John Peebles On Building AI Automation Inside Administrate

Welcome to the Growth Elevated Leadership Podcast with Julian Costelli. Each week, we talk with senior tech leaders to explore stories and insights about the challenges involved with growing technology companies. We hope that these stories can help you become a better leader and help you navigate your own growth journey. Good morning.

This is Julian Costelli and welcome to a special episode of the Growth Elevated Leadership Podcast. This is our first AI Sherpa podcast where we talk with tech leaders and we're going to talk about how they're using AI to succeed at their business. This is part of the broader Growth Elevated Leadership podcast, where past guests have included CEOs and CXOs of great companies like Workfront, CHG Healthcare, Pathology Watch, InMoment, Canopy, the San Francisco Furners, and many more.

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com. Today, I'm happy to welcome John Peebles. John is the CEO of Administrate. Administrate is a leading training management system that helps large enterprise customers plan, schedule, communicate, and adjust their training programs with their employees and their customers.

John is a technical founder who previously served as a VP of technology and CIO for several healthcare technology companies. John is currently a board member of Current Health and TuringFest. John is coming to us today from Edinburgh, Scotland. Welcome, John.

Thanks so much for having me. It seems like whenever you're involved in Scotland somewhat, the weather is beautiful outside. So when you visit, it's beautiful. Today is a beautiful day.

I thought it was just always beautiful. Is that the case? That's actually the case. Yeah.

No, it is. It's always beautiful. Thanks for having me. Yeah, I've enjoyed our visits.

I want to do it again. So, John, this is super exciting. I know you've been working a lot with AI and you're running a tech company, but we're at the intersection where tech companies are using a new technology and you're particularly well-suited to talk to us about it, given your technical background. Before we get into it, tell us a little bit about Administrate.

What does Administrate do? Yeah, so Administrate is a platform that is designed for usually large enterprises to manage all their classroom training. Classroom training, believe it or not, is still pretty much the predominant way that training gets delivered in corporate America today, particularly in highly skilled or highly regulated or environments that somebody could die or millions of dollars of equipment could be lost. And so there's no better way to get a student to a classroom.

The problem with that is that it doesn't scale very well. And you got to get people in certain places at certain times. And all of that logistics is a struggle, particularly when you're trying to keep the training the same in China as it is in Europe, as in South America, as in the United States, which many of our customers are operating across the world. So our software basically helps identify areas where there might be problems, alerts our users to those areas, and also helps them significantly in the planning and scheduling phases, which you can imagine having to plan thousands of classes globally for maybe a quarter or two quarters ahead can be a really big lift.

So it's an interesting problem to solve. Sounds like a huge logistical challenge if it's in person, right? I thought I thought it was all moving to the LMSs. That's not the case.

No, I mean, there's been millions, hundreds of millions of dollars spent in trying to convince everybody that all learning will be done online and self-paced and all that. But I think, you know, classroom training is here to stay. And the pandemic was a big test for that. I think training levels in classroom stayed unchanged, although they went to more virtual means.

But yeah, we partner with a lot of the traditional learning management systems out there. And it's just a great way to give learning leaders multiple ways to tackle a training problem, whether it's self-paced, in-classroom, a combination of both. And we can drive costs down on the delivery there on the classroom side. Terrific.

And so your customers are large enterprise companies and they have to bring these people together. So it's like a logistical planning tool set that allows them to schedule that almost like a school administrative system, right? Yeah, totally. I mean, look, we've all spent lots of time in school, but we very rarely think about the logistics involved in getting that classroom and the materials and the teacher and the students wherever in the right place at the right time.

And I would say probably the closest analog to the scale of this problem is we've all probably flown on an airplane and maybe had a scheduling mishap or something along those lines. It's a lot like that for our customers because they have these planes, which are classes that need to run and people need to be at the right place. You have the right equipment and so forth. And then things happen.

