So What? How to Identify Generative AI Use Cases

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In this episode of So What? The Trust Insights weekly livestream, you’ll learn how to identify AI use cases for marketing. Discover the six specific ways you can leverage generative AI for your marketing needs. You’ll also learn how to use the Trust Insights 5P framework to determine whether or not a task is right for AI. Stop wasting time on tasks you hate and start using AI to do them for you!

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In this episode you’ll learn:

• The major use cases of generative AI
• How to identify Generative AI use cases for yourself
• How to build an action plan with your use cases

Transcript:

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

Katie Robbert 00:33
Well, hey everyone. Happy Thursday. Welcome to so what, the marketing, analytics, and insights live show. I am Katie, joined by Chris. John is off on a super-secret mission that we can’t talk about — aka, he’s at the dentist — but he’d probably rather be on a super top-secret mission. I think most of us would.

So, this week we are talking about how to identify generative AI use cases. We are going to review what the major use cases are for generative AI, how to identify use cases for your own personal use — personal, professional — and how to build an action plan with your use cases. So Chris, let’s start with the technological elephant in the room. What the heck are you wearing?

Christopher S. Penn 01:24
This is a good analogy for generative AI use cases. So, this strange contraption is a stable — theoretically it’s a stabilizer — the shoulder-mounted stabilizer and there’s a GoPro on the end of it. And so, you would take this thing, you put it on, you turn it on, your camera records. It’s the — this is one of the 360-degree cameras — you walk around with this ridiculous thing on and it’s taking video the whole time. And then later on, you can load that into, like, your Oculus and sort of relive the immersive experience.

And I think this is a good example of why we’re talking about gender — AI use cases — because for a lot of people they’re very much as though, what the heck is this thing? I mean, what’s going on?

Katie Robbert 02:10
I think my initial reaction was just no.

Christopher S. Penn 02:15
Exactly. It’s like, what the heck is this thing? Why is it even here? What is it supposed to do? What even is this thing supposed to do? And then, okay, well, once you understand what it’s supposed to do, then how do you apply that knowledge of what the thing is supposed to do? And then, of course, you have to say, does it actually do it? Unsurprisingly, this sounds very familiar.

Katie Robbert 02:41
Well, so before we get into the obvious answer of the five P’s, I have questions. Okay, so you said it theoretically is a stabilizer. So, does that mean it’s not? Does it work? And why wouldn’t you just be present in the moment and not need to have — I don’t know what Oculus is, but I assume like, a VR recreation of the thing — so go.

Christopher S. Penn 03:06
See, this, though, is exactly what we want to do with use case analysis and understanding. So, does it work? No. The reason it doesn’t work is because all these elastic straps do not make a secure connection to your body, which means it’s bouncing around. So, your footage is bouncing around too. Exactly. So, this does not work. This is actually a leftover from our old agency days. I bought it back then to try and do immersive video and it does not work.

The camera works great. Why would you even want that? Well, because you’re at some place where you want immersive video to be able to — yes, relive later, but also be able to share. When you do 360 video, you can cut out snippets, so you don’t have to worry about getting something in frame. Everything’s in frame.

Christopher S. Penn 03:53
So, you’d be able to capture key moments. You would wear this, for example, if you were at a concert — assuming you’re permitted to do so — a concert, a conference, any kind of thing where there’s some kind of virtual experience you want to be able to look back on later. Especially I take stuff like this with me when I travel if I can, because there’s some places that I’m like, I don’t know that I’m ever going to get back here again. So, I want to be able to look at a lot of that data.

When we’re talking about generative AI in use cases, it’s very similar. It’s like, okay, what is the thing? What’s it supposed to do? Does it do it? Is it the equivalent of just bouncing around footage? And then great. So, I understand what it does.

Christopher S. Penn 04:38
How am I supposed to use it and get value out of it?

Katie Robbert 04:42
Okay, that makes a little more sense. So, if we’re thinking about the generative AI use cases, then yes, I agree that the five P’s is a really good place to start. So, for those unfamiliar, or just as a refresher, the Trust Insights five P framework is purpose, people, process, platform, performance.

Purpose: What is the question I’m answering? What is the problem I’m trying to solve? People: Who’s involved internally, externally? Process: How am I going to do the thing in a repeatable, scalable way? Platform: What are the tools that I need? Performance: Did I answer the question? Did I solve the problem? Very straightforward, but it can use a lot of different ways. So today, we’re going to be using it to figure out AI use cases. And so, Chris, I know you were actually writing about use cases yesterday on LinkedIn.

Katie Robbert 05:38
Do you want to talk about that a little bit?

