So What? The AI Maturity Model

Well, hey, howdy. Happy Thursday, everyone. I'm Katie. This is So What, the Marketing Analytics at Insights Live Show. I'm joined by Chris and John above me.

I can hold them up today. Uh I never get that right, but you know, I'm gonna try every time anyway. Uh so on today's episode, we are talking about the AI maturity model. So for the past couple of weeks, we've been talking about uh we've been coming at AI from a couple of different ways. Last week we talked about will AI take my job.

And if you missed that episode, we were talking about how highly repetitive and low creative tasks are the ones that AI will take, and then the inverse is true. The low repetitive, high creative are the ones that are the safest. So you can definitely check that out on our YouTube channel. Uh this week we're talking about how to assess your team for AI readiness, uh, what steps you need to take to introduce AI, and when AI is not the right solution. So last week, as we were talking about AI take my job, it came up in the conversation of it may all be a moot point if your organization is not even ready to introduce artificial intelligence into the workflow.

So, Chris, where do you want to start today? Let's start with the AI maturity model. This is a model that gosh, we put together about four years ago now. And it's something that we use as a way to help people kind of navigate. Are you ready to be tackling this stuff?

Because a lot of the executive suite has what we would sometimes gently call airline magazine uh syndrome, where a high, high ranking executive reads something on a flight about AI, and then you know they storm into the next meeting and say, we need to put AI on a product sector, we make um but I need it. I need it now to stay competitive. Exactly. So um so it came up with this framework as a way of saying evaluating your organization from top to bottom, or your department, or even you as a professional, how ready are you? So it's a seven stage uh framework.

The bottom stage, which is the foundation on which you you build everything, is that data foundation, which is finding, cleaning, preparing, uh, and unifying your data sources. Do you know where your data is? What condition is it in? Right? If if data is the raw ingredient for AI, so if you don't know what you have and you don't know what quality is, you can't really go any further.

Well, and before we get into all of the other uh steps in the model, so this is the bottom, this is the foundation, and I would say, at least in my experience, the larger the organization, the more they're stuck at this first step because data is everywhere. People are siloed and spread out, and institutional knowledge comes and goes so quickly, there's a lot of turnovers. So the data foundation is the step that a lot of people try to skip, but you really can't. John, what do you see when you're talking with uh communities and prospects about sort of where they're where their heads are at with the data foundation? Do you think that they feel like they're ready?

No, I mean that's like the ongoing joke is you know, I talk about us doing predictive and attribution, those are kind of the two cornerstones. But then the reality is more than 95% of the people that we deal with don't have their data in order. It's a complete mess, and they need us to come in and fix, you know, the plumbing. Um, and it's you you hit a huge point because it's it becomes a problem the larger the organization gets because you even get to this point where the bureaucracy, you know, there's people that don't want to share the data, like they just don't even want the results to go to other departments or bosses. It's just easier for them to say, oh yeah, we can't do that, or that's impossible.

So yeah, it's a real cultural hurdle just to you know get that foundation laid and try and get to somewhere where you can start doing more with it. Mm-hmm. And you know, Chris, once we go through all these steps, I think it would be worth going back through and talking through the people piece of it, unless you want to talk about that as we're going through each step. Well, I think it makes sense. You know, uh we we always look at everything as the people involved, the processes you uh need to operate it, the technology platforms that you use to administer it, and the raw ingredients, right?

It's like we always say in a kitchen, there's a chef, there's appliances, there's ingredients, and there's recipes, and you need all four. Uh and you you have the overarching reason, which you talked about in the beginning, Katie, is why are you doing this, right? Each stage has to have a purpose as well. The next level up, you know. Well, if you think that the data foundation, the data is the stuff, right?

And the reason for it is the everything else. Having people who can work with data, there's there's two three, well, three different kinds of people or types of roles you might want to have. You might want the data analyst who can analyze the data, give you an insights into its health, etc. You might want a data scientist who can you know run experiments on it and give you some uh deep looks into what we talked about exploratory data analysis not too long ago, and you have a data engineering, which is the people who can help you store the data, make it accessible, put it in different types of formats and things, and and all three of those roles are people roles with data that again, not a lot of companies think about. I would add a fourth role to that, which is the project manager, because someone, you know, as as someone who's been on both sides of that, you know, skill set if you are focused on doing the thing, then it's hard for you to see all what everyone else is doing.

