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In-Ear Insights: Generative AI Strategy and Integration Mail Bag
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!
Watch the video here:
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
https://traffic.libsyn.com/inearinsights/tipodcast-generative-ai-strategy-mailbag.mp3
Download the MP3 audio here.
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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, 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 the data. The process: How is the data collected? Are we confident that we know that if we’ve set up a survey, how that survey is getting disseminated and how responses are coming back in?
Katie Robbert – 02:28
If you’re using third-party tools, is it a black box, or do you have a good understanding in Google Analytics, for example, the definitions of the dimensions and the metrics, or Adobe Analytics, the definitions of the variables and all of those different segments and channels? Those are the things that you want to make sure that you have control over. Platform: If your data is going through multiple places, is it transforming to your knowledge when it goes from A to B to C or is it going to one place? And then Performance: Did we answer the question being asked?
First things first, you have to set your expectations correctly: This is what we have to work with.
Katie Robbert – 03:10
If you are using SEO data, for example, if you’re pulling data out of Ahrefs, or if you’re pulling data out of a third-party tool like Ahrefs or SEMrush, do you know exactly how that data is collected, all of the different sources?
If you’re saying, “Oh well, I’m looking at my competitors’ data, and this is their domain rating, for example,” do you know what goes into that? Do you know how it’s calculated?
Katie Robbert – 03:40
Those are all the things that you want to do up front before you even get into the 6 Cs because the 6 Cs is going to give you an assessment and audit of your data quality, but it’s not going to tell you all of these things from the five Ps of where it came from, who collected it, how it’s collected, what platforms it’s in.
You want to make sure you’re using both of those frameworks together.
And then, going through the 6C audit that I covered in the AI for B2B Marketers Summit, which I think we have—the 6C audit on our Instant Insights—we can drop a link to that in the show notes of this podcast. You can grab a copy of that. Basically, that’s what I would say to that.
Katie Robbert – 04:28
There’s no—in my world, and I’ve been through a lot of regulated data—there is no such thing as the perfect data set because there are so many factors out of your control. You really need to think about the data being a guideline versus the exactness.
Christopher S. Penn – 04:47
One of the things, with all data, one of the best practices is to get out a spoon and start stirring and sampling. Taking samples of your data along the way.
If you, like you said, if you start out with bad data to begin with, you’re going to get bad data out. AI won’t make that better—AI will just make it bigger.
But even on the outbound side, when you’re looking at data that AI generates, you should be looking at it. I would be really concerned if a company was using generative AI in their pipeline and no one was at least spot-checking the data, opening up the hood every now and then, taking a sample of the soup and going, “Yep, that looks right.” Particularly if there are things that AI is going to get wrong.
Christopher S. Penn – 05:33
One of the things you talked about in your session, and you showed Google Colab with this, was to not let AI do math. If you’re gonna get hallucinations anywhere, it’s gonna be if you let a generative AI model attempt to do math to try to calculate a mean, or a median, or a moving average—it’s just gonna be a disaster.
Katie Robbert – 05:52
Yeah, I don’t do that. The 6 Cs is really, again, it’s just to audit the data set itself.
The process that we’ve put together that uses Google Colab, as Chris just mentioned, is meant to do that in an automated fashion, but also give you the insights on how to clean up the data set. If this is the data that you have to use to answer the question from the five Ps, what do I have to do to make this a usable data set?
It’s going to give you that information as well. We had Anthony’s question: “The correctness is only as good as your preparedness.” You can quote me on that.
Christopher S. Penn – 06:37
The more data you provide, the less likely you’re going to get hallucinations. That’s just the way these tools work.
If you are asking the tool to infer or create things from your data that aren’t in the data you provided, the risk of hallucination goes up if you’re asking language models to do non-language tasks.
A simple example that we’ve seen go very badly time and time again is anything geospatial: “Hey, I’m in Boston, what are five nearby towns I should go visit? Rank them in order of distance.” Gets it wrong every single time.
Because a language model is not a spatial model. It can’t do that. The knowing what language models can and can’t do is a big part of that.
Okay, let’s move on to the next one, which is from a different.
