So What? Marketing Analytics and Insights Live
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In this episode of So What? The Trust Insights weekly livestream, you’ll learn how to deconstruct generative AI use cases to better utilize the technology. You’ll discover a simple framework to help you determine the purpose, people, process, platform, and performance involved in your generative AI use cases. You’ll find out how this process helps you create stronger requirements for your generative AI tools and avoid common pitfalls. Plus, you’ll learn how to save time and get better results from generative AI.
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In this episode you’ll learn:
- Why generative AI use cases often feel overwhelming
- What a deconstructed AI use case should have – the 5Ps
- How to deconstruct a generative AI use case from start to finish
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 – 00:41
Well, hey everyone. Happy Thursday! Welcome to so what? The Marketing Analytics and Insights Live show. I am joined by Chris and John. Hey, guys.
Christopher Penn – 00:48
Hello.
John Wall – 00:49
Oh, yeah, I pulled off the high five without being in my normal office.
Christopher Penn – 00:55
That’s…
John Wall – 00:56
That’s the plus.
Katie Robbert – 00:59
Pre-show, we were commiserating over our shared experiences of slipping on all of the black ice in New England. So, if you are also experiencing that, we feel for you. We have all now since fallen on the ice. Speaking of deconstructing our bodies — ourselves — it’s a terrible transition. Just work with me, people. This week: How to deconstruct generative AI use cases. The reason this came up was because what we’re seeing — and this is true for a lot of people, so we wanted to make sure people knew they were not in it alone — is there’s still a lot of question about where to start with generative AI.
Katie Robbert – 01:40
What we’re seeing, what we’re hearing, and sometimes this happens to us is, here’s a new tool, go make magic with it or show your value with it or find efficiencies or just basically do something extra on top of everything else you’re already doing with very little instruction. So what we wanted to do this week was give you some advice and tools and tips on how to deconstruct generative AI use cases so that you can better set yourself up for success. So Chris, where would you like to start this week?
Christopher Penn – 02:11
We will start with our friend, the 5P framework, because this is the best tool for deconstructing a use case. Okay. Did you want to quickly review the five Ps? Absolutely.
Katie Robbert – 02:26
So the 5P framework is: purpose, people, process, platform, and performance. Purpose always anchors; it always starts. What is the question you’re trying to answer? What is the problem you’re trying to solve? People: who’s involved both internally and externally? Externally being your customers, because every action you take should, in fact, do something for your customers. Process: how are you doing the thing? Platform: what tools are you using? And performance: how do you measure success? The 5P framework is meant to help you organize your thoughts, your questions, your requirements, and give you the opportunity to make sure you’re touching on all of the important pieces and not skipping over your process, for example, or the people involved. For example, purpose and performance are always the bookends. Ideally, it’s people, process, platform. Realistically, it’s platform, process, people.
Christopher Penn – 03:21
And if you’d like a copy of this, you can get it at TrustInsights.ai 5P framework. It is free. There’s no forms to fill out, nothing to get, no information to give. A lot of the time, to what you were saying, Katie, people are just said, “Hey, here’s a use case for AI. You can use it to do this.” And it’s kind of like watching a cooking show where the person’s like, “And here’s the thing you pulled out of the oven, and isn’t it amazing? It’s beautiful.” And in fact, I’m not familiar with it in-depth, but isn’t there a show called Nailed It! where people attempt to make what they see on Pinterest and it just goes horribly wrong? I feel like it’s on Netflix.
Katie Robbert – 04:01
That does sound familiar. John, you’re shaking your head. Are you familiar with this?
John Wall – 04:04
Yeah, I’m trying to remember if it’s Nailed It! or there’s another. But they show a perfect Kermit the Frog cake, and then you’ll see a weird green pile of stuff, and they’ll be like, “Nailed it.”
Katie Robbert – 04:18
I mean, kudos to them for at least giving it a shot. But back to AI. So, actually, a couple of weeks ago, my good friend Sunny Hunt was at an event and she was giving me some voice of customer feedback. The specific feedback that she had heard, anecdote was, our company just invested in Copilot, and the executive team wants us to start using it and demonstrating the ROI. That whole statement and feedback is really vague because what ROI? Of what? Efficiencies of what? And also, for people who aren’t familiar, what is Copilot? What does it do? Am I supposed to write all my emails with it? Am I supposed to write all my documents with it? There was so little instruction.