And so having one person whose sole responsibility it is to keep things moving forward is so important. So I would add project manager to pretty much any of these steps, probably all of them, right? And you might even have like a PMO that it sort of oversees all of them. So the second layer is the measurement and analytics layer. So you have data, but just because you have data doesn't mean you're doing anything with it.

So the second layer in the maturity model is how data driven are you, right? How important is data in your decision-making processes? Um, have you identified and and you know measured KPIs? Do you understand what happened in your data? Just because you have Google Analytics doesn't necessarily mean you know what the data is telling you to do.

It's it could just be a big pile of numbers. So that's the stuff, the processes around becoming data-driven. It's a big culture change to get people to say, stop guessing. Yeah. Well, you know, and it's funny because as you're saying that, it occurs to me that I've seen instances where people call themselves data-driven, but they're picking and choosing the data that best suits them versus all of the data, good, bad, ugly, and different.

And so that to me is not data-driven. That's using and manipulating data to kind of make yourself feel better, to make your company feel better, to make your team feel better. It's not truly being data-driven. Exactly. The example we always give is every time you use like Google Maps to go somewhere, you are literally being data-driven.

You decide the destination, but then you have data helping you drive the actual uh route you want to take. And sometimes there is uh a place for you know experience. If you know the back roads of your hometown better than Google does, um, you might know that a route might be technically a little bit longer, but it's a heck of a lot more pleasant to drive. Other times, like when you're in unknown territory, you're like, no, you know what? I'm gonna listen to the machine.

It's got better insight. I don't want to end up you know on some guy's like pig farm in the middle of uh eastern Pennsylvania. Nothing good happens there. So that's the second layer. The third layer is insights and research.

Uh, this is your qualitative capabilities, the ability to do market research. Because one of the things that's a challenge with data and the analysis of data is that you very rarely get to know why. Like, why do people abandon their shopping carts on your website? Why do customers uh give you two stars on Amazon? Why do these things happen?

And you can't ever get that from quantitative data, right? You have to unfortunately talk to human beings. It's funny you say unfortunately, whereas I on the flip side, I really enjoy that behavioral data, that that psychology. Why did people do it this way? Because I think you I mean, you're absolutely right, Chris.

There is too much uh of a reliance on the quantitative data, but it doesn't tell you the whole story. And so a great example of that is you know, website traffic. Well, traffic is up this month, traffic is down this month, traffic is up this month, traffic is down and down and down and then up. You can see the patterns in the data, but you still have no idea why. You can guess, you can look at external factors, but it doesn't really tell you why, unless there's like a clear-cut answer, like, oh, because we accidentally disabled our website, that's a clear-cut answer, and that's a really good insight.

But 99.9% of the time, that's not the reason why. So you do need to talk to people to find out why have they stopped coming to your site. Exactly. And from the people side, this market research is a separate profession, right? It is entirely a separate profession.

It's not something that your data analyst is necessarily going to have training in it. And so, from a maturity perspective, if you don't have market research capabilities, either in-house or with an agency partner of some kind, it's hard to get comprehensive, rigorous understanding of what's happening in your data and the reasons behind it. You really do need that specialization if you want the data to be credible. One of the challenges we see with market research is there's a whole lot of hippo issues, right? Which is usually the uh the executive on staff who's like, oh, well, our customers want this, like you're not our customer.

And our team that's John. I'm always making stuff up. Yeah, it's gonna be huge. It's the next big thing. Um, as someone who is trained in market research and clinical research, it really is its own specialty.

And one of the pitfalls that we see marketing teams fall into is someone, you know, that highest paid opinion says, Well, you know, just create a survey, which you know, in execution is fairly straightforward, you know, putting the questions together. The question development itself is Chris, you had mentioned credible credibility, and so what often ends up happening is that these surveys are built in such a way to get the responses that they want, to make themselves look impossible. So back to the picking and choosing the data points that you think best suit your needs, the same is true of the pitfalls with survey development and market research, of asking questions in such a way that they're leading, that they're pushing the users in a direction to answer a certain way, and that's one of the arts of market research and survey development, is making sure you're asking the question in such a way that people can answer it however they feel like that you're not you know biasing them towards what you want them to answer. It's a huge problem. My yeah, it's a huge problem, and there's a lot of junk out there.