Christopher S. Penn – 07:31
Chris says that every B2B company is struggling with how to roll out AI, and many CEOs think it is non-strategic and just tactical. “Just go and do some AI.” What are the high-level metrics that you found that can be used with executive teams to show the strategic importance of AI?
Katie Robbert – 07:57
I feel like this is a bad question, and I know I say that. One of the things that I’m currently working on: If you haven’t gotten it yet, you can go ahead and download our AI readiness kit, which is all of our best frameworks, and we walk through how you can get ready to integrate AI.
You can get that at TrustInsights.ai/AIKit. I’m in the process of turning that into a course to help people even further go on this journey of integrating AI.
And one of the things that keeps coming up: so unironically, I’m using generative AI to help me prepare for this course. And I, borrowing a technique from Chris, I said, “Ask me questions about these things that I need to be able to answer.”
Katie Robbert – 08:50
And very similar to the question that this other Chris is asking, there were questions like, “What is the one metric?” Or, “What is the one thing?” And I personally hate questions like that because it’s never as simple as “Here’s the one thing,” or “Here’s the one data point” that’s going to convince people to completely overhaul their thinking and change their mind.
When you are working with your leadership team and they’re looking for strategic initiatives, you do have to start at the tactical level because you have to think about what is the impact day-to-day that this thing is going to have, but also that sort of higher level of how is this helping us achieve our overall vision, our goals.
Katie Robbert – 09:39
One of the exercises in the AI kit, and also will be in the course, is your strategic alignment. The way that it’s approached, first and foremost, you still have to know what you want to do, so you can’t skip the five Ps.
I’m going to give you the TRIPS homework. TRIPS is Time, Repetitive, Importance, Pain, and Sufficient Data. And it’s a simple worksheet where you sort of outline all the things that I’m doing currently so you can find those good candidates to give those tasks to AI.
It’s very tactical. It’s important, though, because if you don’t know where you’re going to start, who cares about the strategic initiative? Who cares about the goals? Because then you’re just kind of throwing things against the wall to see what’s going to stick. So, do TRIPS.
Katie Robbert – 10:33
Do the five P’s, go through this goal alignment work exercise, and then bring all of that information—the narrative, the story, the impact, the risks—to your strategic team, to your leadership team.
There’s no magic. If I just had this one number, and you’re going to say, “Oh, but I could tell them what the ROI is.” “Get out!”
There is an ROI worksheet in the AI kit, but you still have to do all those other things first. And it’s a combination of a lot of data. There is no one magic number. There is no one or two numbers that you can bring. But there are exercises that you can go through to tell the story, to help them understand.
Katie Robbert – 11:24
This is the impact. This is why. These are the risks. These are the people. These are the results that we want to be able to get.
Christopher S. Penn – 11:34
To the ROI one, because that’s one of my least favorite ones. The question I always ask is: Are you measuring your ROI now? Because if you’re not measuring it now, then you’re not going to know how AI made a difference.
Katie Robbert – 11:47
It’s funny how that works.
Christopher S. Penn – 11:48
Funny how that works. To no one’s surprise, they’re not measuring the ROI now. So.
Katie Robbert – 11:54
Yeah, but suddenly we’re magically going to improve it.
Christopher S. Penn – 11:58
Exactly. We’re just going to come up with it just magically. All right, let’s see. Let’s scroll down here into the next set of questions from your session.
Christine asks: With data analytics, is it best to use Data Analyst and ChatGPT or Deep Research? I feel like the Data Analyst is more like collaboration where I prompt the analysis step-by-step. Well, both of those so far.
Katie Robbert – 12:22
But she didn’t say for what purpose.
Christopher S. Penn – 12:25
Just with data analytics, she said. That was her.
Katie Robbert – 12:28
But that could mean a lot of different things. That’s not—and this is no fault to the question asker—but in order to give a proper answer, I need more information.
I need to know. When you say data analytics, what does that mean? What are you trying to do?
Are you pulling insights? Are you trying to do math and calculations? Are you combining data sets? What is that you’re trying to do?