Weather happens and equipment breaks down. And so your flight has to be rescheduled. And it's the same exact problem of, no, my flight to New York City has been rescheduled or there's a problem and we've put you on this other plane. And if you don't like that, give us a call.

It's a similar experience when you think about classroom training. And, you know, some of our customers are doing all kinds of things that you wouldn't expect. Roche is a great example on Indianapolis. They have these blood analyzer machines and you have to learn how to operate them.

The FDA regulates these. You can buy the machine, you legally cannot touch it until you've gone through training. And so the training involves everything from getting the machine in the right place to making sure that the fake blood is at the right temperature in advance. And you can run the fake blood through the machine.

You have to clean it afterwards. And these types of things we just don't think about when we're thinking about the education requirements in an industry like health care, for example. Terrific. And so what's been your company's journey with AI?

We're going to talk about one of the specific projects that you've had success with today. But what's been the journey in terms of how the company's been adopting AI? Tell me where you are on that journey. Yeah.

So, I mean, one thing that I think is really important to understand is I am an AI skeptic. I've got a computer science degree, specialized in AI a long time ago, 20 plus years ago. But a lot of the techniques and concepts remain. And when LLMs kind of busted out on the scene, you know, like everybody, I tried it, you know, tried to ask how many R's were in strawberry and all the little gotchas and things.

And I was pretty underwhelmed. You know, it was cool to be able to generate some weird AI slop art. But apart from that, you know, I didn't personally find it very meaningful in my day to day life. But I found myself at this point about a year or so ago, actually the last two and a half years ago, where I was convinced I was right.

And this was mostly hype and not very useful. And yet everybody else was convinced that I was wrong and they were right. You know, when you've got, yeah. And, you know, it's like I would go out for beers with, like, there's a real good friend of mine, lives across the street, building an AI company, sold a previous AI company to Facebook, now Meta.

And I'd just be like, am I insane? You know, like, like, I'm just not finding this to be that useful or whatever. And, you know, there'd be bits and pieces and he's a bit more nuanced, measured, much more intelligent guy. And he would kind of point out, but yeah, but these are fringe benefits.

It's not this cataclysmic thing until about, but I think one of the key things like in business, you know, in startups, you're looking for product market fit. You cannot just write off what you did two years ago because we tried that already. You got to keep going back and revalidating all the time. Because it's changing so fast, right?

It's changing so fast. And it was about almost a year ago in May of last year when Claude Code got launched. And I started playing with this like I would and was very skeptical and prepared to be underwhelmed. Trying to break it?

Trying to prove your thesis? Yeah, absolutely. Just, you know, trying to torture this thing and get a gotcha moment or two and whatever. And the interesting thing was that I was like, wow, this is actually doing stuff that is useful, that is not terrible.

And that kicked off basically a year plus now of experimentation. And it wasn just me in a vacuum either but it was again colleagues folks that I met over the years all trying to play with this thing And that kind of how it got started for real It was about a year ago Terrific Well listen we got this framework that we been talking about at Growth Elevated of three different levels of AI implementation and utilization The first is to enable your employees. The second is to automate some of your internal functions that are some of the most manual difficult things, the pyramid of suck.

And then the third is to actually put AI into your product. I think you're going to share an example of stage two with us, right? You automated an internal process. Is that the case study we want to go walk through today?

I think so. I think it's an example that I find to be very actionable, very practical. It's something if you're listening, you can try out with fairly low barrier to entry and fairly low blast radius, you know, and you can probably also try it out yourself if, you know, the rest of your company is not believing any of this AI hype like I was. Well, sometimes you want to prove it through an example, right?

And that's why I want to share the story. So tell us what was the process and what was the process before? What put it at the bottom of the pyramid of suck that made you say, man, this is worth trying to shine a laser beam at? Yeah.

So we have just come through an interesting time at Administrate. We kind of effectively rebooted our entire market focus, market segment, go-to-market strategy, entirely new commercial team came in as we now today only sell to large complex enterprises. We used to have a business that was S&B focused and that's kind of gone by the wayside. And that's a huge transition.