My wife was an admin years and years and years ago for a market research company in Boston that served political organizations, and they were the antithesis of market re what market research should be. They said client, usually some politician, um, come on in, you tell us what you want the data to say, and we will get the responses to be able to say that with something that looks like research that backs up whatever you know crazy position you have. And the the benchmark that we use for determining the credibility of a market research firm is a question like how okay are you with answers that you don't want to hear, right? How okay are you with bad news, right? And we judge that with clients when we onboard a client.

We're like, how okay are you with bad news, right? With hearing that your numbers are going down into the right instead of up into the right. And if they're like, Oh no, no, no, it's gotta say this, like, okay, this is not going to be a uh productive engagement because we traffic in in accuracy of data, whether or not that's what you want to hear. And so uh with market research, that's that's especially challenging because it's so qualitative to begin with. It reminds me of uh a Parks and Rec episode, Parkinson Wreck being a TV show with Amy Polar, and it was they were like polling the community, and it was they had phrased the question something along the lines of you don't not want this thing that you don't not want.

And it was like 52% agree, 38% disagree, 16% are just flat out confused by the question. And so clarity like that clarity getting to the point, but also not you know pushing people in a certain direction is just it's so important. Exactly. So the fourth layer on this model is process automation. One of the things about adopting AI is that if you do it right, it is it takes time, it takes resources, it takes people, it takes budget to do it well.

And you need to find those resources within your organization to be able to do it well, which means you've got to save that somewhere else, or you know, just uh invest more heavily in it. And so one of the things that we look for is whether a company has done a good job with process automation. This is not AI, AI and automation are not synonymous. This is have you written code, for example, somewhere in your organization to handle simple tasks? Have you automated away the most repetitive tasks that require no machine learning whatsoever?

Um, for example, at a the the PR firm we used to work at, one of the junior most persons' jobs, uh, which is appalling, was watching them Google for stuff, then copy and paste Google results into a spreadsheet. And that was their job eight hours a day. Now that was a job that you know, we always like to you know, Katie, you just answered this in in a Twitter chat, you know, will AI take your job? Well, in this case, yes, that person's job should be taken by a machine, and and and it should be because it's uh it's a it's a low value job. And they were probably super grateful.

Exactly. So if a company has not done any kind of process automation, um they're probably not in a state to be able to do AI well, especially because process automation involves working with a lot of the similar underpinnings of AI. So, an example, um we curate our content for things like the Trust Insights newsletter and stuff uh with code. Now there's no AI involved in it, right? It it just downloads data, does some keyword matching, and then puts it in a database.

And then we have another piece of code that extracts it, scores it, um, and spits out a essentially a spreadsheet. There's no coding involved. I mean, there's no machine learning involved in the current version. There's there will be the next version. That process automation took what is a three and a half to four hour a week job and boiled it down to a three and a half to four minute a week job.

And most of the time is just waiting for the server to catch up. That's an indicator that we're ready to go beyond static automation because we figured out we can connect to data sources, we can get the right people at the table who have technical talent. We have somebody who can think about data and how to store it and how to retrieve it and how to process it. And so the prerequisites for AI are also the prerequisites of process automation. So it's a really good barometer of saying, yes, this this organization's ready.

It's, you know, it it's funny, Chris, that you started off by saying, like, have you written code? Have you done this? I would say, has anyone even written down how you do the thing? That's I'm never, I guess I shouldn't be surprised, but I'm always surprised every time I sort of go into a new team or you know, start with a new client, and I was like, Well, how do you know put this report together? And then you ask someone else, well, how do you put this report together?

And then you ask someone else, how do you put this report together? And you get three different answers of how it's done versus here's a standard process. I remember with our old team, I think one of the first conversations I had with the team was, well, where's your list of standard operating procedures? And they all kind of looked at me like, uh, what in the what now? I was like, you know, you your documentation, and you know, it was a professionally immature team, so like the team wasn't that old in terms of its formation.