You definitely use Deep Research more than I do, Chris, because I’m not always convinced you need to do Deep Research. And I feel like sometimes it’s just an added step for no good reason. For data analytics, again, it really depends on what this user is trying to accomplish.
Katie Robbert – 13:20
Are they trying to understand best practices for calculating a standard deviation? Okay, you can use Deep Research for that, but then you wouldn’t also use generative AI to calculate the standard deviation.
It would just give you some instructions on how to do that. It’s a tough question. I don’t have enough information to give a good answer.
Christopher S. Penn – 13:41
I would say if you’re doing analytics, Deep Research is always the wrong tool. Because what Deep Research is, is a set of AI agents, which means it’s still using base language models.
It’s not using a compute environment like Colab. It’s not going to write code, so it’s not going to do math well.
And OpenAI’s Data Analyst also kind of sucks. It has a lot of issues in its own little Python sandbox. Your best bet is what you showed during a session, which is to use Colab that writes the actual code to do the math.
If you’re doing math, none of the AI tools in the market other than Colab will write the code to do the math well. And just please don’t do that. It’s just not a good idea.
Christopher S. Penn – 14:27
Cheryl asks: How do we realistically execute against all of these AI opportunities that you’re presenting when no one internally has the knowledge and we all have full-time jobs?
Katie Robbert – 14:40
I’m going to go back to the AI kit: TrustInsights.ai/AIKit. And I know it all sounds very promotional, but we put this together for a reason—to solve these exact problems. The “I don’t know where to start.”
If you don’t know where to start, I’m going to put you through the TRIPS framework. If you don’t know, “Do I even have the data to do this?” I’m going to walk you through the 6 Cs. Those are the frameworks integrated into this AI kit and how they all work together.
To the question that the user has of “We all have full-time jobs”: Yeah, you’re absolutely right. You’re asking people to do something new. Sometimes it’s a brand new skill set.
Katie Robbert – 15:29
Using something like the TRIPS framework is going to help you focus. Is this something we should even be looking at right now? We talk a lot about, “Don’t add one more thing to people’s lists.”
When you go through this exercise, what’s not in the framework but what you have to include in the conversation is: We focused down. We know that these are the two things that we want to use generative AI for.
But then you have to start to ask: Do we have the resources, the right people, the budget, the time? Can we even do this? Is it even realistic? Are we willing to invest time and energy to trying this?
There’s a lot to consider. It’s not an easy question to answer.
Katie Robbert – 16:25
You have to be committed to making time to even think about what you could do, let alone doing the thing.
Christopher S. Penn – 16:33
To close out Autumn’s very complicated question: How do you approach conversations with your clients at Trust Insights who are resistant to AI due to ethical and moral impacts—not only due to some people who are using it as a human replacement and laying off, but also things like ecological impacts? That’s a big question.
Katie Robbert – 16:58
Nobody said you have to use it. So if we know. In all seriousness, if we have a client who comes to us and says, “I want you to do this work. I don’t want you to use AI to complete this work.”
We do not—it does not align with our mission, our value, whatever the thing is, or we are regulated, we’re not allowed to use it.
There’s going to be a lot of different scenarios where AI is not an appropriate mechanism. It’s technology. That’s okay.
The responsibility is on us at Trust Insights to be realistic about. If we’re not using AI, this is the level of effort.
Katie Robbert – 17:41
Just really being transparent about: Here’s what’s possible; here’s what’s not possible; or, here’s how long it will take versus if we used AI to do the thing, if we used it on our side, you’re not using it on your side.
There’s a lot of different ways to have that conversation. But at the end of the day, if it’s not for you, then don’t force it to be for you.
Obviously there’s a lot of tech that is now just integrating AI, and you’re using it without even knowing that you’re using it. That’s not something that we at Trust Insights have control over. We’re.
Katie Robbert – 18:17
Trust me, if we had the power to say, “This is what this tech does,” we would obviously be a lot richer and a lot happier, but we don’t have those magic powers. All we can do is really work with our clients to say what works for you, and here’s what we have capacity to do, and here are our limitations.
Christopher S. Penn – 18:41
Yeah. The challenge that companies are going to run into is that AI kind of sets a bar in terms of the speed at which something will take and a minimum level of quality, particularly for stuff that isn’t code.