Christopher S. Penn 05:41
We can, and I think it’s a good example of a use case, but I think we first should start talking about what the — like, again, back to the ridiculous thing — with generative AI, what does it even do? Most people’s interactions with generations going to be in one of two ways: it’s going to be in image generation — it’s like — or it’s going to be in language generation.

So, tools like Midjourney for image generation, tools like ChatGPT for text generation, those are the two kind of big branches. And what they do is that they ingest data. You give it either in a form of prompt or prompt plus some information. They rely on their own knowledge that they’ve been trained with, and then they produce data, typically in the form of whatever you told it to do.

Christopher S. Penn 06:28
Write a blog post, create a corporate strategy, “Help me, help me write a letter to my favorite celebrity” — whatever the thing is. That’s what the tools broadly do. They take in data, they transform it and adjust it, and they put out more data.

Katie Robbert 06:44
Okay, so then where do we start with figuring out what my use cases are? Because — and I don’t mean me specifically, I just mean sort of the gender you. Because the way that, for example, the way that I use generative AI is different from how you use generative AI. And I think that’s an important thing to note because a lot of companies, at least from my perspective, are basically saying, “Here’s generative AI, use it.”

But when you start to look at the individuals in a company, every single person has a different set of needs, so they’re going to use it in a different way. So, just thrusting upon them one way to use a tool that does a lot of things is a recipe for disaster.

Christopher S. Penn 07:29
It is. So, the first thing you have to figure out when you’re talking about any tool — like generative AI is — okay well yeah, it takes in data, processes, it puts out data, but that’s really vague. What are the specific ways that you can do it? And there’s six specific ways:

There’s generation. You give it instructions. That makes something. There’s extraction. You give it something and some data and say, “Pull out [a] portion of data” from here. There is summarization. You get a prompt to say, “Here’s big data, turn [it] to small data”. There’s rewriting. “Here’s data in one format, turn it into another format,” from, “Rewrite this memo in a professional tone of voice”, to “Translate this into Ukrainian.” You’re transforming the data. Fifth is classifying data.

Christopher S. Penn 08:12
“Here’s some data, help me organize it, help me understand it better, help me understand characteristics of it.” And the sixth is synthesis. “Here is a bunch of data, glue it together or put it in one place, and then let me ask questions of it, let me transform it into other things, but I want to merge and then transform data.”

So, those are the big six use case categories for generative AI, at least with language models, particularly. And so part of then the use case categorization is in the five P’s, looking at something like process and saying, well, what do I currently do? Which of those tasks or parts of those tasks fits in one of those six general categories? And then is it appropriate to use AI to do that task?

Christopher S. Penn 09:04
So, to your question the other day we were talking about categorizing content on the Trust Insights website, categorizing our blog specifically. You want to talk a little bit about that?

Katie Robbert 09:16
Yeah, so like a lot of companies, we deprioritize ourselves in terms of our own website maintenance — content cataloging. We just don’t have a dedicated resource to do it. So, it’s sort of a whoever has time kind of picks up the ball. And what I was asking was based on a post that Chris had written a few months ago about how he had applied a content categorization analysis to the content on his website that basically told him, “Here are the things you’re writing about, here’s what we suggest. You sort of cluster them as,” and then you could sort of figure out the gaps from there.

Katie Robbert 09:58
And so, I wanted to do the same thing with the Trust Insights content because when we started the company six and a half years ago, it was myself and Chris henpecking against the website, hoping that [we] were going to remember to add categories to our content. And so now, where we publish multiple pieces of content a day, every day, it’s going to be a lot to go back one by one to say, okay, are these categorized correctly? Our services have evolved. Are these pieces of content in the right place? Are they missing categorization?

And so Chris, you were able to use generative AI to start the process. So, at least right now we know what things are categorized as. Now, the next step is going to be figuring out what should they be categorized as. So that’ll be the next step in the analysis.

Katie Robbert 10:54
But, it’s a use case that’s specific to us. But I can see probably other marketing teams would want to use it because it helps searchability on your website. It helps if you’re doing cornerstone content, so you have your anchor piece and all of the supporting pieces. And then it helps [to] sort of know, did I even write about this thing yet? How much have I written about this thing yet? Where can I easily find resources to point people to about this question that they keep asking?

Christopher S. Penn 11:30
Exactly. So, what we did was we wrote some code to take the website, take all of our blog posts, and then apply some basic classification. So, this is use case number five. We’re classifying the text content of our blog posts. And then in the middle of it all, we had a bit of a pivot because we realized that a language model can be directed to do more than just classification. It can also be used to do scoring, to do synthesis. It’s a form of summarization.