And so a lot of that documentation didn't even exist. So we couldn't even get to process automation until we could just come to a shared agreement amongst eight people of here's how you actually do the thing. Exactly. And again, process automation, particularly once you're starting to talk about um automating with technology is yet again another profession, right? The ability to code, write code, write macros and stuff like that.

Now, you don't have to be a hardcore programmer, you can do a lot with things like Excel macros, right? There's that is a very valuable skill. But um, once you get beyond a certain point, yeah, you start getting into languages like R or Python or C sharp or uh even Java to be able to process um those underlying data sources. So the fifth layer is a data science capability, and this is it's at this point in an organization where we say the road splits, right? If you're gonna be using artificial intelligence as a core competency as part of your secret sauce, the next three steps in the in the model are things that you have to have in your organization.

On the other hand, if you are applying AI in something that's not a core competency, right? Maybe you just want to make content curation faster, your your content marketing team better. It's and you're not a content marketing company, you're like a yogurt company. Then the next three steps are things that you would use to audit partners and agencies, you know, somebody that you would want to bring in to help you out with. Um step five is data science.

So the underpinning of all artificial intelligence is statistics and probability, mathematics. And you've got to have those statistical and mathematical capabilities, the code, the engineering, to be able to work with data and understand you know what is capable of, what's possible. Again, going back to the the previous episode on exploratory data analysis, you know, we said that was a profession in unto its own end, it really is. It's part of this data science layer. So you need data science people, processes around it, and of course the requisite technologies to make data science ca possible before you can even start talking about AI.

You know, there's a joke, um, you know, and depending on which profession you're talking about, it's well, I got into this because I don't like math. So, you know, we often work in the marketing industry with other marketers, and the joke being I got into marketing because I don't like math. Well, unfortunately, joke's on you because every single one of these things that we've been talking about involves math. So if you don't like math, AI is probably not the right tool for you. John, do you like math?

I started math at a young age and became an economics major. So yes, I'm guided by I am data-driven. John is personally data-driven. I love it. That's your tagline from now on.

I don't even know where to go with that. No, math is painful. People do have to remember that math is painful. I think that's the biggest thing that people miss is that they think there's some people out there that math is always easy all the time. And no, even the people in the cutting edge of math are banging their heads against the wall trying to solve problems and get to the answers.

And that's the mind shift that you need to make. Like don't ever think it's easy, but don't think that you don't have it right just because you're having a hard time with it. Exactly. So you or the agency partner you're working with needs to have data science capabilities to because before you can start the process of doing machine learning, you've got to know whether you have the the data that is suited for it. And tasks that are suited for it.

You know, one of the things that we've been trying, and and Katie, you you did a great job with this in the Twitter chat just now. If disabusing people of the notion that AI is somehow magic, it's just a big pile of math. And if you have a problem that is inherently non-mathematical in nature, AI will not help you. It will just it's extremely quickly and very fancily waste a lot of your time and money. Mm-hmm.

And so, you know, I every once in a while I'll sort of mention that when I was a project coordinator a couple of jobs ago, um, I had to manually test the logarithmic functions by writing out the uh formula by hand and test it against what we had programmed into the computer. And at that time, when I was when I was doing that job many, many, many, many, many moons ago, AI in this context just wasn't an option. The automation wasn't really even an option. We were still doing things with floppy disks and external hard drives. And it just being in that uh sector in the um you know clinical trial sector, it just wasn't a thing.

And my God, would I have loved to have AI doing that for me? Um, because it was just a math equation. That was all it was, and I would love to have had that as an option. Exactly. Were you like rocking a TI35 calculator?

Yeah, I was actually. You know, this is totally off topic, but it's important. You know, on iPhone, right? When you've got the cheese ball calculator, if you turn it right, you'll get like the full TI deck. Oh.

So I know you were just sitting around, you're like, oh, I need to calculate the log for that. What am I gonna do? Yeah. I was actually. Um, no, but that's but yes, I was using and I went through a lot of them because we did a lot of testing.