Katie Robbert – 05:09
But that’s the challenge: these tools are just that — they’re just tools. They don’t come with a user manual. You, the user, have to figure it out.
Christopher Penn – 05:18
Exactly. So go ahead, John.
John Wall – 05:20
I was just…That’s totally the pointy-haired bosses coming in, just like, “Get me some of that AI. We need some AI this quarter.”
Christopher Penn – 05:28
Exactly. So let’s pick a use case that we can take apart and turn it into something. Because one of the things that I love about use cases, when they’re done well, is they are the seed from which other things can grow, such as a requirements document or a product document or a work plan or a standard operating procedure. If you’ve got the use case that’s well-documented, then you can turn it into other things. And 40-year-old me from 10 years ago would be appalled at the words coming out of my mouth right now. But it turns out that generative AI, because language models need language to work well, works really well with things like requirements documents, whereas being vague or confusing does not do well with them. Even the smartest models are like, “I don’t know what you’re talking about.”
Christopher Penn – 06:19
And so if we do these things well, it dramatically cuts back on the time. I’ll give you an example. I’m working on a project for a client, and it took me 90 minutes to write out the requirements for the plan. Then I gave that to generative AI, which is a conversation going back and forth, saying, “We want to do this. We want you to do this. What about this? What did I forget?” and so on and so forth. I ended up with a really long requirements document. I then said, “Turn it into a work plan, step by step, a numbered work plan.” Then I put it into the generative AI tool and said, “Okay, now execute the writing of the code for step one, step two, step three.” And in less than 15 minutes, I had working code.
Christopher Penn – 07:01
First try, no errors, because I went through the hard work of the requirements planning up front. So let’s pick a use case.
John Wall – 07:18
Sorry from Katie on that.
Katie Robbert – 07:20
Apologies again, because this is live, and things happen on the live show. My dog has finally decided that I exist again. My mic was muted because she’s yelling at me because that’s how things work. It’s so funny because I remember when I was a project manager and the fights that I used to have. It’s the uphill battle and the resistance of getting the requirements documented took so much time, and it was so difficult. Excuse me.
John Wall – 07:57
That’s okay.
Katie Robbert – 07:58
It was so difficult. Sorry, there’s birds outside the window. It was so difficult to get the team to recognize the importance of having fully baked requirements. Every single time, without fail, it bit them in the…because they would develop this thing, then they would hand it off to QA, and QA would say, “What am I supposed to test?” Then they would give it to the stakeholder, and the stakeholder would be like, “This isn’t what I asked for.” I, as the project manager, was the one who was in the middle of it, in the line of fire, saying, “I really tried to get these requirements done.” And they’re like, “I don’t want your excuses, I just need you to get it done.”
Katie Robbert – 08:39
It was such a frustrating, soul-killing process of trying to get anything written down. To be fair, it was not just the developers. The stakeholders were really bad at giving me their requirements. “Oh, they can just figure it out. Oh, it can just happen.” It’s just all around… I’ve never been someplace where requirements are something that people are like, “Yeah, we should really do our requirements. That’s going to save us a bunch of time.” So I’m really excited to hear about your experience, Chris, because when I started working with you, as we noted this morning a decade ago, that was definitely not on your roadmap or radar. You were like, “I’m not writing things down. I’m just doing…forget documentation, I’m just moving forward. I’m just doing things.”
Katie Robbert – 09:27
And so this is a whole new Christopher S. Penn.
Christopher Penn – 09:31
It is. So let’s tackle a use case, a very common use case, which is vendor selection. Let’s say we’re a B2B marketing firm. We’re always looking to figure out who we should be using, what we should be using for CRM and marketing automation and things like that. I think if we could show how generative AI could be used to accelerate that process — that arduous, painful process — we might learn a thing or two, and we might have some tips for how we market ourselves. So we’re going to start…I’m going to bring up here my AI tool of choice. Now, I’m using Google AI Studio and Google Gemini. You can use ChatGPT, you can use Anthropic Claude, you can use whatever you want; it does not matter, as long as it’s a modern model.