What we’ve got running now is taking every post and then taking our ideal customer profile, which you learned about or should have learned about on previous episodes of both the live stream and the podcast — if you have not had a chance to go, that you can get the back issues at Trust Insights, AI, YouTube.

Christopher S. Penn 12:25
What it’s doing now is it’s taking our ideal customer profile that you generated, Katie, and it is scoring every post and saying, “How likely is it that this post, this blog post, this newsletter, this live stream transcript, whatever would be satisfying to our ideal customer profile?”

And this process for us — we got our 1400 pieces of content — you could do this by hand, you would be doing it for a very long time. But instead, it’s processing the data piece by piece and appending the scores to our internal data set. What we will do with that after is look at several things.

Number one, we’re going to look at — I call this alignment — our content alignment score over time. Is it going up or is it going down? Are we getting further away from what the ICP wants? Are [we] getting closer to it?

Christopher S. Penn 13:17
Hopefully, we’re getting closer to it. Two, are there specific branches of content that have higher scores? Does the podcast score higher with our ICP? Does our live stream, does our newsletter, do our data diaries? Does Katie’s cold opens? Because we have good taxonomy on our website, we can bucket the URLs with their associated scores and say, “The podcast is killing it.” We are, it’s good for our ICP, but our live stream is kind of all over the place. The scores aren’t consistent, so we know we need to focus more on it. And so this is a classification tool being used with generative AI. So that’s what that use case is.

Katie Robbert 13:57
Which I think is — it’s interesting because it did evolve out of where we originally started for what we wanted to do with it. So, if we go back to the five P’s. And if you want your own instant insights, you can go to TrustInsights.ai, five P framework, and get a worksheet for how to run through the five P’s. So we started with the purpose [of]: I want to be able to make sure that my content is categorized correctly. So, I need to understand the current state, the people — so, you and me — the process. Well, and also our ideal customer profile because I think that’s sort of where people forget is, it’s not just the people doing it, it’s who’s the end user of the thing.

Katie Robbert 14:43
And we needed to be mindful of what they wanted, which is where our ICP came into play. Process: it’s a variety of things, including writing code. Platform: our website, our content, the code, et cetera. And then the performance. Our measure of success. The measure of success is that we are offering content the way that people want it, that the — I guess if we were to have to like, put numbers to it — that the visits to specific pieces of content go up, or engagement on social when we post certain pieces of content go up. So, there’s ways to measure, did we get it? This is our use case. So, let’s talk about how people get to their own use case.

Katie Robbert 15:34
And the way that people get to their own use case is to start with the purpose: what is the problem you’re trying to solve? So, the problem you’re trying to solve isn’t “I need to find a use case to use generative AI”. The problem you’re trying to solve is “I need to find efficiencies,” or “I need to increase sales,” or “I need to generate content faster,” or “I need to reach the right people,” or “I need to increase profit margins,” or whatever the thing is. That’s your purpose. Then you go through the other pieces to find your specific use cases.

Christopher S. Penn 16:09
Exactly. And one of the things that we don’t think about enough with generative AI is that we tend to think of generative AI as the objective. And even when it spits out something like an ideal customer profile, we think that is the performance. “That’s it, we did the thing,” but we don’t think about it in terms of ordered effects. Just because you have an ideal customer profile does not mean you have something useful.

You have a piece of data, you still now need to activate that data and do something with it. You need to ask questions of it, you need to score your existing content against it, and so on and so forth.

Christopher S. Penn 16:44
So, whatever the generative AI use case you’re tackling is, it then has to have second and third and fourth-order effects of, what are you going to do with that thing? So, in this case, with our code, the outcome is an alignment score. That’s not the end purpose because — yeah, it’s cool. It’s a number, you can make a chart out of it — but what do you do with it?

You then say, “Okay, well, now I know that the podcast is not as well-aligned.” Okay, well then what’s the next step? “So, what? So, what is?” You need to — we need to do a better job scripting our podcast. We need to do topics in advance and score them against the ICP to say, “Yep, this is the topic we got to do.”

Christopher S. Penn 17:23
Maybe it’s not the thing we’re most excited about, but it’s what’s going to help the audience the most.

Katie Robbert 17:28
Well, and I think that’s — that’s the interesting thing about a lot of this is when you go through the five P’s, when you get to people, you may just be the person pushing the buttons, but the end result may not be about you. And I think that’s where a lot of people get it wrong.

“The problem I’m trying to solve is I need to generate more content.” Why you need a user story? “As the content marketer, I want to generate more content so that I can reach my audience.” So, it’s about your audience. It’s about being helpful or informing the people that you want to reach. And so, when you go back to the five P’s, the problem you’re trying to solve is create more content. The people: it’s you and your ICP.

