So What? Identifying Generative AI Use Cases using a 2×2 Matrix

Well, hey everyone, happy Thursday. Welcome to So What, the Marketing Analytics and Insights Live Show. I'm Katie, joined today by only Chris. John is at Inbound. He is.

He is representing Trust Insights and Marketing Over Coffee at Inbound in downtown Boston this week. And so we're very excited to hear back from him everything that's going on, uh, what's new with HubSpot, and then just general catching up with people. So if you are in the area, go say hi to John. He's wandering around the Boston Convention Center today. Uh on this week's episode, we are covering identifying generative AI use cases using a two by two matrix.

We'll get to the two by two matrix in just a moment. But, you know, we've covered AI use cases before. We've talked about them, you know, we'll cover again what they are. But we wanted to take it up a notch by really helping you think about, okay, that's great. Now I know the use case, but what does that mean for my company?

And so really finding a thoughtful way to assess whether or not the use case was going to be something you should prioritize. So Chris, where would you like to start today? Uh, well, that's a good question. I mean, one of the things I was delivering a lecture yesterday at Wheaton College, and one of the students asked the very salient question, you know, what jobs is generative AI going to affect? And I said, only the ones that use language, which by the way, is all of them.

Um, there is, I can't think of a single career, even someone, you know, like shoveling stuff, you know, outside of the road, you still use language to do to, you know, reports, to do your time sheets and stuff. You are still communicating because communication is such an integral part of who we are as human beings. And as a result, every job will have be impacted in some way by large language models and generative AI. So the challenge is for executives, for decision makers, for leaders, to figure out A, what are the use cases, or the use case categories, even, and then look at your organization and say, how do I even map this up? Because again, it's gonna affect everything.

Your finance team, your HR team, your recruiting, your sales, your marketing, your operations. If it if the humans are involved, it's gonna be impacted. I think you hit on something that I think is a really important distinction. There is use case categories, and then there's the use cases within those categories. The use cases themselves are numerous, but they all have to eventually fall under one of the categories of generative AI.

So, Chris, what are those six categories for generative AI where as we start to think about the use cases? The categories are generation, aka making stuff, right? Blog posts, emails, limericks, you name it. Extraction, where you have a pile of data in some text format that you need to get data out of. So if you've ever written or you've ever read a white paper and there's tabular data, like buried in the PDF, like 22 pages down, you're like, ugh, I don't want to copy and paste that out by hand.

A language model can go and fish that table out and make it machine readable for you. Summarization. So if you've watched any of our uh our podcast episodes or uh or previous live streams, you know that there are transcripts. Maybe even that's too much to consume. So you can take the transcript, put it into a language model and say, just give me the high points.

Like I give me the Cliffs Notes version of it. There's rewriting where you can take text in any form and convert it to any other form. And this can be silly things like, hey, rewrite the Hobbit, but in emoji, right? Um it can also be uh explaining concepts, re-explaining a concept. One of my favorites is, you know, explain this in terms of pizza.

And you'd be surprised at the number of very complicated things like quantum superposition that can be explained with pizza. Uh pizza's sort of the universal explainer. Uh the fifth category is classification, where you can take bodies and chunks of text and classify them, categorize them. And the last was question answering, where you can take either the model itself or specific pieces of text and ask questions of it. So again, one of my favorite things to do with this.

I was looking at an NDA the other day, and I'm like, oh my God, this NDA is like 27 pages long. I loaded it to a language model and said, okay, tell me how fair this NDA is. Is it bilateral? Is it equal, or is it skewed towards one party or the other? Should I sign it?

And those that's an example of question answering. And again, across departments, if you think about it, if you have, if you're in accounting and you have an accounts receivable ledger, you might to a private server, you might load that ledger and say, I want now I want to ask questions of this account of this ledger and say, Well, you know, what are the categories of expenses that are that are look weird? So those categories help us understand how these tools work because they cover so much of just the ways we use language within within business. And you know, I would imagine that some of the viewers might be wondering, well, why do I need to care about the categories if I just have a specific UK use case? Well, if you want to catch up on older episodes, we actually think it was last week we might have done this.