Um, but that's the kind of stuff that AI is like, yep, I got that, I can do that, I can do that in nanoseconds. Let me just gimme. Exactly. So then the next layer in the maturity model is the deployment of machine learning, at least in some kind of testing capacity. And this is where you are now starting to actually do AI.

You're taking data, you're telling a machine, learn from it and write your own code. Because a machine learning model, which is a term you hear a lot, is just fancy for saying software written by machine. You know, a humans wrote, for example, Microsoft Word. That's a piece of human software. Uh humans wrote Candy Crush.

Machines have written things like GPT 3, which is the uh general purpose transformer for natural language generation. A human never wrote that, a machine wrote that, but it's still just a piece of software. And so when you're deploying uh machine learning within your company, you're looking for and building pieces of software that take in data, learn from it, and then do something with it. So I'll give you a real simple example of one. Um, this is one that we wrote for Katie's Twitter chat uh the previous hour.

And this is a topic model, it takes in all the tweets from the freelance chat that Katie was just speaking on, takes them apart, looks at the words, and then uh reassembles them into a series of topics. And that's all it is. It's you know, it's but it there is learning because the machine has to essentially learn what was said and then categorize and group it together. And that's essentially what machine learning is. So here that's the diagnostic uh that's not helpful.

That well, and that chart tells you how many topics are actually in the conversation, uh, but it's not particularly exciting and fun to look at. Um there we go. Let's bring this. So here are the topics, right? Natural language processing was in there, Katie and the various Twitter handles, um, AI tools and math equations, uh generated content.

Again, that's a pretty good representation of what a lot of the conversation, particularly the things where people had a lot of interaction. That's machine learning. So uh this is we've we now have a model to be able to judge you know what happened in this Twitter chat instead of having to read through uh how many tweets was that anyway. Uh it was 660 tweets, right? I don't have time to it felt like a lot.

My he my head still hurts. Exactly. I don't have time to read 660 tweets, but I have time to look at a single chart with some understanding, you know, about the community that was involved and take some decisions on us. So that's machine learning is taking data, learning from it, and building software. Is it is it a true statement that math is the same in any language?

So math equations, a math equ like the paract the Pythagorean theorem is the same in any country. It's just the Pythagorean theorem. And so that's one of the things that I find most interesting about the potential of AI, because it's just powered by math equations, it is a universal language. It depends, which you knew I was gonna say. Actually, I was hoping you'd be like, yes, you're right, you're a genius, but clearly that's that's not today.

Maybe tomorrow. The underlying mathematics, yes, are portable, but there may be data that the machines have learned from that are baked into the model that are are dependent on uh regional factors, natural language models would be an example. Yeah, no, and that like the the data that the models ingest, I feel like is totally different from just having the core set of math equations. Like, you know, we we talk about how we we use the AREMA model for predictive forecasting. The AREMA model in North America is the same AREMA model that you're going to use in the UK because it's just a math equation.

And so yeah, auto-regressive. Yeah. The data that you ingest, that's where you get into the all the differences. But the equations themselves are universal. That's correct.

That is 100% correct. Um auto-regressive integrated moving average is the same in every country. Um the last stage in the maturity model is the deployment of artificial intelligence of machine learning in the enterprise with a strategic focus to say that as a company, you are using artificial intelligence to solve problems and that you are approaching problems from the perspective of building artificial intelligence models to solve those problems. So the probably the most famous example of this is Google. Google in how employees are asked to think about problems is say, I want you to solve this problem not once, but I want you to think about a system possibly powered by machine learning that will solve it forever and it will make it go away, as opposed to, you know, if you think about what's on our to-do list every week.

Every week there's things like, oh yeah, we got to do this this week, and it's the same task pretty much as last week. Um, as opposed to saying, let's build a piece of software that solves this problem and makes it go away permanently because the machine's gonna handle it now. And so there's really you know, a handful of companies who are at that level, you know, mostly large technology companies, and even then it's it's not necessarily the entire company. Um, you know, for example, with IBM, there's certainly plenty of places and departments and divisions inside of IBM where AI is they are not an AI first company universally. Google is not an AI-first company universally, but these companies have made it a strategic priority.

So that's that is the ultimate sort of pinnacle of AI adoption. Do you feel like it's a problem to put to say that you're AI first versus people first? I think it's a false dichotomy. I think it can be both. Um you are solving problems strategically by trying to make them go away in an in an automated fashion.

