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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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 – 00:00
In 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 – 01:29
So 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 experience in marketing. I had very little in digital marketing, and so people, very first day, were saying things like CPC and ROI, and I was like, “I don’t know what any of that means, but I know that I can manage this team.” So, for me, it was a bit of a learning curve.
That’s where Kelsey is finding herself now. The difference is when I started, everything was still very hands on. Kelsey is now learning in the age of “AI can do it for you.” Chris, you had said, “We’re going to give you some training. We’re going to give you some, reports to put together. AI can generate the insights for it.” The reason I paused and said, “Yes, AI can generate the insights for it, but let’s have Kelsey learn the stuff first” is because I knew from experience—from my own experience—if you just let the machine do it, yourself, the human, are never going to actually learn the words on the page.
Katie Robbert – 03:05
I know that at this point in her career, Kelsey is not as familiar with the content that’s on the page, and I want her to learn that. The reason I want her to learn that is for a couple of reasons, and I think this is true of anyone who’s just going straight to “Let the AI do it.” You then don’t know if the AI is giving you back good insights, bad insights, wildly incorrect insights. Especially if you’re giving this to a manager, a board, a client, if you then can’t stand behind the report and explain what’s on the page because they look at it and say, “Well, this is wrong.” You’re like, “Well, the AI did it.” That’s a really terrible excuse because you, as the human, are still responsible.
Nobody’s going to hold the AI responsible because you, the human, are still the one who can get in trouble. You’re the one who can get fired. AI doesn’t care. AI has job security. It’s fine. So when the situation came up last week, I wanted to pause and make sure that the human understood the terms, the context, and the data on the page so that when she got more comfortable using AI to do the analysis, she could more easily say, “Yes, this is right. No, this is not right.”
The problem that a lot of professionals are going to run into, especially ones who are just starting to come up in the industry, is that AI can do it for you. I say it’s a problem because that means that you’re never going to learn the skills. You’re never going to learn how to do it yourself. There are times when AI is going to fail. AI is not going to be available, and you’ll be so dependent on the machines doing it for you that you then can’t do it yourself.
I think about basic skills. I mean, I hear this from a lot of my friends who have teenage kids. Things like writing a check. It’s not something that kids growing up learn anymore, and yet there’s still a need for it. Maybe not all the time, but there’s still enough of a need for it that when it comes up and they don’t know how to do it or balance a checkbook—or balance your account, rather—they’re sort of like, “Well, the machine’s always done it for me. What do you mean I have to manually take out a piece of paper and a pen?”
Katie Robbert – 05:27
Same with learning how to write cursive. Is there a need for it? Sometimes, yeah, there are. But it’s not being taught anymore because the machines do it. Everything is computerized and typing.
So that’s my very long-winded response to maybe one or two of your questions to say I think that it’s appropriate to be using AI to do the analysis if you know what the analysis is supposed to be, if you already know what all the pieces are.
Christopher Penn – 06:00
My counter-argument on the point you said about blaming the AI is that’s kind of the situation as it’s been in marketing analytics for a decade. We all know beyond a shadow of a doubt that what is in, for example, Google Analytics, is an approximation of reality. It is not reality. Anyone who’s ever had to reconcile—”Hey, the number of form fills in, recorded in GA, does not match up with what’s in HubSpot or what’s in Salesforce”—and stuff, is 100% of people. There’s no contest that this stuff is badly flawed in those instances.
I mean, you can blame Google, but Google’s like, “Yeah, whatever. We don’t care. This isn’t for you. This has helped make our ad business better.” People hire companies like Trust Insights: “Can you get us closer to this?”
So in the case where AI is doing the work and perhaps it is not as correct as it could be, obviously, me as the software developer who makes the prompts and things has the obligation to improve the software. But does the human, as a software—it gets more and more advanced—do we need the human after a certain point?
Katie Robbert – 07:22
I think it really depends. It depends on—well, I would say yes. There has to be still some kind of human intervention so that it’s not completely unsupervised and going off the rails. I think about one of our clients. If we handed them a report that had incorrect information in it and we said—they called us out on it, and we said—”Well, AI did it.” They’d be like, “Well, why are you letting AI give you incorrect information?” That’s what I mean by AI is not taking the blame. Clients and other people are still looking for humans to blame, people to be accountable. Somebody has to be responsible for this—emphasis on the somebody, not the something.