Katie Robbert 18:21
So, it’s about what they need, not about what you need. And so, I think, Chris, that was an important point to bring up because a lot of times we’re looking at using generative AI as “how does it make it better for me, what’s fun for me?” And those are important things to consider. But in a business, it’s likely going to go beyond just you, the individual.

Christopher S. Penn 18:46
Exactly. Now, other ways to use these things, you have to almost reverse-engineer, kind of from the performance. Like, what are you doing now and what’s the performance of it now?

I’ll give you a real good example: writing scopes of work. So, something that we do as a B2B management consulting firm all the time. I don’t enjoy it, it is not a fun task. I enjoy having the conversation. I love talking to people about the challenges they’re facing. I love talking to people about possible solutions. I don’t love the — “All right, now I have to turn this into a piece of paper that someone has to review and sign.” And so, the consequences of me not loving it happens down in both the performance of the process side.

Christopher S. Penn 19:29
The process side is I cut corners and the performance outcome is the scopes of work are not complete. They don’t cover what the audit the customer cares about. And sometimes if I forget something really important, then we’re kind of stuck with what we said we would do in the contract instead of what we should be doing.

So, the use case here for generative AI is not just to write the scope faster — to get an unpleasant task done faster — but to write it faster and better. To say, “You know what Chris? You do a mediocre job because you don’t want to do this task. Let’s let a machine do the first draft so that it’s comprehensive, so that it covers the main point, so that it’s coherent.

Christopher S. Penn 20:16
“Like, hey, you listed a bunch of things in tactics and there’s no deliverables for any of them. Or the deliverables are a mismatch for the tactics. Or you said in the tactics it’s going to take six weeks. And in the timeframe timeline, you said it’s going to take two weeks. Which one is it?”

So, the use case there again — going back to — is partly generation, but it’s also summarization because you’ll take something like a call transcript and say, “Here’s what we talked about. Summarize this down and then generate a scope of work from it.” And now, we’ve got two different use-case categories together, like Lego blocks, or like Voltron’s lion robots putting together to create something that’s better than what the person would’ve done alone, when the person isn’t going to do as good a job as a machine.

Katie Robbert 21:00
In that use case, have you measured their performance to see if the scopes that you use generative AI to write are better and/or close more?

Christopher S. Penn 21:10
I have not done an assessment of whether they close more. I have — I have qualitative feedback, mainly from you, that there are fewer comments like, “This is missing. What?” There’s more of “What does this mean? Or what are you trying to say?” Or “Did you write this machine,” but less of “Where’s this? This is not here.” Yeah. “You’re missing this piece. You’re missing this piece.” And stuff. So, it’s now a question of things no longer missing that are obvious or things don’t make no sense whatsoever. It’s now, “This isn’t clear. Explain this more, or is this too much explanation?” Because sometimes our tools get verbose.

Katie Robbert 21:53
It’s interesting because either way, the scopes need human reviewer — typically me — and it’s easier to cut back than it is to add more because I wasn’t there for the conversation. So, I don’t know what’s missing or I can’t say, “Well, you have six weeks here. You have, you know, nine weeks here. Which one is it?” So, I will say in terms of — for lack of a better term — like, all the pieces aligning, it’s better for that. And then, it just needs to be streamlined a bit. But, it’s much easier to streamline something once you have all of the information — or more than you need — than it is to sort of go, “Well, I have three of the eight things. Where are the other five going to come from?” So, it is definitely better in that respect. I will say that.

Christopher S. Penn 22:49
Yep. And this is a critical thing about gendered AI use cases is: you don’t have to hand over everything to AI. You shouldn’t hand over everything to AI, nor should you hand over the things to AI that you enjoy doing. There’s this bizarre sort of belief that AI is going to take over everything and that you will no longer be able to do the work that you enjoy.

No, the purpose of AI is to do stuff that you direct it to do. So, if there’s something you enjoy doing — if you’re a person who likes writing scopes of work, and I know there are business development people who do — if you like that, don’t hand that to a machine. Hand off something else to a machine that you don’t enjoy doing.

Katie Robbert 23:26
And for me, that’s creating PowerPoint presentations. That is the bane of my existence. And so, my specific use case — and I was asking around our team earlier this week [about] what my use case [was].

The purpose for me is, as a speaker, I need to create a supplemental PowerPoint deck so that the audience can follow along with what I’m talking about at an event. People: it’s me, but it’s also the audience at the event. Process: I’ve built the outline. I know the pieces I want to talk about. I’ve thought about our ICP and the information they want to learn. So, I’ve had — put the pieces together — platform: I use generative AI to help me put my thoughts together that I had given to — given to ChatGPT and I was like, “Help me frame out a talk.”