Ideally, that means that you are actually then freeing up your people to be able to do to tackle problems that are not repetitive, right? If you if you are AI first, you want all of the repetitive BS to go away. All right, John, pop quiz. Where on this model do you think trust insights falls? Oh, let's see.

Well, we are AI powered. I mean, I can say that you know, digital customer journey and some of these reports that we run are run on machine learning, and that's the only way we get to that stuff. So at least portions of the business are fully AI driven. Um on the other hand, say our Facebook's ad, our Facebook ad campaigns are more down at the data foundation problem level. You know, we've got some reports and we're sort of uh aware of what's going on.

But yeah, I think we have a corporate competitive advantage because of our use of AI. So yeah, at least part of the company is fully mature. What do you think, Chris? I have answers, but I want to hear yours first. Um, you know, I think John's right.

It dep certain parts of our company, you could say we go all the way up to the top of the maturity model, but I would say other parts of our company, you know, the Facebook ads is a great example. Um, we can't even access our Facebook ads at this point because that bad. Because Facebook locked us out. So we're we're we are solidly stuck at the first rung of the ladder uh in that particular context. Um, I think that we want to be more all the way up to the top of the rung of the ladder, but because we're such a small company, we it gets down to resources and skill set.

And so with Chris being the primary resource for a lot of our data science, machine learning uh and those pieces, it becomes a challenge just because of his personal bandwidth. I would say our our baseline as a company is around the green step, the process automation, where we have automated, I think as much as we reasonably can, again, with those resource constraints in mind, but far more than other companies of our uh revenue, for example, um, and with far fewer people than those than other companies, you know, other peer companies. And we do that through process automation. Because a lot of what we do is not AI, right? You know, when we're processing customer data, like we ever every you know, week we take millions of records from one of our clients, process them, stick them up into a big query database, and then you know, that's available for reporting.

That's pure process automation. There's no that not a lick of AI in there anywhere. And it doesn't have to be because it's the wrong tool for the job. Um we are, as John said, in some areas we are using machine learning. Monitoring your Twitter chat uh is an example.

But one of the things that I think we have to be cautious of and advise people is that you know, it's kind of what you were saying, Katie. Not everything has to use AI, right? It's it's the right tool when you got a lot of data. It's the right tool when you got a lot of repetition. It's the wrong tool for everything else, right?

So if you're sitting there saying, like, how do we solve you know our our creativity crisis, you know, and we you know, people are not being innovative enough, AI is not gonna fix that problem for you. Right, you're just adding tools on top of problems, exactly. It's like you you you have a kitchen and you just keep buying appliances and still nobody, you haven't hired a single person to cook, right? So it's like, hey, uh 14 blenders, like, yeah, and you have nobody, you know, the dog is in there staring at them, and that's it. That's all that's in the kitchen.

Um I've just got this really funny visual of my dog just looking at the blender, like, is that gonna do something? Exactly, right? Maybe okay. And and but that's a really you know, it's a good funny visual, but it's also true that's what a lot of AI tools are like. Um so the AI maturity model is something that you can look at broadly as as a company, but it should be ideally be matched up with a problem uh statement, right?

A user story of some kind. Is this a problem that AI should be used to solve? Right? It looks like a blender. Should you solve a soup problem with the blender?

Maybe. Should you solve a steak problem with a blender? No. Only if your jaw is wired shut. Exactly.

Um that's what you would dis determine in your user story. I, the person who's hungry, want to blend up a steak because my jaw is wired shut and I need the protein. Exactly. Um so if you are looking at this model and you're looking at your company or your organization within a company, and you're on the green rung or the orange rung, it's not an indictment that you're bad somehow. It's not an indictment saying, oh, you've you're you you've fallen behind.

Well, you might not have problems that are solvable by AI, right? That's if you're, for example, if you're doing food innovation, um, you maybe you're making sweeteners and thickeners and stuff like that. Yes, you can do some mixing and matching of large-scale nutrition data sets and process, you know, the open food fax database to see what ingredients are showing up. But at the end of the day, you've got to feed that to somebody and say, hey, how's that taste? You know, in a test kitchen.