Christopher Penn – 10:17
So we’re going to start with a skeleton prompt, and the skeleton prompt I’m going to put in goes like this: “You are a world-class management consultant (style of McKinsey, Bain, and BCG). We’re going to create use case documentation for a set of provided information. The use case documentation is going to follow the Trust Insights 5P framework (explain the 5Ps and the format).” So we’re going to start with that skeleton. Now I’m going to say, “The use case is using generative AI tools, in particular Perplexity AI deep research, to select a vendor for a B2B company’s marketing automation and CRM. Start asking me questions.” This is taking that use case, which is pretty clear in terms of what we want to do and say, “Okay, let’s dig into this more.”
Christopher Penn – 11:25
“So what are the primary pain points or challenges? What are the overarching business goals? Why is Perplexity being used as this choice?” So we’re going to start by saying…well, in this case, we’re going to… I’m going to put in a knowledge block and put in a couple of knowledge blocks because we want the tool to understand us overall. “Here’s some background information about us, our ideal customer and how we currently market.” Let’s go ahead and put in our company profile. We’re going to put in our current marketing and put in our ideal customer profile. Now, go ahead, Katie.
Katie Robbert – 12:15
I was going to say what I really like about this — and this again was sort of my pain point as a project manager — one of my jobs was to ask questions. Unfortunately, nobody liked to see me coming, ever. What you’re doing here is the system is asking the questions on behalf of the project manager, for lack of a better term. You’re not getting frustrated by it, like, “Oh, let me give the machine more information to make better requirements.” It’s really just fascinating to me that this kind of process can help people like me — the business analysts, the project managers, the operational people — get the information without being in the line of fire. To be completely honest, a lot of times our roles are seen as naggy and annoying because we’re asking a million questions.
Katie Robbert – 13:15
But if we can automate it and give it to someone — to answer the machine — that takes us out of it, and we can still just get our jobs done. I love this. I love seeing that this is the way that it could work, and I’m not personally offended. Answer me, answer the machine, I don’t care. I just need the answers to the questions.
Christopher Penn – 13:34
John, I’m going to ask you in terms of CRM, what are the things that you care about? What are the must-haves that you want as our business developer?
John Wall – 13:47
Classic ones on the CRM front are: deal management, lead management. Integration with email is nice to have if that works well. Integration with the website to be able to track leads and score leads. Those are the huge ones.
Christopher Penn – 14:06
What things piss you off about our current implementation?
John Wall – 14:10
Oh, that’s a good one. Full reporting suite — we have a bunch of handcuffs turned on with that. Robust API with unlimited calls. What else pisses me off? I do have to say HubSpot is actually pretty low piss-off threshold. They’re not doing too bad. But let me think if there’s anything…
Katie Robbert – 14:35
…else, connection between the databases. So, contacts and companies are separate.
John Wall – 14:42
Yeah, that’s…I’m okay. HubSpot does that pretty well because it has…it can…Well, and so there, that’s something that should be on the hierarchy side. It should have one-to-many for table connections, as far as contacts, companies, organizations, and deals. Because if that stuff is one-to-one, that’s a horrible mess. I don’t know what else…anything else?
Christopher Penn – 15:08
I would say I agree with the things about the APIs. I would say that we care about: robust, clear reporting; proactive notifications; integration with platforms like Google Analytics, Google Looker Studio, BigQuery, dedicated Python libraries. I think those are all things that you would want in a good CRM. Our timeline is this year, and it is currently February 2025. Budget: what do you want to pay, Katie?
Katie Robbert – 15:47
Nothing.
John Wall – 15:48
I know. Well, it’d be better if they paid us. That would be…
Katie Robbert – 15:51
No. So when I think about what we spend on this kind of service now, I don’t want to have to spend more than I’m spending. So I would say less than $300 a month for — call it — five users. Right now, we have four users. I would like the option of adding a fifth.
Christopher Penn – 16:11
Okay, so $60 a month per user.
Katie Robbert – 16:14
Yes. Because right now, that’s for us what we have budgeted. So I can at least absorb that kind of cost.
Christopher Penn – 16:27
Okay, so CEO, business development staff, data science team. Okay, what other questions do you need answered to build out this use case as we discussed? So let’s see what it comes up with. Now we’ve given it a bunch of new answers. Okay, further clarification: initial vendor list, feature prioritization, vendor evaluation, security, compliance evaluation process. For the purposes of the live stream, I’m just going to say, let’s just go ahead and build it. But you would want to spend this time doing this part. But in the interest of time, let’s go ahead and build the 5P framework use case following the template using the information I’ve provided. So it’s going to go through and take a look at that template, and it is a possibility it may completely screw this up. So, if it does…No, actually, it’s looking pretty good.