It's a lot more different than you'd wish. I feel like I've been the CEO of two companies, maybe even more. And it keeps it interesting, but it is extremely difficult, took longer than anybody thought, was more expensive, etc. As part of that, your implementation efforts really ramp up in terms of the complexity.

That's right. The enterprise customers expect more. The change management just on the people side, the configuration that has to happen, the integrations that they are looking for with other systems within their tech stack, it's all just a completely different beast. And for us, that beast we were hoping would be about 12 weeks, you know, just kind of the industry standard, at least in my experience.

But you always want it to be shorter. The problem that we had was that our platform requires a number of decisions to be made kind of early on about how it's going to be deployed. So just simple things like, are you selling training or not? Right?

If you're selling training, that opens up a whole can of worms on discussions around what are the business models you're using? And are you going to use our infrastructure to sell it online? So all the configuration steps that you'd need. Do the orders come in from Salesforce or do they come in from both your website and Salesforce?

All of these types of discussions. And this is the type of thing that would take a long time during the implementation process to not only get through from just a dialogue perspective, but once we had the information, it's like, okay, yeah, we sell in 25 countries. And of those five are franchise sales channels where we don't really control everything, but we want to know what's happened. And, you know, so these complexities start to balloon out.

And where would these complexities go to? Like, what was the process? Would you have an implementation manager and a spreadsheet, basically? Yep.

Yeah, the team was a project manager and then a solution consultant and then a bunch of wild spreadsheets. And that was just for the configuration information. And then you would get to the point where it's time to load data. And this is a familiar problem for a lot of us in enterprise software, but you'd then get the data and it would be wrong in many different subtle ways.

Or incomplete. Or incomplete, or somebody would be promising it any day now because they were typing it up from whiteboards and whatever. And then you'd have to get that loaded in, test it all with the configuration, and then you would hook up various things and hopefully go live. You've described the product as almost a Lego set, like you can configure it in so many different ways, which is a strength.

At the same time, the complexity that that creates seemed like it almost could be overwhelming. Yeah, 100%. And this was a problem for us because our go-to-market motion was starting to work in some ways better than we anticipated. We got some more complex customers than we expected.

And we were projecting out and saying, look, we've got a small team. We want to keep it small. We don't want to get out over the skis, but we've got some large customers that are in the pipe that are maybe out for signature. Large expectations.

Yeah, yeah. And so then you extrapolate out, okay, it's going to take - and so this configuration information, which would be stored in a spreadsheet, would then have to be translated into a control panel. And if you look at our control panel, it's like a lot of enterprise software. There's like 50 or 60 different sections, and each of those have tons of knobs and things.

Like flying a plane, a complex plane? Yeah. And, and, you know, what, and, and like, we weren't idiots necessarily. We understood that this was a problem and our implementation team would come and say, hey, twisting all these knobs and fiddling with all these dials and translating the spreadsheet into the configuration control surface was time consuming and manual and error prone.

And if you got something wrong, sometimes it'd be difficult to, to go back and start over. configure something in a weird way the the data model configuration whatever it would mean you'd have to almost start over in some cases and and redo that that bit of setup sounds like a perfect example to try to try to automate or do something with ai because time consuming a little complex annual yeah important repetitive and and growing if you're growing if your sales are growing well right?

So you saw this big growing body of work that was going to constrain your growth? And you extrapolate it out and it's like, we cannot have a two-person team be doing one to three projects at a time. You know, there's just not, it's just not, yeah. And so the challenge is you, this team would come to our product meetings and say, hey, we need to be able to script this or run things in programmatically.

And our product team would be like, well, yeah, I mean, this control panel is largely built on legacy tech and we've been investing lots and lots of effort and time on building a really nice modern experience. But like every company, we have legacy stuff. And the problem is, is that you only set the product up once or twice or you fiddle with it maybe once a year or every 18 months or 24 months, right? Once you're done, you don't want to touch it again.