They want to be mad at someone. Even if it’s not a human’s fault, it doesn’t matter. That’s sort of my—that’s where I’m coming from when I say I want my team—I can’t control anyone else, but I would like my team to understand the pieces before letting AI take it because I know from experience that if we put out something that’s slightly off or incorrect, and our reasoning is that, “Well, we didn’t do it, AI did it,” people are still going to be mad at us. They’ll be like, “Well, why didn’t you supervise your AI to do better?”
Christopher Penn – 08:53
No, I think that’s perfectly valid. The way that I see these tools evolving, I think a lot of companies will be at a point where it’s like, “Yeah, we’re just going to outsource this entire task to a machine.” There will be maybe an account manager who just hands off the report, but then says, “Yep, this was made by machines from your data, and that’s that.” Because we see that already to some degree. We see that with SaaS companies. SaaS companies will create software that a client logs into. They log into Google Analytics or a Domo, or whatever, and it’s like, “Yeah, the reports, the report. There you go. Good luck.”
Katie Robbert – 09:35
Which is a huge risk. It’s a huge risk. Yes, companies are doing that, but I think that you’re sort of, in some ways, comparing apples and oranges. If you take Google Analytics, for example, Google has put together this piece of software that tracks your website data. They say, “Here’s a portal from all of our clients to log into,” and then what they get is what they get.
That’s not exactly true. Yes, what you see is what you get, but then there’s still, theoretically, a person for you to go back to and say, “There’s a problem with my account, or the way that you built it is incorrect, and we need to fix it.” There are a lot of moving pieces. I can see that you’re ready to argue with me about this.
My point isn’t that companies like Google have said, “Hey, you’re on your own, kid,” because they have. I’m not denying that. My point is that we, as humans, are still looking for the human to blame for the technology problem, even if the problem is the tech. That’s my point. It’s not that we’re not just letting tech run autonomously without us. We are. We absolutely are.
I think it’s a huge risk because look how bad Google’s reputation has gotten. Look how many people have abandoned Google Analytics because of a mediocre, not-working product because there’s no one who’s fixing it. There’s no one who’s—so people are finding other solutions. Google’s falling behind in a lot of places because they’ve let the machines just sort of do their thing. That’s the risk I’m talking about. That’s Google.
Katie Robbert – 11:20
Google is way bigger than Trust Insights; Trust Insights will ever be. We make a couple of wrong steps as a very small company, and Trust Insights is done, especially if we say, “Hey, the machines did it. We had nothing to do with it.” It’s still our name. It’s still our reputation. You can argue with me all you want about this, Chris, but the machines are not ever, in this context and Trust Insights, ever just going to run without human intervention.
In your example of bringing on an account manager just to deliver the reports, that’s still a risk because if the account manager or a human is not doing quality assurance on the reports, making sure the information is correct, if the human does not know what is contained within the report, that’s a risk, and that will not happen under my watch.
Christopher Penn – 12:11
Shouldn’t you be using the machines to also do QA?
Katie Robbert – 12:14
It’s sort of the same idea of having a developer QA their own work. The answer is no. No, absolutely not. Hard no, period.
Christopher Penn – 12:24
It’s interesting because that’s actually how I use them to check myself. I will say, “Okay, I’ve written this report. Go through it and make sure I didn’t say anything stupid.”
Katie Robbert – 12:33
But see, you just said you wrote it; the machine’s checking it. Not the machine wrote it; the machine checked it.
Christopher Penn – 12:41
So there’s, like, I would have a machine check itself as well when we get to that point.
Katie Robbert – 12:47
But here’s the thing: you’re not just saying, “Okay, the machine wrote it, great, let’s just send it.” You, the human, are still reading it to make sure that what the machine wrote is not incorrect. If you’re saying, “Machine, check my work,” you, the human, are still checking the machine, checking your work, saying, “Did the machine check my work correctly?” You’re proving my point over and over again that the human can’t step out of the process completely. The human still has to be the subject matter expert.