We actually did a head-to-head to head, no, it was a few weeks ago, uh a few tests of the different tools in terms of the different categories of uses. So you can catch that uh live stream episode at trustinsights.ai slash YouTube, because the generative AI tools that are on the market as of today have different strengths and weaknesses. And so if your use case falls under generation versus extraction, there you're gonna want to consider different tools. And that's why understanding which category the use case falls under first is gonna help you decide which tool uh you're gonna need to use. So, Chris, how do we get from use case categories into decision making?

How do we bring it to a two by two matrix? What is a two by two matrix? It's a consultant's best friend. No, a two by two matrix is you have two dimensions of some kind that you want to simplify things. So cost versus quality, speed versus uh features.

Um, you know, pepperoni versus uh pineapple versus pepperoni and you know on pizza. Um whatever the case is, you you have two sometimes they're mostly two dimensions that you want to try and categorize different things on. And the those two dimensions ideally are scalar, like it's not just an either or, but it's you know, there can be variations. So again, if you're talking about things like cost versus quality, it's not just expensive or not expensive. It's like, well, this is ten dollars and the quality is here, and this is a hundred dollars and the qualities over here.

And so is paying the extra hundred dollars worth it, you know, it depends on how much more quality you get for that. And so that's that's a a two by two matrix in its essence. And there are so, so so many of these. Again, this is a consultant's best friend because even though it runs the risk of oversimplifying sometimes, it can take a very difficult um or complex environment and just narrow people's focus just to get them to pay attention to a couple things that really matter. Well, we've done this exercise.

We've actually used the two by two method to help categorize uh keywords for SEO. So difficulty versus um, now I'm forgetting I'm blanking on the other volume. And so you would want to go after, for example, uh high volume, low difficulty, or if you are feeling really, you know, ambitious, you could do high difficulty, low volume and try to create your own space. And so it's a way to categorize a lot of information so that you can prioritize it. And so today what we're going to do is we're going to look at the some of the use cases for generative AI in terms of optimization versus innovation.

And so these are things that we're looking internally. We're looking at the things that we do in a business, not necessarily the things that we're providing to our customers, but that will come into play in the conversation. We're focusing on will this use case optimize what we do, which is to make it faster, to make it more efficient, to make the processes even more automated and repeatable versus innovation, which tends to be a new spin on something we're already doing, something net new, something we haven't done before, something we haven't had the ability to do before. And so we want to start to break down the use cases into that two by two matrix. So Chris, if I said to you, hey, I have a bunch of things that I want to do with generative AI, I'm guessing that you would say, great, let's put it on a two by two and figure out where it all nets out.

I would exactly do that. And I would I would say, you know, maybe the easiest way to do that would be with something like, you know, even just putting on a on post-it notes on a wall can be one of those things. So let's bring up your standard two by two matrix and then start talking about the processes that you have. Is it internal? I mean it's not gonna be, you know, it's something that the company does, is it external, meaning something that the company does with customers or clients or or vendors or lobbyists or whoever inside or outside the company?

And then to your point, is it making something existing better, which is optimization, or is it bringing something new to the table? So a real simple example. Suppose you're an insurance company and you have elaborate insurance policies, and your your call center is constantly swamped with people saying, I don't understand what's going on in my policy. I don't know if my policy does this. So one of the things that you could do with the language model is have a chat bot, right?

And you would have that chat bot uh for policies. Now, this is not something that's existing, right? This is something that would be net new, and it's something that's customer facing. So you maybe put that right there in that external innovation category, because yeah, that's that's new, that's gonna be different. Um, but and it but it could be a pretty big deal.

Well, so let's think about some of the classic use cases that we've been hearing. Um writing blog posts, writing anything. I want to replace all of my writers with a chatbot, with chat GPT, with Llama. I think that is something that comes up a lot in conversation as people as companies are examining should we bring in generative AI? So writing content, I you know, I would argue that it's an internal optimization, unless your company doesn't currently write any content at all.

And then it sort of skirts the line of optimization and innovation. I would say um it also skirts the line between internal and external, because you don't just write content for yourself. I mean, probably not. Um, you you publish it on a blog for external use, for maybe it's SEO benefits, maybe it's just you know customer attention or or customer care. But either way, that content's got to be out there doing something.