Christopher Penn – 17:40
So let’s take this and examine it, make it a little bit bigger here. One, two: our use case for generative AI is using Perplexity’s deep research to efficiently and effectively identify and select a marketing automation CRM. Performance: feature-aligned, budget adherence, timely compliance, stakeholder agreement. People: the stakeholders, the audiences, the Trust Insights team. The platform — this is the platforms we’re going to use for this use case — build the detailed requirements, use Perplexity, and then the directions. So from here, I’d say, “Great. Using all the information I’ve provided, build a very detailed, rich, robust prompt for Perplexity Deep Research. I will provide the Trust Insights ICP about us and marketing activities as a single document. I need you to write the rest of the prompt.” So we’re going to direct it based on this.
Christopher Penn – 19:03
Now that we have the use case written out like a recipe, we can start building out a plan. Now, if I was going to build this as software, I might say, “Now build a PRD, a Product Requirements Document.” If I was building this out as a service of some kind, I might say, “Build this out as a Business Requirements Document.” So here is the prompt for Perplexity. Let’s go over to our friends at Perplexity, and I’m going to choose Deep Research, which is their research utility. I’m going to put in that prompt, and then let me, in a separate window, copy in the ICP and the two knowledge blocks so that Perplexity has it.
Katie Robbert – 19:46
So while you’re doing that, the other scenario is…rather than going straight to Perplexity, what you built in the previous software in Google Studio gives you an opportunity to take those five Ps and have a conversation. So bring it back to your stakeholder, bring it back to your team. “This is everything I’ve collected from you. This is how we’ve put it together. Is this correct? What are we missing?” It takes you out of the conversation in some ways because it’s all of their information summarized into the five Ps, and it’s like, “Did we get all the right people? Is this the process that we understand it to be, that we want it to be? Are these the platforms that we would want to use? Is this the right measure of success?”
Katie Robbert – 20:41
What we know about humans is it’s easier to react to something than it is to give net new information. So if you put this together, let’s say I brought it to John, I’m like, “John, we’re trying to select a new CRM. Is all of this correct?” He could then read it through and be like, “Actually, it’s missing these five things,” or, “This is great. I hadn’t thought of the following piece of information.” But it gives us the opportunity to get on the same page about the use case. That’s a big part of the process of selecting use cases for generative AI — making sure everybody’s in agreement of what it is we’re doing. Why is research spelled incorrectly?
Christopher Penn – 21:25
Because I keyed that wrong.
Katie Robbert – 21:27
Oh, okay. It wasn’t like AI is going off the rails.
Christopher Penn – 21:31
No, no. I’m showing the output from Gemini so we can look at what the use case looks like. Ideally, this use case would be something that you would catalog and store somewhere in your company’s knowledge management system. Say, “Okay, our use case for generative AI for researching vendors looks like this.” Then you can say, “Here’s the ingredients, here’s the tools, here’s the directions.” Just from the directions alone, you have the ability to turn it into…
Katie Robbert – 22:03
…a standard operating procedure, which is also fantastic. That’s when we talk about business requirements and documentation. That’s a big part of it. How do you do something the same time, more than once? A lot of it comes down to documentation. So I’ll get down off of that ivory tower for now. I could probably rant about documentation forever.
Christopher Penn – 22:27
So we put that prompt in along with that huge knowledge block, and now Perplexity is going through. It has so far identified 47 sources. It is now going through and doing the deep research, assembling, reading each source, assembling its ideas about who it should be doing. Then it’s going to come out with the list of sources and then the summary of key findings based on Trust Insights’ operational requirements, technical needs, and budget constraints. Three platforms emerge as top candidates: Zoho CRM Plus, HubSpot CRM Suite, and Freshworks CRM. Here’s the platform methodology: the recommended platforms — Zoho CRM Plus — meets 100% of must-have requirements; it’s very strong. HubSpot meets 90%; it has limited relational depth. Freshsales meets 85%, and it is continuing to spit out all of its…
Christopher Penn – 23:23
…So here’s the features that we asked about in our requirements in our use case, and it scores each CRM on them. It shows you implementations for the API. This is a very thorough…this is the Deep Research product…a very thorough analysis of which CRM we should buy.