Christopher Penn – 13:20
Yes. So with that, then, for all the companies that are looking at this vendor, that vendor, all these different tools, thinking about how many people can we lay off and save money and boost our profit margins, what is the proper role for AI, particularly within analytics?
Katie Robbert – 13:43
I think—if you want to look at your team holistically, AI is a team member. AI is not the whole team. AI is one part of the team, and I think that’s fine. I’m not anti-AI. I think there are a lot of things that AI can do. I think that you can use it to help analyze your reports. I think you can do it to QA. I think you can do it to write all of your software. I think those things are totally appropriate. You need to have a subject matter expert paired with the AI. That’s how that needs to work. So if the AI is the worker, you need the subject matter expert paired right next to it, checking its work, almost micromanaging it, to say, “Did you do the thing correctly?”
Katie Robbert – 14:32
Because AI doesn’t care if it’s micromanaged—no feelings, no emotions, whatever. You need someone who understands what the output is supposed to be, working directly.
Christopher Penn – 14:43
Next to AI, especially for iteration. Let’s look at an example of this so we can put it into practice to what Katie was saying. This is an example of one of the reports. Now, this is not generated by AI; this is generated by good, old-fashioned statistics written in the R programming language. This talks to the Google Analytics API—actually, no, this talks to the Ahrefs API and says, “What domains link to your website in the last”—I think this is 60 days?
Yeah, 60 days. So this is from my personal website. We can see there are a bunch of different domains here and the number of links. It’s a very straightforward chart. Let’s take a look at what generative AI said, given the same level of prompt that we were talking about earlier. I gave it the chart.
Christopher Penn – 15:31
I said, “Tell me, explain this. In this chart,” I did the pre-priming step, saying, “What do you know about analyzing inbound links?” First, so that we populate the history and we load the chart and ask it for reference recommendations. The instructions given to Kelsey on our team were to say, “Look through what it pointed out and pick out, one or two things to highlight on this chart.”
So it says, “Good, you’ve got a diverse backlink profile, a presence of high-authority links, consistent link building. The first scene seems to indicate ongoing link-building efforts. Areas for improvement: over-reliance on a few domains, lack of information on link quality, limited insight into anchor text diversity. Recommendations: diversify backlink sources, focus on quality over quantity, conduct a backlink audit.”
Now here’s the chart again, based on those recommendations and your knowledge of link building, Katie, how do you think AI did?
Katie Robbert – 16:28
I think it did okay. I think it did okay. There were a couple of things—a couple of terms I got tripped up on. I knew that as Kelsey’s learning the fundamentals of SEO, I knew if I said, “Well, what does it mean when you say this?” She’d be like, “I’m not really sure.” That, to me, is a red flag to say, “Well, then you can’t put it in the report if you don’t understand what it means.”
I think one of them was, like, “Lack of diverse anchor text.” I’m looking at this particular output, and I’m like, “Where did you get that information for this? This information—this particular chart—doesn’t contain that information. So how did you get from A to B?”
Katie Robbert – 17:19
When a client says, “Well, what does that mean?” you have nothing to point back to. So that was my concern over letting AI go ahead and do analysis and having someone who’s less familiar with the pieces accept the output and say, “This is it.”
When I was reading through the initial analysis—again, all training, she hadn’t done anything incorrectly; she was learning—I said, “Well, what does this mean? What does this mean? You keep going back to diverse links. What does it mean to have more diverse links? What does that look like? How would you explain it to me as the person who needs to take action?” All of that context was missing from the AI analysis. Some of the analyses, quite frankly, I don’t know where it came from.
Christopher Penn – 18:05
So I’m now going to prove your point entirely, yes and completely. This is my website. This is my website, and the AI’s outputs actually gave me some really good ideas for improving this report because there are gaps to just this particular slide. There are gaps that could make this report more informative, like putting the actual domain authority on here so we know the quality of these domains. What this report tells me, as the website owner and as someone who has been doing SEO since 1994—aka 30 years—is that my site is followed by a bunch of crypto bots, and they are scraping my content. Because my content is loaded up with links to my site and Trust Insights and stuff, those get repeated in the scraped content.