You can't just sit on your hard drive. Right. Um, and so as we're putting the use cases here, would I guess we would have first decided which of these boxes we would want to focus on in terms of where things land. So, for example, um, external and innovation, I'm guessing because it's customer-facing and it's something new, is gonna have a higher cost associated versus internal and optimization because it's something that's pre-existing that we're just making a little bit better. Not necessarily but these are the kinds of decisions or these are the kinds of context uh data points that you would want to have, because I would imagine that this is a great exercise, but then if you don't know which box to focus on, then you just have things in boxes.

You do, and so uh the I would almost liken this phase of sort of your um AI strategies. This is almost like brainstorming. Like, what are what are all of our processes, right? And you just start slapping stuff on the wall and things, and then you could start saying, Okay, well, now let's start thinking which these things are and it's you go back to the old-fashioned Eisenhower matrix, right? Urgent, important, and easy.

How urgent is it? How important is it, and then how easy it is to do it. So a chat bot for policies, not super urgent, although your customer care center would probably disagree. Is it important for the customer? Yeah, to get right answers, and is easy, it's not real easy to do, but going back to those three factors, it would be important to reduce call volume, right?

So that you could save some money on your call center. And also, as long as it's giving the right answers to customers, it'll make customers happier. So with any of these tasks, um, and again, this is something that is not just marketing. This is a department by department, your HR department should be looking at all of its processes, right? Your sales department, your accounting department, your compliance department, your legal department, everybody should be looking at their internal extra processes and saying, Well, what do we do all day?

I can imagine a version of this, a two by two where it is, you know, urgency and cost are the X and the Y. And so you can start to say, is it urgent, but it's high cost? It's urgent, but it's low cost, and then you can start to get pick off those small wins versus it's not urgent and it's high cost, those things go on the back burner. But I think you would need to go through this kind of categorization exercise first to even know what the things are. Exactly.

So let's think back to your um your pharmac pharmaceutical days. Um when you were doing that kind of work, what was one of the tasks that maybe was just a pain in the butt? We had, and I don't so I don't know the full way to sort of like summarize this, but basically, we had over we had almost 10,000 data points every single month that had to be cleaned by an SPSS syntax that was printed out and then manually processed. And so I guess data cleaning is the best way to put it. Um, because it was high volumes of data, we were constantly running out of server space because of how manual the process was.

And so that to me would have been an internal optimization, which would have allowed for a higher volume of reporting as it was because the process was so manual. We could only offer, we like basically hit the wall in terms of resource time every single month. When you said the words SPSS, SPSS is a programming language, it's a statistical programming language to me. That immediately screams AI because it's a language, right? And a language model can generate language.

Um code Llama or GVT4 can write SPSS syntax really easily. And so yeah, to me, that would be if that's a big pain, meaning it's important. Um and you know, you're running out of disk space, so there's a there's emergency there. Um but that's easy. Well, and you could we couldn't scale it because we couldn't add on more reports.

Um, you know, it's I don't want to get too tangential, but I do recall our these big binders of printouts of the SPSS syntax and someone going through with a pencil checking it for errors every single month. Because every single month when we ran it, we would get hundreds of errors in the SPSS syntax when it would pause and then we had to figure out where it was and then update the master syntax. It was it's when I look back, it's bonkers. Totally bonkers. So yeah, data cleaning, uh writing code for syntax.

I guess it all falls under the same category. Um the others the other thing was the actual write-up of the reports themselves. The reports themselves were about 50 pages long of Word docs, and so it was analyzing the data and summarizing it. There's that word that word that tells us that's one of the six core use case categories. Um again, that goes squarely in the internal optimization.

Although the if the reports are going externally, it can go on that line there because it's gonna the customers are gonna see it too. Oh, yeah, they absolutely they were external reports, not internal. Um, there was one of the things that I was charged with was helping to identify early warning signals of uh substance abuse. So basically, I worked within uh opiates and stimulants, and so looking for conversations on chat in chat rooms that people were it was early indicators that the new opiate that just got released, even though it was meant to be non-abusable, is now being abused so that we could try to get ahead of it. So I guess it's internet scraping.