And that's not something that that's not a problem AI can solve. Well, and you know, I think that, you know, as you were sort of talking through this, uh, Chris, you know, I think that there's a lot of there's that shiny object syndrome about AI. And there's this misunderstanding that, you know, if you introduce AI, then you are suddenly, you know, competitive or have a competitive advantage over your peers. That's not necessarily true. Because to your point, the problems may not have anything to do with AI.

You may have the wrong team, you may be serving the wrong market, you may have. I mean, we talk about it at a very basic level. It's what the audience, the offer, and the creative. If one of those things are off, AI can't necessarily fix that. It may help you understand what the problem is, but it's not just going to be like, and here's your solution.

And now if you just do this one thing, you'll make a million dollars. And again, this is something that came up during during your Twitter chat. AI is trained on data, right? And the largest amount of data comes from the biggest sources. The biggest sources typically are like, you know, large competitors of yours.

Which means that if you're if you're using AI as a competitive tool, maybe for like you know, generating content, and you've trained it on your competitors' mediocre content, you will inherently be spitting out more mediocre content. So by definition, that's how AI works. Uh, it trains on what is existing, and it's always biased towards the largest data source. So if you want to sound like everybody else, by all means, you know, have AI write your content from start to finish. If you want to sound different and ideally better, um, at the very least, you're gonna have to fine-tune a data set to you know to uh to remove a lot of the mediocre crap.

And you may find that, yeah, it's not the right solution because the the qu uh quantity of excellent content is so small compared to the ocean of mediocrity that there's not enough excellent content to train an AI on. And so you may just say, you know what, we're just gonna do it the old fashioned way. We're gonna get out our pens and pencils, we're gonna go to local bar, we're gonna get drunk, and we're gonna write until something good comes out. John, I think Ocean of Mediocrity should be the name of your Morrissey cover band. If I was gonna start a Morrissey cover band, yeah, that's that definitely has a more secure slant to it.

Black eyeliner, I'll be good. I think you know, this is all, you know, it's it goes back to, you know, AI might even be a moot point, or rather, will AI take my job might be a moot point if your organization is not even close to being ready to introduce AI if you don't know where your data lives, if you don't have anyone to pull the data together into some sort of understandable form, if you don't have anyone to analyze the data, pull out insights, if you don't have standard processes in which the data is collected in which you do things, like those are the four basic things that you need to master before introducing artificial intelligence into your team, into your role, into your company. So if you're still stuck at the first rung, then you definitely have nothing to worry about in terms of AI taking your job, because AI cannot fix that problem. AI cannot fix your bad data processes and bad data integrity. It can highlight the issue, but it can't fix it.

And when it comes to you know thinking about whether AI can take your your job or not and what you should be doing with your career, we talked about as we walked through this model, how many individual professions are bundled in here data scientists, data analyst, engineer, coder, mathematician, statistician, process uh manager. Um there's so many individual professions that if you want to up level your career, figuring out what you do right now and then adding in some of the skills from those other professions will make you a more valuable to your organization, but B much harder to replace. Because if you have data analysis skills, but you also have data engineering skills, and you know, for example, how to move a database in or out of third normal form, you are a better analyst because of it. And as from the engineering side, you understand how the architecture of your data will impact the analysis. So what you if you have with one foot in both worlds, you're much more valuable than someone who's specialized in one or the other.

And even if you take away little tasks from both those, there's still so much synergy between these the two different roles that a machine is gonna have a real hard time uh replacing even the majority of your tasks, because they they're they're so convoluted and intertwined together. So the more that you can take multiple synergistic skills and and combine them, the harder it is to replace you. Agreed. All right. So that's the AI maturity model.

Again, think about where you are on it. Um, think about where you might want to be personally, professionally, uh, for as an organization and as a company. But don't beat yourself up if you are not as far as you think you should be, or if you perceive that a competitor is further ahead than you are, because again, just because someone else has a nicer blender doesn't mean they're a better cook. Um any final parting words. I'm gonna have to write that quote down.

I th I think that covers it. Alright, folks, thanks for tuning in, and we'll see you all next week. 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 and a 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.