That’s why you see auxiliaryfx.com, which—and forex trading, binary FX. These are all sites that scrape other people’s content to boost their own reputation so that they can then hawk their crypto products, whatever. Those are crap links, and those are crap sites. Those are not sites that you would generally want linking to you unless you were also a crypto company of some kind.
What this tells me is that even though those sites are generating a lot of links, they’re probably not very good quality. Having domain authority on here would certainly help—perhaps even having a threshold in the code itself to say, “Let’s not show anything with the domain authority under 50,” for example, to weed out some of these. But the prompt was insufficient.
Even though we primed it with good data, it wasn’t specific to the way that we do things. This is the essential part of generative AI and the essential part of your AI strategy: your prompts, if you want them to do well, have to contain a lot of your unique perspective and your human-led perspective on how the machine should think.
Christopher Penn – 19:51
So the first thing I would do to improve this is to add domain authority. Maybe add it even at a filter, or at least in the prompt, say, “Ignore any domain with an authority under 50.” Then, second, based on that, get rid of any recommendations that are clearly just scrapers. Then do your assessment about inbound link quality because the machine did exactly as it was told. It said, “Provide recommendations based on this data.”
Even that prompt could have some nuance because if we told Kelsey the exact same thing, she’d be like, “Okay, I’m going to do my best to come up with something because that’s what’s expected of me,” when the real answer might be, “There’s not much here to do, to work with.” You would only know that if you knew the business, in this case, my website; if you knew the domain, aka SEO; and if you had some idea of what you were looking at. That’s something that is knowledge that’s encoded in our heads as subject matter experts, but we didn’t put it in the prompt.
I think for this task, in the future, what would be a useful exercise is as Kelsey trains up on SEO, as we provide guidance, we also write that down so that it becomes part of the prompt.
Katie Robbert – 21:39
Yep, the AI is mine. No, but I think that’s exactly it. That’s the risk of people going straight from no experience to “AI can do it for me” is—one of the things that I still have to look up is—so I understand domain authority and domain rating. You, looking at that list at a glance, can tell what links are—what. I would still have to go look up links individually and say, “Is this one a high-quality link or not?”
That’s sort of like—that’s sort of the difference between you and me in terms of our expertise. I understand all of the pieces, but there are still parts that I need to double-check before I’m like, “Okay, the machine got it right.”
Katie Robbert – 22:29
Again, there were still some recommendations in there, like about the anchor text, that I’m like, “Where did that come from?” Someone who’s less familiar, who doesn’t maybe know the thresholds of good domain authority, what that means for your site— “Oh, but you’re getting a lot of inbound links. That’s great. High quality, low quality, okay, does it matter?” Those are things that you need to understand. To your point, what you consider low quality for your site might be high quality for a different site, and that’s where it gets murky. You have to have that subject matter expertise. I think that is the whole point of this episode: that, yeah, AI can do it.
Katie Robbert – 23:18
AI can totally do it, but you still have to know what the AI is doing in order for the outputs to be trusted.
Christopher Penn – 23:27
AI is very much like the fairy tale genie in a lamp. It will do as it’s told. It will give you what you asked for, which means if you are not super clear about what you ask for, you will get it. It may be what you want; it may not be what you need, and it certainly may not be what your customers need or your team needs. This is a really good example of even something as simple as, “Hey, try this out in generative AI.”
It clearly needs a lot more customization if we want it to do it the Trust Insights way. We can have it do it the generic way—”Here’s the generic knowledge about SEO”—or we can say to do it the Trust Insights way: “These are all the things that you need to know. You need to have the domain rating on the chart. You need to have some color coding so that it’s visible to the machine. You need to have an explanation—a step-by-step explanation—of how you drew the conclusion so that we can check your thinking and check your work. You need to have this, that, and the other thing.”
Having all that in the prompt—the prompt will be, like, this long—that’s okay because it’s no longer the generic result that everyone else is going to get using generative AI. It’s going to be keyed in and tuned into the way that we do stuff. I think that is probably the big takeaway: making sure that you invest the time to properly develop your software as opposed to just going for fast and easy.