Katie Robbert 24:26
And now, I need to put that outline into a PowerPoint deck. And so, I’m actually looking for generative AI to do it for me because I already know the thing is I just don’t want to sit there and copy and paste text into a slide. I just don’t want to do it. It is so mind-numbing for me. And I usually don’t mind the monotonous tasks, but this one in particular — I just don’t want to do it. So, I’m actually looking for generative AI to do it for me.

The performance not being that I got a PowerPoint deck done, but that I got a supplemental deck completed that helps the audience at my talk follow along and understand. And so that the information resonates with them.

Christopher S. Penn 25:13
Exactly. And now, you’ve run into issues where — there aren’t a lot of tools that do that well. And the reason for that goes back to where we started the episode, which is that there’s two different major models in generative AI: there’s diffusers, which make images, and there’s transformers, which make text.

Transformers generally don’t do a good job of making images. That’s not what their architecture supports. Diffusers can’t do text. And so, you are taking a task that is taking text and turning it into an image, which should theoretically be a diffusion task. However, what’s important is that the content itself makes it into the image. And that’s something that diffusers can’t do because diffusers can’t understand language.

Christopher S. Penn 25:55
And so, you have this case where this task is really almost symbolic in some way as opposed to literal and neither model does a good job of it. Together, they each have their own separate strengths. So, a really important part of generative AI use case identification is understanding: what kind of task are we doing? And is the technology capable of doing that? In this case, that is that big conceptual leap from image to — from text to image — that reflects the content of the text. And the tools today are not up to that.

Katie Robbert 26:30
Well, and that’s assuming that I want a bunch of images. I don’t mind if the text itself makes it into a PowerPoint and then I have to put in some images of my own, but I just don’t want to go through and structure the deck itself with all the words and the thing. Someone else do it.

Christopher S. Penn 26:50
This is an interesting point then because you’re now talking about something that is partly language-based and partly not. So, the way that you would handle that — and again, this is use case development — is to say, what is the tool? What is it capable of? In this case, arranging a coherent structure for a presentation is a language task. And that is 100% something the tools can do. “Slide one, slide two, slide three. This is what should be on each slide.”

Making the presentation is not a language task. That is a file task that programming language — like Python — can do. Python can make PowerPoint slides and glue them together into a deck. What Python can’t do is write the slides for you.

Christopher S. Penn 27:34
So, in this case, your use case is a combination of a pure language task and then having a language model write the code to make the slides to put the text into that is a doable thing. However, it is a more advanced use case because you’re now getting into Python coding and it may or may not be the best use of your time. Now, is it a fun problem to solve? Absolutely. I would do that for fun.

Katie Robbert 28:02
Well, but I think to that of being [the] best use of your time, I think it really depends on how often — and this gets into the repeatability. So, one of the ways that we start to document these use cases is with our TRIPS framework. You can find that at TrustInsights.ai/TRIPS. And so, it’s time, repeatability, importance, pleasantness, and sufficient data. So, in this case, if I’m thinking about creating a PowerPoint deck: time — longer than it should take because I’m going to drag my feet — but let’s call it two hours, give or take — repeatability: it’s a talk that I can use over and over again. So, I would say it has high repeatability, importance. Well, they expect me to give something when I get on stage. So, I say it’s highly important that I get it done. Pleasantness: zero.

Katie Robbert 29:00
I enjoy giving the talk. I don’t enjoy making the slideshow. So. But, that’s what we’re talking about, is zero. Insufficient data is great because I already outlined the whole talk.

Christopher S. Penn 29:09
Exactly. So, if you’ve got the outline now, you have to give the model a prompt for turning the outline into slides and then, secondarily, having a model build the Python script to take the slide data and one by one create the slides in PowerPoint format in a file on your computer. That’s how you would approach this particular use case.

Katie Robbert 29:31
All right, so people wanting to break into the AI-powered market, take note: this is a use case that I guarantee I am not the only person who wants a system to just take the information and make a PowerPoint deck. So, if someone invents that thing, let me know. Hit me up. I’m ready to be your beta tester. I will give you a testimonial. I am ready to not have to make a PowerPoint deck ever again.

Christopher S. Penn 30:00
Do we want to try and engineer that live, or at least a facsimile of that?

Katie Robbert 30:06
Sure, let me send you my outline.