The challenge is going to be for companies: If you want to not use AI for something, and that’s a valid choice, you will have to still meet user and customer expectations that they will get the thing just as fast and just as high quality as a competitor that is using generative AI or classical AI.
And that’s for a lot of companies and a lot of people—that is a tough pill to swallow.
Christopher S. Penn – 19:22
If you are a graphic designer and someone says, “I could use AI and have my thing in 42 seconds, or I could use you and have my thing in three weeks and you cost 10 times as much.” It’s a very difficult thing for the graphic designer to say, “Yeah, I don’t use AI, but I can’t meet your expectations of what you would get out of an AI in terms of the speed and the cost.”
Katie Robbert – 19:51
Right. But then, what they’re trading is quality. What they’re trading is originality.
So it really just comes down to having honest conversations and not trying to be a snake oil salesman to say, “Yes, I can be everything to everyone.” We can totally deliver high quality, super fast and super cheap.
Just be realistic, because it’s hard because we’re all sort of in the same boat right now: Budgets are being tightened, and companies are hiring but not hiring. They’re not paying enough and people are struggling to find work.
And so we’re grasping at straws, trying to just say yes to anything that remotely makes sense.
Katie Robbert – 20:40
Chris, that’s where you and I were when we started Trust Insights; we kind of said yes to a lot of things that upon reflection, we wouldn’t say yes today. But when we were starting the company, we kind of felt like we had to.
And it takes a lot of courage to say no, but we’ve gotten better about saying no to things that don’t fit.
And I think that’s where a lot of people are going to find themselves—when they get into those conversations about the moral use and the carbon footprint and what it’s doing to our environment.
I think it’ll, unfortunately, be easy to overlook those things if it means that I can get a paycheck. And I can put food on the table. It’s just going to be hard.
Christopher S. Penn – 21:32
Yep. Until, the advice we’d give people at every level in the organization is: Yes, you should have familiarity with the tools so you know what they do and what they can’t do.
But also, you personally could be working on your personal brand, on your network, on your relationship building with clients—past and present—with prospective clients.
Because at the end of the day, something that Reid Hoffman, the founder of LinkedIn, said is that every opportunity is tied to a person. If you’re looking for an opportunity, you’re really looking for a person.
And as complicated and as sophisticated as AI gets, it still is unlikely to replace that interpersonal relationship, at least in the business world. It will in some of the buying process, but the pre-buying process is how you would interrupt that.
Christopher S. Penn – 22:24
Maybe that’s a talk for another time about Marketing in the Age of AI. But at the bare minimum, your lifeboat—your insurance policy—is that network.
It’s one of the reasons why we have the Trust Insights newsletter. We spend so much time on it.
It’s one of the reasons why we have the Analytics for Marketers Slack group and spend so much time on it: Because we want to be able to stay in touch with real people and we want to be able to go to real people whenever we can, as opposed to hoping that the algorithmic deities choose to shine their favor upon us this day.
Katie Robbert – 23:07
I think Marketing in the Age of AI is an important topic. The other topic that we see people talking about a lot is that pushback on AI and that craving for human connection.
I personally don’t think that AI created this barrier between humans. It’s always existed. If anything, new tech doesn’t solve old problems.
If anything, it’s just put a magnifying glass on how much we’ve siloed ourselves behind our laptops versus making those human connections. But it’s just easy to blame AI. AI is sort of the scapegoat for anything that goes wrong right now. Whether that’s true or not.
So, Chris, to your point, if you’re reliant on technology and not making those human connections, you definitely have a lot of missed opportunities.
Christopher S. Penn – 24:08
Exactly. If you’ve got some thoughts about today’s mailbag topics, experiences you’ve had with measuring the effects of AI, with understanding how to handle data quality, or wrestling with the ethical issues, and you want to share what’s on your mind?
Pop by our free Slack group. Go to TrustInsights.ai/analyticsformarketers where over 4,000 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/TIPodcast and you can find us at all the places that fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.
Katie Robbert – 24:50
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.
Katie Robbert – 25:43
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 Metalama.
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 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.