Katie Robbert – 23:51
What do you think, John?
John Wall – 23:53
I’m psyched that HubSpot comes in strong. I mean, I hate to be a jerk, but the common denominator among Zoho customers is that they don’t have any budget. That’s really why it makes the list. So it’s good to see HubSpot in there. Then, the Freshworks one…I haven’t seen that one before. I’m also surprised that it didn’t throw Salesforce in, but I think that is the right choice because the price point is so huge now. They’ve blown themselves out of the market, and it really is overkill for what we’re doing here. So this is definitely right on the mark. This would be the short list that I would select if I had to do that in my head right now. As expected, it’s come up with a third vendor.
John Wall – 24:33
That’s a possibility — that is somebody that I’ve never heard of.
Katie Robbert – 24:37
So Chris, can you scroll back to the scoring for a second? What I like about this is we can see very quickly at a glance — and this is something that can be copied and pulled out to be used in communication — but if we said, for example, “Zoho is not the right tool,” we already know that for whatever reason, we can then prioritize what’s the most important. Perhaps that’s something we should have done during requirements gathering. We’ve listed out a bunch of things, but if we had to rank them in order of the most important to least important, this would help us make the decision faster. So if I’m looking, it looks like budget compliance, 10 out of 10, for HubSpot, versus reporting. That’s higher in Zoho, lower in HubSpot.
Katie Robbert – 25:30
But is that as important as the budget or the API, for example? Those are the kinds of things that, as you start to get closer to a decision, are going to be the deciding factors.
Christopher Penn – 25:43
Exactly. The advantage of doing this use case development, deconstructing the use case, turning it into a full use case document is also for tools like Perplexity. If you’re not on the paid plan, you have a limited number — five per day — of Deep Research queries that you can run. So, if you’re going back and forth, asking questions inside of Perplexity, you’re going to burn through those credits really fast. If you take the time in a tool like Gemini, or any reasoning model, to think through and ask questions and build the use case thoroughly, and then have it build the prompt, you will conserve those very precious few credits you get per day and get it right the first time out of the box.
Katie Robbert – 26:25
That’s a really solid point. We’ve talked in previous episodes about the compute time and what that means. So if you want more information on that, you can go to Trust Insights AI YouTube and find our So What? playlist and find all of the other past episodes where we talk about the systems themselves, what those tokens and compute costs, and all that good stuff, actually means.
Christopher Penn – 26:51
This is an example of the deconstruction of a use case to say, “I’ve got this thing, use generative AI to do vendor selection.” That is the use case. This is what it looks like deconstructed, when you take it apart and you turn it into a recipe that you can then turn into a work plan that you can then take action on. And to what you were saying, Katie, earlier, about someone like Sunny saying, hearing people say, “Where do I start?” That’s where you start. You go on LinkedIn; you see somebody showing up; “Check out these awesome generative AI use cases.” “Okay, I’m going to take that one. I’m going to put it into this prompt framework. I’m going to say, ‘Help me deconstruct this. Help me work out the five Ps. Would I even use this use case or not?'”
Christopher Penn – 27:35
For example, today, LM Studio released a new version that has speculative decoding, and it’s amazing; it’s awesome. And everyone who is not a techie is like, “So what is the use case for that?” The use case is it speeds things up remarkably when you’re doing local AI. That still doesn’t tell you how do I get started with this. What do I do with it? It sounds cool, what do we do with it?
Katie Robbert – 28:04
A lot of users default to…so if I opened up ChatGPT for the first time, you can ask the question, “What can you do?” It’ll give you a long list of things that it can do, but that doesn’t necessarily resonate with you as an individual and your job. Chris, to follow this line of reasoning, using the five Ps, you can at least structure your question and the information that you have into — purpose: “I need to figure out how to use generative AI in my day-to-day job. My day-to-day job is X.” That’s your knowledge block, people. “I’m the person. Here are my direct reports. Here are my customers. Process: here are the things we do. Platform: here are the tools that we use. And performance…”
Katie Robbert – 28:50
“My measure of success is ROI on our work product, or more efficiency with reporting and gathering.” All of your information that way, and then giving that to generative AI…it’s very meta, but you can do that. It would say, “Oh, here’s where you could potentially use a tool like me to help you meet your goals.”