Christopher S. Penn 30:08
Okay, while you’re doing that I’m going to fire up in this system. I’m going to use Google’s Gemini 1.5, the pro model, and we’re going to start off with the PARE framework. So, if you’re not familiar, the PARE framework is from Trust Insights. All these things, by the way, are totally free. There’s not even a form to fill out.

I’m going to start off with a PARE prompt and I’m going to say, “Our topic today is creating a compelling slide presentation outline.” What’s the topic?

Katie Robbert 30:42
Managing the people who manage the machines.

Christopher S. Penn 30:45
“Managing the people who manage AI technology.” So, that’s going to be — I’m going to put that in quotes just so the model knows to do that — and now it’s going to start creating — I need to turn off this banner here — it’s going to start creating the PARE framework. So, we have it automatically choose the role because that’s what it’s designed to do. It’s going to outline the best practices — this is priming the model.

“So, developing a clearance structuring outline. Think visually.” It’s then going to move automatically into the augment phase here. “Common mistakes when creating a presentation outline about managing the people who manage the machines.” It’s then going to look at some of the truths and falsehoods. So, what are the things that are generally true, but maybe false? What things are generally considered false, but might be true?

Christopher S. Penn 31:39
So, it’s going to go ahead and do that and then tell me my expert tips and tricks for this particular task. So, we’ve got now the model priming done. And again, this is — by the way — this is something that we teach in our AI course, which is completely not free.

Katie Robbert 31:59
Sorry, not sorry.

Christopher S. Penn 32:02
All right, so we’re going to now say, “Great. I’m now going to provide you with the outline of a talk. I’d like you to separate the outline into individual slides. No slide should contain more than one key concept. We want very few words per slide. Here is the outline.” And we’ll take your “Imagine People Who Match the Machines” talk, convert this into a slide-by-slide outline, as instructed.

So, we’ve got this lovely outline and now: “Slide one, name, slide two,” et cetera. “Yeah, image, text,” and so on and so forth. So now, it’s going to create our individual slides. And this alone — I mean, it would be copy-and-paste, a decent amount of copy-and-paste.

Katie Robbert 33:20
But this alone definitely gets me closer to the end goal.

Christopher S. Penn 33:25
Exactly, we’re in step four. Now, we’re going to say, “Next, I need to pull in my Python coding instructions because I have a long list of things that are good Python coding practices.” This is at eight pages of coding. So, I’m going to load that in system instructions. “Let’s see. Next, I’d like you to outline a Python script that will take each of the slide’s text from the above outline and generate a PowerPoint slide. The script should create one single presentation composed of multiple slides that choose the appropriate Python packages to complete this task. Optionally, we can include a CSV of slide number and slide text if it will make the process easier. Adhere to best practices as detailed in the system instructions and produce the skeleton of the script first.”

So, let’s see what it comes up with. This is now —

Christopher S. Penn 35:11
So, we’re now switching contexts from making the presentation itself and all the best practices to making the PowerPoint itself. So, you can see here that — go away. So, it’s now starting to create the outline of the code. This is actually already pretty good. You can see here that it’s got the main script execution section. It’s got slide data. It details the libraries we need, how it’s going to handle image handling. If you want to specify images, you could do that.

So, this is a very interesting little thing here. You could, if you wanted to get super crazy, you could ask the script to include an API call to something like Midjourney or OpenAI’s DALL-E or Meta AI and say, “Here’s the text for the slide, create an image, return the image,” and then have the script put it actually on the slide.

Christopher S. Penn 36:04
Now, I don’t know how good it would be-

Katie Robbert 36:05
But it would be interesting for some people. That’s going to work.

Christopher S. Penn 36:10
Yes, exactly. And so now, you would go through standard software development lifecycle stuff. You would take the code, test the code — augmented — every time it throws an error, come back and say, “Here’s the error, fix it,” until you get a working piece of software. So, in the span of six and a half minutes, we’ve taken your outline, turned it into a slide outline, and then built the skeleton for actually creating the power point deck.

Katie Robbert 36:39
Which is still faster than me putting everything into a PowerPoint deck, procrastinating, whining about it, closing it, coming back to it, telling people how much I hate it, then coming back to it. The process takes much longer than it should because — much like you writing scopes, it’s not something I enjoy, so it’s going to take longer than it should because I can’t — I could just sit down and power through and do it. But let’s be realistic, when we’re doing things we don’t like, we just don’t tend to do that. We put them on the back burner, we do them last. We give them the least amount of effort. And let me be clear: I love the topic, I love speaking about the topic. It’s literally just putting a slide deck together that I don’t enjoy.

Katie Robbert 37:34
So, I want people to know: I am happy to talk about it with your team — with anyone — on a stage…just give me a second to put the slides together.