Katie Robbert – 26:48
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.
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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!
Watch the video here:
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
https://traffic.libsyn.com/inearinsights/tipodcast-generative-ai-strategy-mailbag.mp3
Download the MP3 audio here.
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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, 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 the data. The process: How is the data collected? Are we confident that we know that if we’ve set up a survey, how that survey is getting disseminated and how responses are coming back in?
Katie Robbert – 02:28
If you’re using third-party tools, is it a black box, or do you have a good understanding in Google Analytics, for example, the definitions of the dimensions and the metrics, or Adobe Analytics, the definitions of the variables and all of those different segments and channels? Those are the things that you want to make sure that you have control over. Platform: If your data is going through multiple places, is it transforming to your knowledge when it goes from A to B to C or is it going to one place? And then Performance: Did we answer the question being asked?
First things first, you have to set your expectations correctly: This is what we have to work with.
Katie Robbert – 03:10
If you are using SEO data, for example, if you’re pulling data out of Ahrefs, or if you’re pulling data out of a third-party tool like Ahrefs or SEMrush, do you know exactly how that data is collected, all of the different sources?
If you’re saying, “Oh well, I’m looking at my competitors’ data, and this is their domain rating, for example,” do you know what goes into that? Do you know how it’s calculated?
Katie Robbert – 03:40
Those are all the things that you want to do up front before you even get into the 6 Cs because the 6 Cs is going to give you an assessment and audit of your data quality, but it’s not going to tell you all of these things from the five Ps of where it came from, who collected it, how it’s collected, what platforms it’s in.
You want to make sure you’re using both of those frameworks together.
And then, going through the 6C audit that I covered in the AI for B2B Marketers Summit, which I think we have—the 6C audit on our Instant Insights—we can drop a link to that in the show notes of this podcast. You can grab a copy of that. Basically, that’s what I would say to that.
Katie Robbert – 04:28
There’s no—in my world, and I’ve been through a lot of regulated data—there is no such thing as the perfect data set because there are so many factors out of your control. You really need to think about the data being a guideline versus the exactness.
Christopher S. Penn – 04:47
One of the things, with all data, one of the best practices is to get out a spoon and start stirring and sampling. Taking samples of your data along the way.
If you, like you said, if you start out with bad data to begin with, you’re going to get bad data out. AI won’t make that better—AI will just make it bigger.
But even on the outbound side, when you’re looking at data that AI generates, you should be looking at it. I would be really concerned if a company was using generative AI in their pipeline and no one was at least spot-checking the data, opening up the hood every now and then, taking a sample of the soup and going, “Yep, that looks right.” Particularly if there are things that AI is going to get wrong.
Christopher S. Penn – 05:33
One of the things you talked about in your session, and you showed Google Colab with this, was to not let AI do math. If you’re gonna get hallucinations anywhere, it’s gonna be if you let a generative AI model attempt to do math to try to calculate a mean, or a median, or a moving average—it’s just gonna be a disaster.
Katie Robbert – 05:52
Yeah, I don’t do that. The 6 Cs is really, again, it’s just to audit the data set itself.
The process that we’ve put together that uses Google Colab, as Chris just mentioned, is meant to do that in an automated fashion, but also give you the insights on how to clean up the data set. If this is the data that you have to use to answer the question from the five Ps, what do I have to do to make this a usable data set?
It’s going to give you that information as well. We had Anthony’s question: “The correctness is only as good as your preparedness.” You can quote me on that.
Christopher S. Penn – 06:37
The more data you provide, the less likely you’re going to get hallucinations. That’s just the way these tools work.
If you are asking the tool to infer or create things from your data that aren’t in the data you provided, the risk of hallucination goes up if you’re asking language models to do non-language tasks.
A simple example that we’ve seen go very badly time and time again is anything geospatial: “Hey, I’m in Boston, what are five nearby towns I should go visit? Rank them in order of distance.” Gets it wrong every single time.
Because a language model is not a spatial model. It can’t do that. The knowing what language models can and can’t do is a big part of that.
Okay, let’s move on to the next one, which is from a different.