Yeah, the volatility of the configuration changes is low. I mean, it's very, very low. And so you could never rank it from a business perspective very high on the priority list because actual customers were getting actual value out of other things. And it's kind of like, well, this is just our cross to bear during implementation, right?

So I think it was on the plane home from the Growth Elevated Conference this year where I had been messing around with Claude Code. And I feel like despite being an AI skeptic, the thing that I love, I believe that when it's all said and done, agentic coding is probably going to be one of the very few killer apps that is left. And the reason for that is there's a forcing function where the LLM generates a bunch of stuff and you have no idea if it's true or not, if it works or not, but you can execute it with code.

You can test it quickly. You can test it. I can't go out and check if the summary of Tolstoy is accurate or captures the sentiment exactly, but I can with code, right? And it either works or it doesn't, right?

And if you think about it, I mean, you and I have been in tech companies for a long time. We've spent tens of millions of dollars paying people to write code that we've never read. But we go out there and we execute it and we do the demo. The engineering says it's done and you go out and you're flying without a net.

You go and demo that in front of a customer. So it's not an unusual experience, actually, once you leave the keyboard. So and I think that's where having a network of peers that are experimenting with this rapidly is really valuable. because I remember talking to Mike, who's our CTO here at Administrate, Bush Guy, former principal engineer from GitHub, but probably best known as the lead maintainer for the Homebrew project.

So almost every engineer that uses a Mac ever has used some software that he's been responsible for. And he and I were always monkeying around with this stuff. And a lot of it is just trying to one-up each other, if I'm being really honest. You know, I'm trying to demonstrate that I'm still an engineer and he's trying to demonstrate that I'm actually a suit.

And there's all of this competition things. But I remember him telling me in the summer of last year hey one thing that really cool about Claude is that it very good You know LLMs are great at summarization and translation and things like that He said one thing that awesome is you can point it at a minified JavaScript library i.e. obfuscated bit of code, and it can basically like explode it out into normal English and code, and then it will tell you how it works.

And I just couldn't believe this when he told me. And he's like, yeah, try it. And so I, of course, I tried it and you take this library that's been intentionally obfuscated and it will just report the code to stuff you can read and explain it to you and translate it. Right.

And so I was sitting there on the plane and, you know, probably should have been trying to get some sleep. And like, honestly, I'd had a couple of glasses of wines. It's not like the clearest, like headed moment. Right.

And so the skeptic in you was vulnerable. Yeah. Yeah. Give it a shot.

And, you know, you're on plain Wi-Fi. And I just said, okay. And I found the part of our code base. I actually messaged an engineer and I said, hey, where's all this control panel code?

Right. And they said, oh, it's over here in this nasty area or whatever. Don't look there. It's dangerous.

It's, you know, whatever. And I was like, okay. Dangerous cobwebs. And so I just said, hey, Claude, look, we're trying to build a configuration work tool, workbook.

And I found all of our implementation documentation, found all of our support documentation. I got all of our API information. I dumped it in a folder and I said, we are going to build an integration tool or implementation toolkit that basically will explain to a customer, here are the decisions that we need to make and why. And it's going to reference not only our implementation artifacts, but also our support documentation.

I loaded up all of our marketing case studies and said, reference these as well. And so every time we say there's a decision that needs to be made, you will have a reference to a successful customer case study. And then, so, and I said, and then what we're going to do is please look at this code over here. that's awful and people say you know it's got signs up that say beware and don't tread in this area whatever and uh introspect it yeah figure out how it works and the code haunted house yeah yeah exactly get in there figure out how it works where all the ghosts are and whatever and uh and then just um build out an api that that can manipulate it and it did and and it was about half an hour, 45 minutes.