I don't know. No, that would that would be a that would be I I would you can call it web scraping, I would I would just call it um data acquisition. Yeah, again, I remember sitting there with printouts of papers of like chat conversations and hand coding them because we also did uh we built our own sentiment analysis tool, but a lot of it was manual. Mm-hmm. Yep.

What I find interesting is we've got a lot of stuff in three of the four boxes. That internal innovation doesn't have anything in it yet. Right. And so what is what's an example? So an example would be maybe you have maybe your team uh internally just needs something that doesn't exist.

So I'll show you a really quick example here. Uh I was working on uh someone to ask, hey, would it be possible to make a a mobile app? Can can chat GPT make a mobile app? So I said, here's the requirements. Tell me what the thing, you know.

So the best option is the framework like Flutter. Um, and here's all the the requirements for it. So I said, okay, cool. Flutter sounds like a good choice. Then it says, okay, well, great.

So see here's here's the process. Set up this thing, set up your Google Sheet, start the back end, and then it starts giving me instructions. Okay, here's how you set up the Flutter projects, go download it, install it, and stuff like that. Here's how you do the Google Sheets integration. This is the code that you use.

And it I was doing this for about 20-ish minutes the other night, and uh just just for fun, because as one does. Um in that time, with its help, I built a bare bones mobile app that can work on Android and iOS. Now, again, if we think back to our chart here, this app is not supposed to be for external use, right? Is it's an internal use for an organization. So something like that would be super powerful, right?

So let's put this here uh internal app development. Well, so let me understand though. The chat GPT or Llama tool didn't build the app, it gave you instructions to build the app. It gave me instructions. Oh, and it gave me some of the code.

Instructions, yeah. Yes. Okay, but I think that that I what I just want to make that distinction is that these tools will not build you an app, but it will give you the blueprint to build the app at this time. It depends on the language. So Flutter, no.

If I had specified Python, the answer would be yes, because there's a system called GPT Engineer where you give it extensive requirements and it will actually write, build, and compile the code. It is a little buggy. Um I can't imagine that there's a UI for it. Like the app and then, yeah. Oh, yeah, then the system itself runs at the command line, but the final app will have a UI.

You will specify what you want in the UI in the requirements gathering. And I think that this is another really good example as to why you would want to categorize the use cases under one of the six main categories, because not all of the generative AI tools are going to be able to do that. They're not all going to be proficient in Python or you know, CSS or whatever the other cobalt, all the other programming languages are. Some will handle it better than others. And if that becomes a real need for your company, you want to make sure you're choosing the right tool.

Exactly. So that's an example of an internal innovation, something that doesn't exist, but you could dramatically streamline the process. One of the things that has held companies back, and and we're in this pile of companies, is you have an idea, you're like, uh, it's gonna take six months to write the software and QA and deploy it and stuff like that, and we don't have that kind of time because we're all busy doing you know paying work. Um with these tools and their ability to generate code you can cut that time down by two thirds three quarters maybe even 10x maybe you know nine you know 90% less time you still have to spend time on it but you can spend less time on it and do both innovations and optimizations so we have for example uh we were talking about this a lot recently in Slack uh our content curation software so we built some software years ago to do content curation and it was always a little buggy actually it was more than a little buggy it was always a lot buggy and then about a month ago something crashed I remember going in and going uh I gotta fix this thing and then I'm like wait no I don't I'm gonna feed it to chat GPT um to the GPT4 model and I fed this I said here's the prompt here's here's my PHP code fix my code right and it's like hey here's the 18 things you did wrong in this code um great just fix them all and now it runs so much better um it runs faster it handles errors better because it has error handling now and it didn't used to um and so even that that's internal optimization uh we can put that on here and just sort of code maintenance um but that is an example of where even something that is so mundane is super super valuable and you know having run software development teams. The QA team, especially it so where I worked, we had about 20 software developers.

We had three separate product lines plus all the internal things for the company and the website. And we had two QA engineers, just two. And they were responsible for testing everything that came, you know, that got landed on their desk. And they were always backed up because you know, despite the product manager's best efforts to stagger the different product lines. It was myself and two others, and we always worked together to try to stagger so that we could share resources, things happen.