Christopher S. Penn – 07:31
Chris says that every B2B company is struggling with how to roll out AI, and many CEOs think it is non-strategic and just tactical. “Just go and do some AI.” What are the high-level metrics that you found that can be used with executive teams to show the strategic importance of AI?
Katie Robbert – 07:57
I feel like this is a bad question, and I know I say that. One of the things that I’m currently working on: If you haven’t gotten it yet, you can go ahead and download our AI readiness kit, which is all of our best frameworks, and we walk through how you can get ready to integrate AI.
You can get that at TrustInsights.ai/AIKit. I’m in the process of turning that into a course to help people even further go on this journey of integrating AI.
And one of the things that keeps coming up: so unironically, I’m using generative AI to help me prepare for this course. And I, borrowing a technique from Chris, I said, “Ask me questions about these things that I need to be able to answer.”
Katie Robbert – 08:50
And very similar to the question that this other Chris is asking, there were questions like, “What is the one metric?” Or, “What is the one thing?” And I personally hate questions like that because it’s never as simple as “Here’s the one thing,” or “Here’s the one data point” that’s going to convince people to completely overhaul their thinking and change their mind.
When you are working with your leadership team and they’re looking for strategic initiatives, you do have to start at the tactical level because you have to think about what is the impact day-to-day that this thing is going to have, but also that sort of higher level of how is this helping us achieve our overall vision, our goals.
Katie Robbert – 09:39
One of the exercises in the AI kit, and also will be in the course, is your strategic alignment. The way that it’s approached, first and foremost, you still have to know what you want to do, so you can’t skip the five Ps.
I’m going to give you the TRIPS homework. TRIPS is Time, Repetitive, Importance, Pain, and Sufficient Data. And it’s a simple worksheet where you sort of outline all the things that I’m doing currently so you can find those good candidates to give those tasks to AI.
It’s very tactical. It’s important, though, because if you don’t know where you’re going to start, who cares about the strategic initiative? Who cares about the goals? Because then you’re just kind of throwing things against the wall to see what’s going to stick. So, do TRIPS.
Katie Robbert – 10:33
Do the five P’s, go through this goal alignment work exercise, and then bring all of that information—the narrative, the story, the impact, the risks—to your strategic team, to your leadership team.
There’s no magic. If I just had this one number, and you’re going to say, “Oh, but I could tell them what the ROI is.” “Get out!”
There is an ROI worksheet in the AI kit, but you still have to do all those other things first. And it’s a combination of a lot of data. There is no one magic number. There is no one or two numbers that you can bring. But there are exercises that you can go through to tell the story, to help them understand.
Katie Robbert – 11:24
This is the impact. This is why. These are the risks. These are the people. These are the results that we want to be able to get.
Christopher S. Penn – 11:34
To the ROI one, because that’s one of my least favorite ones. The question I always ask is: Are you measuring your ROI now? Because if you’re not measuring it now, then you’re not going to know how AI made a difference.
Katie Robbert – 11:47
It’s funny how that works.
Christopher S. Penn – 11:48
Funny how that works. To no one’s surprise, they’re not measuring the ROI now. So.
Katie Robbert – 11:54
Yeah, but suddenly we’re magically going to improve it.
Christopher S. Penn – 11:58
Exactly. We’re just going to come up with it just magically. All right, let’s see. Let’s scroll down here into the next set of questions from your session.
Christine asks: With data analytics, is it best to use Data Analyst and ChatGPT or Deep Research? I feel like the Data Analyst is more like collaboration where I prompt the analysis step-by-step. Well, both of those so far.
Katie Robbert – 12:22
But she didn’t say for what purpose.
Christopher S. Penn – 12:25
Just with data analytics, she said. That was her.
Katie Robbert – 12:28
But that could mean a lot of different things. That’s not—and this is no fault to the question asker—but in order to give a proper answer, I need more information.
I need to know. When you say data analytics, what does that mean? What are you trying to do?
Are you pulling insights? Are you trying to do math and calculations? Are you combining data sets? What is that you’re trying to do?
You definitely use Deep Research more than I do, Chris, because I’m not always convinced you need to do Deep Research. And I feel like sometimes it’s just an added step for no good reason. For data analytics, again, it really depends on what this user is trying to accomplish.