It almost one-shotted it because, again, they're very good at translating code from one language to another or whatever. And basically, we had this script, a mission of scripts that I could run on my local machine. So I didn't need to deploy anything. I didn't need to call our engineering leads.

It was within our secure boundary because our laptops are encrypted and so forth. And I could basically punch a button and it would generate out a bunch of decision, we call them decision trees, in Notion. It would then walk you through these decisions. And there's a great feature that Notion has, which is you can incorporate little databases within the pages.

And so it's kind of like an access database if you're old enough to remember that. And so you could fill out things and then there's referential integrity. So one of the things you have to do is you have to say like, how many companies do you have operating within administrate? And then what regions do you have and what regions do which countries operate in?

And because of the way that Notion works, you can enforce that they can't choose a region that doesn't exist, right? And so you go through all this, and then it would just output a YAML file that was human readable. And then you can look at it, and then you could punch a button. And 30 seconds later, all the config is imported.

Your instance is completely set up. And that is dozens and dozens of hours of manual knob fiddling and whatever that is now gone and doesn't need to exist anymore. And the wonderful thing about it is you could then decide, well, I screwed that up. I forgot X, Y, or Z.

You can blow the whole thing away and run it again, right? And it'll just fix it. You just tell the model, hey, change A, B, C, D. Yeah.

Or the customer's like, I forgot this. Fine. Add it in and we'll punch the button again. And it's a tempotent so you can run it as many times and it won't destroy stuff that, you know, it's already got imported correctly.

And that was the first major step that like opened our eyes to, wow, there's these opportunities internally that are relatively low lift that literally seem impossible. Right. If we if we'd taken that, if I hadn't known about the translating minified obfuscated code into that, I just somehow heard overheard from Mike and he'd seen it somewhere and and whatnot, then you just would never have attempted a project like that. Right.

you know how do you do this it's almost too incredible to believe yeah you don't think oh it should be natural but that's that's the key is is having having an example like that to test out to see what's possible and also having a peer to to like you said you were describing the camaraderie and the challenges that you were challenging each other right yeah exactly and so you know then then you kind of get to the and there were some bugs or whatever here and there but it was like and it hallucinated a few things on the the explanation stuff whatever but you know you just, I gave it a read through.

And then the interesting thing was we ran into the next challenge, which was nobody believed me, you know? Uh, so I land. So that's a, that's a human challenge, right? Yeah.

It was just too much, right? It was. And I mean, I could barely believe it myself. And I'm like, I'm serious.

Like I think I have this thing and, and, uh, and, and the second piece of work that I did, which was pretty fun too, is, you know, we use gong, like a lot of companies to record sales calls. And so very quickly, we built a little script that sucked out all the calls from Gong, all the transcripts, boiled them down, and then basically went through and shoved them into a handover document framework that then sales didn't have to sit there and do a bunch of manual effort. And what was really cool about it is I was pretty adamant about every single time that we had a fact that was referenced, it had to be referenced, it would be footnoted to reference the gone call and the timestamp.

So it's not making stuff up. So you have that check. And it can still do that, but at least you can double check it and click right in there. And same thing for our contracts.

It would say, you know, they've bought this piece of the platform and it's contract page four, line 32, you know. And so, you know, we started iterating on this rapidly. And like I said, nobody believed it. I've convinced somebody to look at it.

one of our product managers, he couldn't believe it. And then we just said, Hey, like, look, we, we've done this work and we think this will cut out a lot of time. And, you know, I, I, it still took weeks for the team to really kind of understand or get through the shock of, wow, like just the digestion you process. Cause they were, you know, they were stuck in the old paradigm.