Things get backed up. Someone goes on vacation, code breaks. And so we were always behind schedule, especially when we came to QA, because our QA team was top notch. They were very thorough, which meant it took more time and you had to get in line. Exactly.

So the other thing I'm noticing here is that the external optimization book doesn't have anything really squarely in it yet, Katie. Giving that some thought about something that's customer facing that could use some optimization. What would what comes to mind for you? I was thinking about that, and it depends on your kind of business. So for us, I would think that an external optimization uh might be email deliveries, um, response times to you know, inquiries.

So, you know, for example, if we have a client who's high touch who sends a lot of you know, different email threads, then is there a way to use one of these tools to, you know, basically help with that custom customer response? Have some sort of acknowledgement. We got your email, the information goes into our task management system so that we can see it and get it faster. To me, that's an external optimization because the customer benefits it. There's some internal, but the customer benefits mostly from you know that relationship building.

It doesn't replace the human, the account manager, but it helps make sure that their information doesn't get lost. I would agree with that. I think it's a great use case. Another one, I mean, optimizations literally in its name SEO search engine optimization. There are so many ways to do SEO with these tools.

One of my favorites for things like content creation is take the persona of the person that you think is your ideal customer and have the tools compare that persona and what they're likely to be interested in with content you're creating and say, you know what? There's some words and phrases and and concepts and topics that are just not in your content that need to be if you want to appeal to CEO. I would agree with that. And so when we start to look at all of this, you know, we can see that we're very heavy on optimization and very light on innovation. And I think that that's one of the misunderstandings of innovation in general.

Uh, I wrote about this a few weeks ago for the newsletter. Uh, if you want to subscribe, it's trust insights.ai/slash newsletter. And the thing that is misunderstood about innovation is that innovation, a lot of people approach it of like, I'm creating something brand new that the world has never seen. And that's not necessarily true. Innovation is always built on the back of something that's pre-existing, it's finding a new solution to an existing problem.

So, you know, maybe, you know, electric cars that drive themselves is an innovation. But having to get from point A to point B is not a new problem. So an electric car that drives itself is a new solution to an existing problem. And that's the difference, and that's the distinction with innovation. So when we look at innovation versus optimization, yeah, an internal app development, that's a new innovation because it doesn't exist to us, but developing an app is not a new problem.

We're just finding a new solution to an existing problem. Uh, our customers needing more efficient responses to their policies, it's not a new problem. It's a new solution to an existing problem. For those of who are not word nerds, innovation literally comes from Latin from innovare, uh, which is uh broken out in pieces means to renew, to make something into something new. So it's not making a new thing, it's making something as old into something new.

And so, with that distinction, it does not surprise me that the majority of use cases would be optimization versus innovation, because what we are all collectively trying to do is squeeze more things into the same 24 hours. And so using systems like a chat GPT or a llama or stable diffusion or whatever, you know, helps us get there faster, more efficiently, and that's the optimization piece. You're muted, Chris. Sorry, if you think about it, if you think about those those six use case categories, those six use case categories really are about working with data you already have right so generation not necessarily the generation is hey write a blog post um and you still have to provide a lot of information about that in your prompt question answering is actually no question answering even that you have to provide some data you have to ask questions of something you can ask them a model generically but you're not going to get as good results as asking a specific knowledge base. The other tasks extraction summarization rewriting classification are inherently taking stuff you already have and doing something with it so by definition like you said that innovation is going to be coming from transforming something old into something new and so when we think about so we've been plotting these use cases on the two by two you know some some of these are going to fall in every you know the internal optimization bucket and so you could look you could look at this and go, okay, but that's everything falls into the same bucket now what do I do?

So the next step that you would take is you would take those pieces that fall into that quadrant and then start to you know dig deeper like okay so is data acquisition a higher priority than code maintenance or is you know data cleansing more costly than writing content and finding additional ways uh so you'd start as Chris is doing you'd start a second matrix and you would start to figure out the categories so we're gonna do cost um versus I would assume uh value importance, yeah. High importance, low importance, and that makes sense. So things that are low cost, high importance are what we would want to start with. Those are those tend to be the quick wins. So they're not gonna cost you a lot of money, but you're gonna get a lot out of it.