Katie Robbert – 13:20
Are they trying to understand best practices for calculating a standard deviation? Okay, you can use Deep Research for that, but then you wouldn’t also use generative AI to calculate the standard deviation.
It would just give you some instructions on how to do that. It’s a tough question. I don’t have enough information to give a good answer.
Christopher S. Penn – 13:41
I would say if you’re doing analytics, Deep Research is always the wrong tool. Because what Deep Research is, is a set of AI agents, which means it’s still using base language models.
It’s not using a compute environment like Colab. It’s not going to write code, so it’s not going to do math well.
And OpenAI’s Data Analyst also kind of sucks. It has a lot of issues in its own little Python sandbox. Your best bet is what you showed during a session, which is to use Colab that writes the actual code to do the math.
If you’re doing math, none of the AI tools in the market other than Colab will write the code to do the math well. And just please don’t do that. It’s just not a good idea.
Christopher S. Penn – 14:27
Cheryl asks: How do we realistically execute against all of these AI opportunities that you’re presenting when no one internally has the knowledge and we all have full-time jobs?
Katie Robbert – 14:40
I’m going to go back to the AI kit: TrustInsights.ai/AIKit. And I know it all sounds very promotional, but we put this together for a reason—to solve these exact problems. The “I don’t know where to start.”
If you don’t know where to start, I’m going to put you through the TRIPS framework. If you don’t know, “Do I even have the data to do this?” I’m going to walk you through the 6 Cs. Those are the frameworks integrated into this AI kit and how they all work together.
To the question that the user has of “We all have full-time jobs”: Yeah, you’re absolutely right. You’re asking people to do something new. Sometimes it’s a brand new skill set.
Katie Robbert – 15:29
Using something like the TRIPS framework is going to help you focus. Is this something we should even be looking at right now? We talk a lot about, “Don’t add one more thing to people’s lists.”
When you go through this exercise, what’s not in the framework but what you have to include in the conversation is: We focused down. We know that these are the two things that we want to use generative AI for.
But then you have to start to ask: Do we have the resources, the right people, the budget, the time? Can we even do this? Is it even realistic? Are we willing to invest time and energy to trying this?
There’s a lot to consider. It’s not an easy question to answer.
Katie Robbert – 16:25
You have to be committed to making time to even think about what you could do, let alone doing the thing.
Christopher S. Penn – 16:33
To close out Autumn’s very complicated question: How do you approach conversations with your clients at Trust Insights who are resistant to AI due to ethical and moral impacts—not only due to some people who are using it as a human replacement and laying off, but also things like ecological impacts? That’s a big question.
Katie Robbert – 16:58
Nobody said you have to use it. So if we know. In all seriousness, if we have a client who comes to us and says, “I want you to do this work. I don’t want you to use AI to complete this work.”
We do not—it does not align with our mission, our value, whatever the thing is, or we are regulated, we’re not allowed to use it.
There’s going to be a lot of different scenarios where AI is not an appropriate mechanism. It’s technology. That’s okay.
The responsibility is on us at Trust Insights to be realistic about. If we’re not using AI, this is the level of effort.
Katie Robbert – 17:41
Just really being transparent about: Here’s what’s possible; here’s what’s not possible; or, here’s how long it will take versus if we used AI to do the thing, if we used it on our side, you’re not using it on your side.
There’s a lot of different ways to have that conversation. But at the end of the day, if it’s not for you, then don’t force it to be for you.
Obviously there’s a lot of tech that is now just integrating AI, and you’re using it without even knowing that you’re using it. That’s not something that we at Trust Insights have control over. We’re.
Katie Robbert – 18:17
Trust me, if we had the power to say, “This is what this tech does,” we would obviously be a lot richer and a lot happier, but we don’t have those magic powers. All we can do is really work with our clients to say what works for you, and here’s what we have capacity to do, and here are our limitations.
Christopher S. Penn – 18:41
Yeah. The challenge that companies are going to run into is that AI kind of sets a bar in terms of the speed at which something will take and a minimum level of quality, particularly for stuff that isn’t code.