Yeah. And so thankfully, though, you know, they're great and they latched onto it and we had a couple of new customers kicking off. And, you know, it wasn't like nothing's perfect, but we could iterate very rapidly. And again, we're at the point where we were doing like a weekly 30 minute feedback session where we're looking for, hey, this didn't work very well or that needs to be tuned up or whatever.

it's we're now at the point where that's too slow because we can we can ship the fixes or the adjustments so fast yes right and so that's that's been really interesting but one of the wonderful things about it was i was actually had the opportunity to to meet up on a customer one of the very first customers that went through this process and it's a far cry from we've all done a sass implementation we've all seen the spreadsheets of doom or google docs or if a really sophisticated air table or something like that but we can provide a login to their section of our notion they log in everything's laid out it's explanatory um they we still get on the phone with them and explain things but they walked through this process and they they were just commenting you they didn't know it was a new thing for us they were coming out just how slick everything was and there's obviously been you know months and months and years of of uh refinement and all this stuff.

And you're kind of thinking, wow, like this is, this is wild. And the thing that I love about it. That was the feedback they were giving you on the, on the process. And you're like, we just, we just got this together.

This is brand new. Yeah. It was like, it was like, you know, less than 40 hours of actual well time. That must have been incredibly satisfying.

Yeah. And it makes our team look like. So you reducing time to get the work done You increasing customer satisfaction You accelerating the time for customers to onboard Those are all key benefits Yeah and massively improving quality and the perception of quality I think as well It's also just the customer is coming on board with a much deeper understanding of the decisions that influenced how things work in the platform, which is difficult sometimes to fully internalize. So, yeah, I think that has since grown arms and legs and spawned a whole bunch of subsequent projects that will be familiar to anybody that's running a similar style of platform business, which is great.

The configuration is done, but now we need to move on to that data import challenge. Right. And then there's other challenges that exist behind that. But I think what's interesting and what I love about this whole thing is it was just such a nice, self-contained experiment.

I had to have access to some R code. I got access to Gong because I didn't have that at the time. You know, I got an API key from Claude from Anthropic. But it meant I could test this hypothesis.

And it was a really fun thing to make a lot of progress quickly. And then it didn't become somebody else's burden. So myself and the product manager, we're still the owners of this. That's pretty cool.

You know, he spends probably a couple hours a week on it. And I spend 15, 30 minutes a week on it. But it scales. And it doesn't have to be this thing that we then, the CEO's nightmare pet project that gets handed off to somebody who actually does all the work.

Right, right. uh that's that's terrific what what advice would you have to other tech leaders to kind of cross the chasm if you will what was holding you back that that uh you might other people might identify with and what was your advice be to them yeah well so i think i think that to be charitable to myself which my girlfriend says and i always am i think that you know before clawed code came around it was difficult to get stuff done and painful right like you could do some coding but it was not it's just ergonomically annoying right and the models were not they've improved a ton so i feel i feel like i don't i don't think i wasted too much time right i was like i was on it within weeks of it coming out and and experimenting and playing and whatever and that i think that's a key piece which was i started playing on my own hobby projects you know projects making lego move around and you know my own personal websites and stuff like that and and i think just that that experimenting and hey can i try this getting familiar with the tools and what they can do otherwise you don't even know it's possible exactly and then you get it's like that thing right like we we've all been i remember going to like this winter camp once and i was like oh there's this snow hill like snow plows had packed up it's huge mountain of snow right and we get out there with like you know a garbage can lid or something and you know guys in college or whatever and it's like you know we uh we slide down on the garbage can lid and it's like okay now two of us are gonna go and then it's two guys standing up going backwards you know and it's like and then i think you know somebody breaks an arm and we still keep going you know it's like it's like it's that type of thing you slide into the river yeah it's really exactly it's really that iteration and that kind of because you start with oh can it write me this function can it write me this module?

Can it write me this, this, this bigger module? Can it write me piece of an app? Can it write me a whole thing? You know, that, that is really important.

So the experimentation is just so, so important. And I have, um, I have personally seen some of the best engineers, some of the most, the people I respect the most that have so much experience in tech, they just do not believe people will not believe it until they do it themselves. There's just no convincing anyone. You got to get your hands dirty, right?