Christopher Penn – 29:15
Exactly. Let’s run through one more example. This will be a much less technical example, but we’re going to use the exact same use case decomposition framework prompt. By the way, we’ll put a copy of this prompt in Analytics for Marketers. So, if you’re not a member of the Analytics for Marketers Slack group, go to TrustInsights.ai Analytics for Marketers, and you’ll be able to get it after the show. Today’s use case is evaluating an NDA for bilateral fairness for both parties. “I need a generative AI tool to read the NDA and tell me whether or not I should sign it. Ask me questions about this use case.” So let’s see what it comes up with for this relatively straightforward use case. And yet, it’s a super valuable one. “So what is the context of this NDA? Is it a potential business partnership?”
Christopher Penn – 30:16
“What are your concerns?” So the context is an NDA with a consulting firm to hire them to do work for us. Our major concerns are unfairness — things that protect them, but not us. Our current approach is to just sign it and hope.
Katie Robbert – 30:45
This is fiction.
Christopher Penn – 30:46
Exactly, yes. “Give me some examples of unfairness.” Unfairness would be requiring strict confidentiality on our part, but no confidentiality requirement on their part, or denying us the ability to change the jurisdiction of arbitration. The desired outcome is a scorecard, clause by clause, of the NDA and its fairness to both parties. A 10 is fair to both. A one is deeply unfair to one side. It should also specify who’s getting hosed. The NDA is a PDF; we need to evaluate it right now. Okay, so we have ourselves a lovely use case document. Now we have the purpose: evaluate an NDA from consulting firm; move beyond “sign and hope.” Success is measured by the generation of a clause-by-clause scorecard from a generative AI tool.
Christopher Penn – 32:22
The people: you, the stakeholder, the audience. The platform: the generative AI tool. The process: one, PDF access to a tool; threshold for unfairness tools; directions. “Great. From this use case, build me a thorough prompt for a generative AI LLM to evaluate the NDA.” In this case, we are, as before, essentially generating code — it’s just code in plain language. I’m going to…let’s see, do I have an NDA that I can use? That’s always the question. Let’s see, in PDFs, we have…I have a bunch of NDAs; I can’t use any of them on the air. Wait, I can use ours. I’ll use ours because that one is safe to use. Yes, here we go. Let’s put this into…
Christopher Penn – 33:34
Let’s copy our prompt, start a new prompt, and take our lovely NDA, export it as a PDF, and now we can drop it in and see whether our NDA is fair to us or not.
Katie Robbert – 34:09
I don’t know why I’m nervous about this exercise.
Christopher Penn – 34:14
Let’s see: clause…that clause: preamble, N/A; project-defined fairness: confidential information is a nine; exclude information is a 10; general obligations is 10; development similar because a nine fairness. This is looking for non-solicitations of seven. It’s a two-year non-solicitation period; it’s 10, in general. Our NDA…this is a six; governing law…oh yeah, that’s the arbitration, in general. Ours is a very nicely balanced NDA. It doesn’t unfairly disadvantage one party or the other.
John Wall – 34:56
Well, you can’t lose because it’s either fair, or it’s optimized to be the best possible agreement for us.
Katie Robbert – 35:04
So, John is the one who does the majority of reading of the legal contracts, MSAs, NDAs, partner agreements — that kind of stuff. Is this a process that you would use for doing some vetting of that documentation? Do you think that would help you and save you some time?
John Wall – 35:20
Yeah, absolutely. Everything, the thing would be the testing of it. Run through it three or four times, and if, after three or four times, it hasn’t done anything really fruity — or even… it’s even enough to know that it will take care of 80% of these things. I just need to look for one or two things to make sure there’s nothing really bizarre in there — indemnification gets really weird if that’s in there. But a lot of times that’s not part of an NDA. I am all for all of this because there are better things to do than combing through a legal document that…I mean, we have to be honest…
John Wall – 35:55
…There’s a 90%-plus chance this is just going to sit in some file folder forever and never get looked at. It needs to get done to close a deal, but it’s not the best use of time.