The challenge is going to be for companies: If you want to not use AI for something, and that’s a valid choice, you will have to still meet user and customer expectations that they will get the thing just as fast and just as high quality as a competitor that is using generative AI or classical AI.
And that’s for a lot of companies and a lot of people—that is a tough pill to swallow.
Christopher S. Penn – 19:22
If you are a graphic designer and someone says, “I could use AI and have my thing in 42 seconds, or I could use you and have my thing in three weeks and you cost 10 times as much.” It’s a very difficult thing for the graphic designer to say, “Yeah, I don’t use AI, but I can’t meet your expectations of what you would get out of an AI in terms of the speed and the cost.”
Katie Robbert – 19:51
Right. But then, what they’re trading is quality. What they’re trading is originality.
So it really just comes down to having honest conversations and not trying to be a snake oil salesman to say, “Yes, I can be everything to everyone.” We can totally deliver high quality, super fast and super cheap.
Just be realistic, because it’s hard because we’re all sort of in the same boat right now: Budgets are being tightened, and companies are hiring but not hiring. They’re not paying enough and people are struggling to find work.
And so we’re grasping at straws, trying to just say yes to anything that remotely makes sense.
Katie Robbert – 20:40
Chris, that’s where you and I were when we started Trust Insights; we kind of said yes to a lot of things that upon reflection, we wouldn’t say yes today. But when we were starting the company, we kind of felt like we had to.
And it takes a lot of courage to say no, but we’ve gotten better about saying no to things that don’t fit.
And I think that’s where a lot of people are going to find themselves—when they get into those conversations about the moral use and the carbon footprint and what it’s doing to our environment.
I think it’ll, unfortunately, be easy to overlook those things if it means that I can get a paycheck. And I can put food on the table. It’s just going to be hard.
Christopher S. Penn – 21:32
Yep. Until, the advice we’d give people at every level in the organization is: Yes, you should have familiarity with the tools so you know what they do and what they can’t do.
But also, you personally could be working on your personal brand, on your network, on your relationship building with clients—past and present—with prospective clients.
Because at the end of the day, something that Reid Hoffman, the founder of LinkedIn, said is that every opportunity is tied to a person. If you’re looking for an opportunity, you’re really looking for a person.
And as complicated and as sophisticated as AI gets, it still is unlikely to replace that interpersonal relationship, at least in the business world. It will in some of the buying process, but the pre-buying process is how you would interrupt that.
Christopher S. Penn – 22:24
Maybe that’s a talk for another time about Marketing in the Age of AI. But at the bare minimum, your lifeboat—your insurance policy—is that network.
It’s one of the reasons why we have the Trust Insights newsletter. We spend so much time on it.
It’s one of the reasons why we have the Analytics for Marketers Slack group and spend so much time on it: Because we want to be able to stay in touch with real people and we want to be able to go to real people whenever we can, as opposed to hoping that the algorithmic deities choose to shine their favor upon us this day.
Katie Robbert – 23:07
I think Marketing in the Age of AI is an important topic. The other topic that we see people talking about a lot is that pushback on AI and that craving for human connection.
I personally don’t think that AI created this barrier between humans. It’s always existed. If anything, new tech doesn’t solve old problems.
If anything, it’s just put a magnifying glass on how much we’ve siloed ourselves behind our laptops versus making those human connections. But it’s just easy to blame AI. AI is sort of the scapegoat for anything that goes wrong right now. Whether that’s true or not.
So, Chris, to your point, if you’re reliant on technology and not making those human connections, you definitely have a lot of missed opportunities.
Christopher S. Penn – 24:08
Exactly. If you’ve got some thoughts about today’s mailbag topics, experiences you’ve had with measuring the effects of AI, with understanding how to handle data quality, or wrestling with the ethical issues, and you want to share what’s on your mind?
Pop by our free Slack group. Go to TrustInsights.ai/analyticsformarketers where over 4,000 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/TIPodcast and you can find us at all the places that fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.
Katie Robbert – 24:50
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.
Katie Robbert – 25:43
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 Metalama.
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 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.
Katie Robbert – 26:48
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.
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