And so let's put all these take everything that isn't in the uh internal optimization. Well, it's let's take all these things off of the grid here. Now, so let's take some of the data cleansing, the the idea of of saving that time, it's important, right? Because it was costing you an enormous amount of time. And because SPSS, it's language-based, that goes right there.

High importance, very low cost, because that's an easy win. Right. Um code maintenance, also an easy win. Um, you know, it depends on where your code fits, like how important your code is, but like that stuff for our content curation software, again, an easy win. The machines can do that.

I would also imagine that it also depends on how big your code base is, and the same is the same as how big your database is that your data cleaning. So, you know, when I think back to that example, you know, we had you know tens of thousands of rows of data versus we're pulling this month's out of Google Analytics. Those are two very different data sets. And so we may have to start to break it down further of like the time invested. So it may feel like it's gonna be low cost, but if you're going back through months and years of code or data, you're gonna start to increase the cost.

Exactly. Um chatbot for policies. Um, I would say, you know, that that's a of medium importance, but the cost is pretty high because you have to, you know, bu do the embeddings, build a vector database, all that stuff. There's a lot of infrastructure costs that goes with that. So that's that's fairly high cost.

And I would say internal app development is low importance, high cost. Yep, I would agree with that. That's that that is our employees are not quitting because of that. Writing content is I'd say probably in the you know it's it depending on how you're doing it, it it can be s somewhere along here. Mm-hmm.

And I th yeah, I think it depends on what content you're writing. So if you're using the tool to write, you know, a 300-word blog post, you know, it's not gonna cost you a lot. But if you're using it to write an academic paper that's thoroughly researched and site cited, then you're gonna have higher costs associated. Exactly. Customer response, what example you were talking about, to do the way you were talking about require you to build um basically build an AI app with Langchain and start chaining models together because you're gonna need a couple of you know a few different models to accomplish a few different tasks.

So I would put that on the higher cost side. So we're but how important is that to you? Given that we are a service agency, I would say it's a higher importance because without customers, we don't exist. Right. Okay, so we'll put it there.

SEO right now, where would you put that? I would put that in the upper half of the low importance. And I would keep it I think it yeah, I think it's lower cost because it's a lot of topic generation, keyword research, things that historically, you know, aren't uh big cognitive overload. It's just a matter of finding the time to do it. Mm-hmm.

What about data acquisition? That scraping you were talking about. Um, I would say that that is I'm gonna guess that that's high importance, but also high cost. It it depends. I would put it here because it depends on the kind of data and stuff.

But yeah, it could be there. And then that report generation, just streamlining those reports. I would say that's high importance, low cost. I would agree. So what I find interesting is in the internal, external innovation versus optimization, you definitely have things weighted towards one half of the board.

When we put it in terms of um cost and importance, uh, we're actually pretty concentrated in the low cost, high importance quadrant. So A, that tells me we did a good job of identifying tasks that are actually meaningful, but B, that tells me there's a substantial opportunity for AI at our company. And when you start to look at the tasks themselves, you can see there's a lot of overlap of the kind of thing that we're needing to do. And so that's where I, you know, sitting in the CEO seat, I would say, huh, what's going on with our data team that these are all the things that we need to be focusing on first? You know, are they, you know, overwhelmed with the amount of things, or is this truly just an opportunity to bring in some automation?

You know, and and I'm not saying like you specifically, Chris. I see you sort of smiling. I don't I don't mean you specifically. I mean like if I were objectively looking at this as someone who isn't mean for not just insights, I would say, huh? I'm seeing all of our developer work, all of our analyst work, those are the things that I should be paying more attention to.

Do they have repeatable process? How much volume of things are they generating? Am I getting what I'm paying for? And is this a really good opportunity to bring in a lot more artificial intelligence? Exactly.

Katie, what would you say it is that you do? I I just bark orders and boss people around and get cranky. That is all I do. Uh but no, I agree. There's a lot of things here where and again, this is this is an exercise that every department in every company should do.