Katie Robbert – 36:05
To your point about testing, when you’re looking at an NDA, for example, they are written — all of these legal documents are written — in such a way that really doesn’t make sense to a casual person, someone who isn’t as familiar with all the legalese. I have to look up “indemnification” every single time, regardless of the fact that I read through all of the contracts. I’ve helped put together the contracts, but it’s one of those terms that just does not stick in my head. The amount of time I spend relearning what “indemnification” is, versus “is it fair or not…”
Katie Robbert – 36:47
…Using a system like this, I could be saving so much more time than trying to reorient myself every time to a contract.
Christopher Penn – 36:56
Exactly. I had to turn on the warning banner.
John Wall – 37:01
Those are hours of your life you never get back. Nobody is on their deathbed like, “Oh, those NDAs I did were so great. I’m so glad I spent my life doing that.”
Christopher Penn – 37:11
Well, I imagine if you were a lawyer getting paid $450 an hour, you’d be like, “Yes, give me more NDA.”
John Wall – 37:17
There are people…yes, there absolutely are. They are not with us.
Katie Robbert – 37:20
We’re talking about people who are like us — who are not lawyers, who are perhaps in small businesses or teams that are trying to get contracts passed through without needing to bring in their whole legal team. Can we at least get a baseline of, “Is this a good contract or not?” That’s really what we’re talking about. I think even in the feedback, Chris, from Generative AI, I saw there was a callout, like, “Involve your legal team.” It was like, “Oh, okay, yeah, I should probably do that if I’m talking about legal documentation for my company.”
Christopher Penn – 37:58
Exactly. To recap for deconstructing AI use cases: first, you have to just know what the use case is. Can you articulate it? “I want to use generative AI to do this.” Even though we always say to people, “Don’t lead with the technology,” we acknowledge people are going to lead with the technology. So step one is to know what the use case is. Step two is to feed it into the 5P framework and say, “We’re going to build a use case deconstructed: purpose, people, process, platform, performance.” Then answer all of the questions that generative AI is going to ask us about, like, “Who else is involved? What else are you doing? What tools are you currently using?” and so on and so forth.
Christopher Penn – 38:43
Once it spits out the fully documented use case, we can do things like generate a prompt from this use case, which — I don’t know if you noticed — the prompts that this process generates are like a page and a half long. They are not, “Evaluate this NDA for fairness.” We saw just the output from the use case because it considered all the things that went into it, and we answered all those questions, and came up with a robust prompt that will give you much better results. Then you can build a work plan, you can build a requirements document, and have generative AI do the thing. But if you follow this process, you will get really great results out of generative AI.
Katie Robbert – 39:27
I really like pairing the 5P framework with deconstructing the use cases and having the system ask you the questions — “These are the questions I need to answer; now ask me about them.” — it’s a really helpful way to put all the information together. I’m a really big fan of this.
Christopher Penn – 39:46
Yep. So when the next stakeholder comes to you and says, “Hey, we need some ROI on this AI stuff,” you can say, “Great, let’s build out the use case, let’s build out the performance. Is ROI a performance measure?” If so, in the process of using the 5P framework, you would say one of our stakeholder requirements is some way to demonstrate ROI. So we need to incorporate that in here. Now, in the case of something like the NDA evaluator, the ROI is pretty simple: you’ll probably cut your legal time in half because you have very specific questions for your legal counsel as opposed to just, “Read through this whole thing.” John no longer has to spend four hours reading a 22-page NDA just to decide whether or not we should sign it.
Christopher Penn – 40:35
In the example of using Deep Research to evaluate a shortlist for vendors, the ROI there is not spending three days on that; instead, having the shortlist built for you — if you did the five Ps, if you did the use case, and you had it generate the prompt so that you got the best results the first time out.
Katie Robbert – 40:58
John, final thoughts?
John Wall – 41:00
We are not lawyers, we are not investors, we are not regulated software reviewers, but this was all useful stuff.
Christopher Penn – 41:10
Again, if you want a copy of the use case prompt that we used to generate, you will find that in the Analytics for Marketers Slack group. We look forward to seeing you there! It’s free to join, no obligations, and you get to chat with 4,000 other marketers who ask and answer questions every single day. That’s going to do it for this week’s show. We will talk to 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/ti-podcast and our 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/analytics-for-marketers. 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.
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