Christopher S. Penn 37:46
And in terms of building the generative AI use case, one of the things that we did here, I think is really important to highlight, is: building the presentation seems like it’s a task, but it’s not — it’s a container. What we’ve done here is we’ve broken that task up into individual pieces.

First, take the outline and convert it into a slide outline — a very granular task. Then take the slide outline, turn it to a CSV. Okay. Then, take the CSV and put it into a — into a Python script. And then the Python script can converts it into a PowerPoint. So, what seems like a task of “make a slide deck,” is in fact multiple tasks bundled together.

Christopher S. Penn 38:26
And if you want to unlock generative AI use cases, a big part of the process is decomposing your tasks into their subtasks and saying, “Okay, that is a language problem we can solve. That’s a coding problem we can solve. That’s not a problem we can solve.”

Katie Robbert 38:43
And I want to go back to something you said about as you’re identifying the use cases: there’s a misconception that just because AI can do it, doesn’t mean it should do it. So, if you’re someone — for example — who enjoys putting together PowerPoint presentations, also get in touch with me. But, AI doesn’t have to do that if you really enjoy it and you’re really good at it. I personally don’t feel the need to use AI for writing a contract because it’s something that I don’t mind doing. Is it my favorite thing? No, but it’s certainly not the lowest on my list of things that I don’t like doing. So, I wouldn’t necessarily use AI for that. So, my use case and your use case, Chris, are just different.

Christopher S. Penn 39:33
Exactly. And other things is — again, looking at the way the technology itself works. So, if we go back to our discussion earlier about taking our blog and scoring our blog post by post — this is, even if you enjoyed the task once, doing it 1400 times in a row — this is an incredibly repetitive task. And so, having a language model perform this particular task is the only sane way to do it and get good results. However, even with this task, you still need experts in the process. So, part of when we created this use case: there are two halves of it. One is the technological capabilities to extract data out of WordPress, normalize the tables, turn it into a single table that AI could analyze, then testing out five different models to see which one created the best result.

Christopher S. Penn 40:34
And that was not the role of the AI or the AI engineer. That’s the role of you, the subject matter expert, to say, “Yeah, I agree with these scores more than these scores.”

Katie Robbert 40:46
And that actually — so now you can talk about that LinkedIn post. That’s what you were writing about yesterday, was: the need for the subject matter expert and the technological expert and the technology itself. And that’s like, the magic formula of making these things work the way you want them to.

Christopher S. Penn 41:08
Exactly. So, just as a quick example — this is from yesterday — we had the spreadsheet of the different pieces of content. Each model came up with scores, and we obviously had a look at what the content was. And you knew — based on our ideal customer profile, because you built the ideal customer profile — whether or not a model was making a good — making good judgment or not, and which models made really good judgment. You would look at a certain piece of content and go, “Our ICP wouldn’t be interested in that.” And so, if it got really high scores from a particular model, you would know that model is not interpreting the prompt properly. Its scoring algorithm is not good, and therefore, it just gets disqualified.

Christopher S. Penn 41:49
We end up settling on Meta’s Llama 3.18 B model because its scores are the most closely aligned to what the human reviewer said. “Yeah, this is the closest to reality with use cases.”

Part of use case development is having this human review to say, “Okay, as you’re doing your AI test cases and your pilots,” that performance — part of that performance is — it’s not, “Just wait for the project to be done.” It’s iterative. You have a lot of milestones along the way [that] say, “Yeah, it’s doing what it’s supposed to,” or, “No, it’s not.”

Katie Robbert 42:24
And I think that’s another misconception is that you just hand it off to AI and then step back and go, “Did it do the thing?” And, we’ve talked about this in different — form, like different places — but one of the challenges with using AI — well, is that it takes time. It takes a lot of human intervention. It’s not enough to just give it a one-line prompt. “It didn’t do the thing!” Get frustrated, throw your hands up, walk away. “AI can’t do that!”

We’ve shown — yes, more technical examples — but we had to actually give it the PARE framework. We had to start with RACE, we had to follow up with PARE. We had to be mindful of the five P’s. We had to think about, “Is this a task that we even should be doing with AI?”

Katie Robbert 43:17
And all of that upfront prep work is going to get you to: okay, these are the things I can do with AI that makes sense for me. And then you can start to measure: did I do it? Am I more efficient? Am I increasing profit margins? Am I doing whatever the problem it was I was trying to solve?

Christopher S. Penn 43:35
Exactly. And going back to the thinking about, “Can AI even do it?” We were working on another example yesterday of taking our keyword list from our SEO and our content marketing strategy and saying, “Here’s our ideal customer profile. What is the likelihood that our ideal customer profile would even search for this term?”