It's not just marketing, it's not just engineering, it's not just IT. Everybody, um, HR should be doing this because this is where you're going to see like this this sector here, the low-cost, high important sector, if you can knock out these five tasks here, your business is gonna grow, right? Your business is gonna grow because you'll be much more efficient at things that right now consume a lot of time. They consume a lot of resources, and they don't have to. You know, a AI may not be able to do it all, but AI will certainly help the workers who are in those departments do their jobs better and faster.

Well, especially if you sort of if you take like data cleansing, for example, there's a lot that could go into that. So if you start to pick apart the actual steps that go into the data cleansing, then maybe it's just, you know, having AI do the SPSS syntax versus, you know, formatting everything. Like it really depends on what the task itself is. So I think that it should be looked at not as, oh crap, now this task is going to be taken by AI, but how can AI enhance this task so that I can focus on internal app development so that I can focus on chatbot for policies because I'm no longer inundated with report generation. Exactly.

That's a great way of looking at it. If you can knock down this stuff that you know is consuming time and money and people, then other stuff that's on the board will get taken care of. Um it will actually get the attention it needs. I would agree with that. And so, Chris, what so it sounds like the first step for people who are interested in this kind of an exercise is to understand the six types of uh the six categories of generative AI uses.

And so that's number one. Number two is to then go through the exercise, you know, don't worry about getting it right or wrong, but just start to list out the different things that you think AI could be doing. Uh, it doesn't matter if it's a realistic thing right now, it could because that could go into innovation, or if it's something you're like, if I could just get this machine to write these reports for me, I would be 10% less cranky. I would say that's high importance. No, I think that's exactly it is to look at this processes and say the the question that we ask when we do consultations like this is what processes are consuming the most time that are repetitive?

Like every month I have to do this. Okay, that that anytime I hear the word repetitive, that's like an alarm bell saying you need to be looking at a technology platform coupled with people and processes to reduce the the burden that that repetitive process imposes on your organization. That's like the red flag that says, okay, the moment you say every month or every week or every hour, okay, that's something that that we should be digging into. If you have something that comes along once a year, like, okay, you know, maybe maybe not, depending on depends on the size of the task. But those monthly reports, those QBRs, those things that we everyone is saddled with.

Those daily roundups. Exactly. All that stuff, that is that is an immediate sign that there is some kind of process and platform that you can bring in there that will reduce the burden on the people. And if you aren't even sure where to start, then we can help. You can reach out to us at trustinsights.ai slash contact, where John Wall, who is not on this call uh today, our chief statistician slash all around swell guy, uh, will be the one to talk you through what's possible.

Um, and if you just want to sort of hang with people who have similar questions to you, then you can join our free Slack community, analytics for marketers at trustinsights.ai slash analytics for marketers, where you can ask and help answer other people's questions. I think, Chris, we have what 3500 people in there now about that. Yep, exactly. No, though I think those are those are good places to start. But when we've done this consulting for others, the other thing that doing this sort of work does is really highlights opportunities for collaboration.

And if you can use machines and technology to streamline that to accelerate the speed at which you collaborate, you will get more done and be much more productive. I mean, it's it it sounds so cliche, you know, uh, as as consultants are are wont to do, but it's true. Knowing that this department and that department and this department all have exactly the same task and slightly different disciplines means that all three of those uh functions could be able to learn from each other and should be able to share best practices internally. It's the reason that the phrase many hands make light work has stuck around for so long. That's true.

All right, Katie. It looks like um folks have their homework cut out for them. And again, uh, as mentioned, if this is something that you want to have done at your organization, we do this in half day and full-day workshops and stuff. Let us know. Um, any final parting words?

Start planning, start experimenting, start brainstorming. Um, you know, this is a great exercise to do ahead of selecting a tool because you don't want to pick the wrong tool and find out, oops, I just spent a lot of money on something I can't use. Exactly. And we are recording this uh in September. Uh this is the time of year when folks start thinking about annual planning and budgeting and forecasting and what the year ahead is going to look like.

So this is the this is a great time to be doing this kind of planning and analysis as well. So thanks for tuning in, folks, and we will see you next time. 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 slash TI podcast at our weekly email newsletter at trustinsights.ai slash newsletter.

Got questions about what you saw in today's episode? Join our free analytics for marketers Slack group at TrustInsights.ai slash analytics for marketers. See you next time.