Yeah. I mean, this wasn't something that you could have just delegated easily, right? Yeah. Or you could, and it would have come back, not possible.

Or, you know, we tried, and that was that. Yeah. Well, it's really hard when the CEO programs it and shows that it works. That's a pretty compelling way to get some change in the organization.

Yeah. It was funny too, because I was actually, I tried to almost keep it quiet that I had done. So I was like, we were experimenting. I was like, we, it was like, there was no we.

The royal we? Yeah. And then I was like, we got our product manager. I was like, oh, Hashan did this.

You know, because it's like people listening to it. Give someone the credit, yeah. Yeah. They knew Hashan was involved.

The AI committee. Yeah. It's just like, I didn't want people to know. And then I think kind of word got out.

And then, you know, it's kind of like, oh, wow. And it's been nice to see because now people are experimenting on their own stuff. Yeah, that's what you want. I mean, you've got to celebrate the successes.

You've got to make it accessible. And you've got to give people the room to experiment themselves. But that's terrific. So now you've got scalability in this area.

And now more projects are growing laterally. What advice would you have to another CEO who maybe isn't as technical as you? What advice would you have to others to just try to implement in their organizations? Yeah, I mean, I think the coding area is a real sweet spot, right?

I remain nervous. You know, we took, last week there was an election in Edinburgh here in the UK. And my girlfriend and I are going out to vote. And I go to the normal precinct.

That was where I voted two years ago. It's not there, right? It's an elementary school and there's a bunch of elementary school kids and people are like, what is this long haired guy, you know, doing like poking around? So I'm like, oh, that must not be the right.

I couldn't find my polling card. I thought I'd taken a picture of it. And so, of course, somebody pulls out and asks Gemini, you know, their phone and pulls it. Where's the polling place?

Oh, it's down, down, down many street, just down a quarter mile down there. Oh, that's. And I'm like, I just don't know. Right.

Like how this training data stopped about six months ago. So like how, so we look it up and it's actually, no, it's the library across the street, right? Like it's just completely wrong, you know? But never in doubt.

Never in doubt. Never in doubt. And I was on a family trip. My girlfriend and I met my parents and we're out in Italy and there's a European view of holidays and vacations.

And there's maybe an American view of this is our trip across the pond once a year, you know? And my parents were taking the American view and my mom lined up 75 stops in nine full days, you know? God. And they're all timed entry, right?

Because Italy has gone to all timed entry. And so this is very logistically, and actually there's a whole bit about using AI to do that. But there's like this fresh hell, right? Where part of the trip, the train wasn't running that day.

It had been canceled for whatever reason. And everybody pulls out their own AI and asks it. And all of the AIs are coming back and like, Gemini says this. And oh, you know, Claude says that or whatever.

And I'm just standing there in the middle of Italy with like, and I'm just like, this is just unbearable. Right. So I remain skeptical about a lot of these applications, but I think the coding area is where you should really have a look. There will be so many areas where there's simple little jobs.

One just popped up this morning. It's a bugbear of ours. It's been for years. It's like getting customer certificates lined up where they want the first name and the last name swapped in on a PDF.

It's going to be printer ready. And sometimes it's a plastic card. Sometimes it's a piece of paper. And it's all very important.

And that's a problem that we're going to tackle because we've been doing it manually for a long time. But it's code. You can validate it. You can see the output.

But I think those are the areas where, you know, these internal processes that will have just grown arms and legs are very difficult, very time consuming, soul destroying. If you can get them automated, it's low risk. If it blows up, the next person in the chain is going to know. You know, you don't have to worry.

The opportunity is much easier. It's like it's all of a sudden like they're emerging from the blur. Yeah. Yeah.

That's pretty awesome. Well, John, thank you for sharing that experience with us. Congratulations on that progress. and we're grateful to have you on the show today.

All right. Thank you so much and enjoy the day. Absolutely, we will. Thank you for listening to the Growth Elevated Leadership Podcast.

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