We’re using — we’re leveraging the power of language because this is clearly a language task. Wrap it in some code and then say, “Okay, now here’s our 1500 term keyword list. Score every keyword based on the likelihood that our ICP would search for that term.” And now we have a new version of our keyword list that is called an ICP-aligned keyword list.

Christopher S. Penn 44:22
So, just because the term gets a lot of search traffic doesn’t mean it’s a good choice for content strategy because it may be about something that your ideal customer does not care about and wouldn’t search for.

Like, we were talking about affiliate marketing. That’s very much a B2C thing, not a B2B thing. And we serve a lot more B2B. And so, that scored very low in the algorithm. And when you looked at it, you’re like, “That’s very low.” Yeah, that means it’s working because it’s saying, “Our ideal customer would not look at this.”

So, this is a case [where] we have the purpose [of]: we want a keyword list that’s focused on the things our ideal, our customers, would search for so that we’re creating good content. Our performance is very clearly: get more search from the right people.

Christopher S. Penn 45:08
And then, the people, process, and platform was building the technology, and then you and I were viewing it to create this — not only the use case, but then the actual output of this, is an ICP-aligned keyword list.

Katie Robbert 45:20
What’s interesting — and it’s really easy to digress and talk about the ICP more — but if you want to learn more about what we’re doing with building ideal customer profiles — the service isn’t on our website as of yet, but it will be — but, we are developing and rolling out that ICP service because one of the things that I’ve learned through doing — even just the exercise we were doing yesterday [where] we built our ICP and then we have the keyword list — is: now we have a tool that shifts the focus. So, when we created the keyword list, we created it with the lens of “What do we want to be known for?” So, it was about us, not about our customer.

Katie Robbert 46:05
Now we have basically a synthetic customer to use for brainstorming and we can say, “Just because this is what we want to be known for doesn’t mean this is what the customer wants.” And we are going to be able to — over the next few months — basically, shift the focus off of what we think to what the customer actually wants. And I feel like that’s going to be such a more powerful strategy for us rather than saying, “Well, we want to be known for data science.” Well, guess what? That’s not what our customers care about. And if we want to get more of our type of customer, we need to give them what they want, not what we think they want.

Katie Robbert 46:49
And so, that’s sort of like my little plug for, if you want to know more about how you can get your own ideal customer profile, you can reach out to us at TrustInsights.ai/contact. But, it’s going to help you when you’re trying to figure out those AI use cases. If you know who your customer is and what they want, you can then align that to the things that you’re currently doing to your services, and then figure out where AI fits in appropriately.

Christopher S. Penn 47:21
Exactly. So, we started this live stream with the crazy contraption.

Katie Robbert 47:28
I already forgot about that.

Christopher S. Penn 47:29
I already, like, blocked [the] contraption. Why does the crazy contraption exist? What does it even do? And how does it work? What are the use cases for?

Katie Robbert 47:41
That is so silly.

Christopher S. Penn 47:43
It is absolutely silly, but it highlights the point of when you’re building generative AI use cases, you need to know what the technology is capable of, what it’s not good at, whether it does it or not. Then, from there you need to say, “Well, how does it do the thing it does?” — the six major use cases of generative AI. Then, you take your five P’s: what do we want more? “We want to save time, save money, make money.” What tasks do those things? We then start to decompose those tasks into their individual components and say, “Can generative AI do all these? Some of these, none of these?” And from there you’ve got a use case, a generative AI use case.

Christopher S. Penn 48:19
Our use cases to: can outline talks very easily. Can build PowerPoint slides, less easily. Can’t do the fit and polish on the final slide. So, you know that the task is not totally done by AI, but parts of it can be. I think that’s the best methodology today for building generative AI use cases: to go through all these steps together and ultimately arrive at — “If this task,” — you know, going back to our TRIPS framework — “this task consumes an inordinate amount of time” — like reading 1400 blog posts — “it’s worth your time to use generative AI for it.” Any final parting thoughts, Katie?

Katie Robbert 49:00
I can’t wait until generative AI can make my PowerPoints for me. That’s — that’s all I care about right now.

Christopher S. Penn 49:07
All right. Well, that’s going to do it for this week’s live stream. We will see you all on the next one. Thanks for watching today! Be sure to subscribe to our show wherever you’re watching it. For more resources and to learn more, check out the Trust Insights podcast at TrustInsights.ai/tipodcast and a weekly email newsletter at TrustInsights.ai/newsletter. Got questions about what you saw in today’s episode? Join our free Analytics for Marketers Slack group at TrustInsights.ai/analyticsformarketers. See 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.