all sorts of stuff, you give all that and then say, “Great.” “Now break this down in simple language and help me write a blog post for the top five things that marketers need to do to rank in 2025.” You’re going to get a much more not only accurate but also engaging and helpful post because you’ve really done the deep thinking.
Christopher S. Penn – 24:43
Exactly. And then once you’ve got the knowledge blocks codified and you’ve done the hard work—may not be coding, but it is definitely work and definitely thinking— You can then use a no-code system like N8N.
Maybe you have an ICP. Maybe you have a knowledge block about SEO, maybe you have all the things, and you chain it all together and you say, “I want you to first generate five questions that we want answers to, and then I want you to take my ICP and ask the five follow-up questions.” “And I want you to take this knowledge and answer those 10 questions and write it to a disk file.”
And you can then hit—you could probably rename it the easy button— Yes, but you could hit that, and it would spit out 5, 10, 15, 20 pieces of content.
Christopher S. Penn – 25:25
But you have to do all the work and all the thinking up front. No code does not mean no work.
Katie Robbert – 25:32
And again, that’s where I always go back to. A really great way to get started is the 5Ps. And you can give the Trust Insights 5P framework to your generative AI model and say, “This is how I want to organize my thoughts.” “Walk me through this framework and help me put my thoughts together.”
And then at the end, say, “Give me an output of everything we’ve talked about in the 5Ps.” That then becomes a document that you then give back to a new chat and say, “Here’s what I want to do.” “Help me do the thing.”
Christopher S. Penn – 26:06
Exactly. You can get a copy at Trust Insights AI 5P framework. Download the PDF and just drop that in. Say, “Help me reformat this.”
Or even better, “Here’s the thing I want to do.” “Here’s the Trust Insights 5P framework.” “Ask me questions one at a time until you have enough information to fully fill out a 5P framework audit.” “For this idea I have.” A lot of work, but it’s a lot of work.
If you do the work, the results are fantastic. Results are phenomenal, and that’s true of all of our frameworks.
I mean, go on to TrustInsights.ai and look under the Insights section. We got a lot of frameworks on there. They’re all in PDF format.
Download them from anything in the Instant Insights section. You don’t even need to fill out a form. You can just download the thing and start dropping it.
Christopher S. Penn – 26:51
And we did this the other day with a measurement thing. I just took the SAINT framework right off of our site, dropped it in, said, “Make, fill this in, ask me questions for what’s missing.” And the output I got was fantastic. It was better than anything I’ve ever written myself, which is awkward because it’s my framework.
Katie Robbert – 27:10
But. And this is gonna be awkwardly phrased, but you’re you. And what I mean by that is it’s hard to ask yourself questions and then answer those questions in an unbiased way.
‘Cause you’re like, “Huh, what do I want to eat today?” “I don’t know.” “I want to eat pizza.” “Well, you ate pizza yesterday.” “Should you be eating pizza today?” “Absolutely.” “I love pizza.”
It’s not a helpful or productive conversation. And quite honestly, unless you’re like me and you just talk to yourself out loud all the time, people might think you’re a little bit silly.
Christopher S. Penn – 27:46
That’s fair.
Katie Robbert – 27:47
But you can. The reason I bring it up—and sort of… That was sort of a silly example. But the machine doesn’t care about you.
The machine doesn’t have emotion. It’s going to ask you questions. It’s not going to care if it offends you or not.
If it says, “Have you eaten today?” If you say, “Yeah, get off my back,” it’s like, “Okay, whatever.” It’s not going to give you attitude or sass back. And if you respond in such a way, it’s not going to be like, “Why are you taking attitude?” And it’s going to be like, “Okay, let’s move on to the next thing.”
It’s a great way to get all of that information out without any sort of judgment or attitude, and just get the information where it needs to be.
Christopher S. Penn – 28:31
Exactly. You can also, in your digital twin that you’ve made of yourself, you can adjust its personality at times and say, “Be more skeptical.” “Challenge me.” “Be critical of me.” And to your point, it’s a machine. It will do that.
Christopher S. Penn – 28:47
So wrapping up: asking for no-code solutions is fine as long as you understand that it is not no work. In fact, it is a lot of work. But if you do it properly, it’s a lot of work the first time, and then subsequent runs of that task, like everything in the SDLC, get much easier. And the more time and effort you invest up front, the better your life is going to be downstream.
Katie Robbert – 29:17
It’s true.
Christopher S. Penn – 29:18
If you’ve got some thoughts about no-code solutions, about how you’re using generative AI, how you’re getting it to challenge you and get you to do the work and the thinking, and you want to share them, pop by our free Slack group. Go to TrustInsights.ai/analyticsformarketers where you and over 4,200 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 Trust Insights AI TI Podcast. You can find us at all the places fine podcasts are served. Thanks for tuning in. I’ll talk to you on the next one.
Speaker 3 – 29:57
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.
Speaker 3 – 30:50
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.
Speaker 3 – 31:55
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.
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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.
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I think a lot of people misunderstand AI as a quick-fix solution. You really nailed it when you mentioned that it’s not about ‘no work’—it’s about working smarter with AI. Could you share any tips on how to approach AI with a strategic mindset?