Katie Robbert – 25:04
Well, I think that’s a big takeaway, but also the big takeaway is that your people actually need to know the subject matter. To write that prompt, the team needs to be subject matter experts in the thing. So that’s the two takeaways. I don’t want to lose the human side in favor of just, “Write better prompts for AI.” You still need people who know the thing, so—that’s why Kelsey is training, and you and I are the trainers.
We can very quickly look at the report and go, “Yes, that’s correct. No, that’s not correct” because we’ve been doing this a long time. We have the experience that we can very quickly go, “Yes, no, yes, no. Here’s how to fix that.” She will get there if we let her get there. If we just give her AI, she won’t get there.
Christopher Penn – 25:59
And so, no surprise—
Katie Robbert – 26:01
The 5P Framework. Well, that’s it. When using AI, when training teams, when building reports, whatever it is you’re doing, starting with the 5P Framework is a really good decision because it’s just going to outline all of the pieces. What is your purpose? Who are your people? What is the process? What are the platforms? How are you going to measure success with your performance?
Starting with your purpose: “I want to train my AM on how to use AI correctly so that she can use it to generate a larger volume of reports.” Caveat, being trained correctly. Then you go through the other—
Christopher Penn – 26:46
Pieces. Even with this report, it was something of a failing on both of our parts. Neither one of us explained what the purpose of this report is. What do you do with it? When you hand it to the client, what is the client supposed to do with this thing because it isn’t clear?
Even if you don’t know what inbound links are supposed to do, the purpose of inbound links is to improve the reputation of your website and drive direct referral traffic to your site. That needs to be written out explicitly in the 5P assessment and then in the report itself, like, “Here’s what you’re supposed to be doing. If you’re not doing it, then it’s going to show up in the report.” Like, “Hey, all these random crypto links: they are not serving the purpose of inbound links, and this report is supposed to reflect that.”
Katie Robbert – 27:35
Well, and that’s sort of the other thing that I really love about the 5Ps: it helps keep you focused on what it is you’re doing. If it doesn’t—if the recommendations or the actions don’t line up back to the purpose, then it’s just going to be a distraction, and it’s just erroneous data that can go somewhere else. If the purpose of the report is to improve inbound links, then why are you telling me about this great ad that you put together over here? Like that’s—yeah, it might be a good idea, but it has nothing to do with what we’re talking about at present.
Christopher Penn – 28:11
Yep. To wrap up, for any task where you are thinking about using AI, make sure you go through the full 5Ps. What is the purpose? Who are the people involved, what knowledge do they have and don’t they have, and what knowledge will they need to be able to accomplish the purpose? What processes do you have in place, and how well-documented are they, or not?
Then you get to the AI doing the portions that are appropriate for it to do and ultimately measuring the performance—whether you saved time, saved money, or made money with it—because ultimately everything has to somehow find its way to that. If you don’t do those things, then yeah, AI is probably going to underperform, and you’re not going to get the benefits that you’re hoping for.
Katie Robbert – 28:57
Train your people, then train AI.
Christopher Penn – 29:01
Or do—or I would say do both. Train your people and document everything so that training your AI is easier because if you take the time to do the documentation—
Katie Robbert – 29:16
Yes, yes.
Christopher Penn – 29:18
—then feeding it to an AI is super easy, and you don’t have to do the same work twice.
Katie Robbert – 29:23
For those who don’t know why I’m so excited, this is like a big—this is a big deal to get a developer to be pro-documentation, and it is just—it’s a win for the non-technical people. Such a big win.
Christopher Penn – 29:41
If you have stories about how you have trained people and/or machines or how you leverage subject matter experts with AI and you want to share those stories, pop on over to our free Slack group, go to Trust Insights, AI, Analytics for Marketers, where you and over 3,000 other marketers are asking and answering each other’s questions every single day about analytics, AI, and all other things marketing.
Wherever you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/tipodcast. You can find us on most places where podcasts are served. Thanks for tuning in, and we’ll talk to you next time.
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.
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