What AI Sees in Your Organization That You Cannot

Ryan:0:01
Welcome back to the [High Ticket AI Systems] podcast where we talk about all things client acquisition, scaling, marketing, sales, AI, and everything else in between. Everyone, please welcome the one and only Thomas White. Thomas, welcome to the show, my friend.

Thomas:0:17
Good to be here.

Ryan:0:19
Good to have you. for the people at home who may not already know, give them some background on exactly who you are and what your company Humanistics does best.

Thomas:0:30
Yeah, s so I'm a a serial entrepreneur. I started twenty five different companies over my career. First one I started was a technology company in the so true. Yeah. My first company I started when I was twenty one or so is in in New Orleans in technology. It was new at that time. And I s ultimately a company sold to a public company in Cupertuno, California.

Ryan:0:39
A serial entrepreneur, I'm sorry, but that is that is very true if you've twenty five companies.

Thomas:0:57
So I moved into Silicon Valley and was there as it came live. So I was at the first headquarters of Apple computer was built across the street from my office. And it was it was really the wild west. You know, people everybody came to Silicon Valley, they were hiring people like crazy. So every quarter we had to raise everybody's rate or they'd go somewhere else. So it was it was way fun and exciting. And I did technology for 30 years and then I decided

Ryan:1:06
Wow.

Thomas:1:26
I wanted to really take a different view. So I bought a professional development company, working management side and leaderships, you know, cultures and so forth, and did that for a good while. And now I've come back to technology. I've come back because I 'cause I've always wanted to solve problems that looked hard, but when you put different components together and look at them through a different lynch, you can solve them in ways people never imagined. That was my first business and that's what we're doing today, so I didn't change.

But those 20 years or so I was looking doing other things, I was really learning how we as humans operate. What we we're not a machine, we're we're something totally different. And one the the challenges with AI is it often thinks of us as a machine. Early AI in the 60s, people may not remember AI isn't new, it's been around a while. It was very much a mechanistic approach. And when you have you when you do that, you don't really solve the problems. And what came out of that early AI was factory automation.

All the robots and things with

Ryan:2:22
Thomas, can can I just bring you back to what you just said about AI not being new? Because I think this is a really common misconception. people usually say AI has just been around for three, four years. Like what what's your view on that? Where did you see this this whole space started?

Thomas:2:29
Reason that

Thomas:2:37
Absolutely not true. It's it's it started in the sixties and seventies. You go back if you could just anybody have anybody's there knows its the history. Started in MIT and other schools on the East Coast, and they were they were solving some of the problems we're solving today, but the level of technology, the capacity was very limited. So they could solve it to the level they could solve it. So what you could solve was repetitive work in factories. So instead of having a factory worker put a rivet in. million times, you have a machine do it. And the machine would do it more accurately and the ability of to be able to observe it and notice if it's off and stop the assembly line. So there's some capacity, capabilities that came out of early investment AI. What we see today, even today, the the work of whether you get chat GPT when it came out, all these things, it had they had been around 10 years. So that wasn't brand new either, but they had to they had to work a long time to get to that threshold of it was good enough. So it didn't just like Okay, flip a switch and it's good enough. No, it took them a long time to get there. And what's the why it's so phenomenal is still it's the thing about the doubling effect. When you get a when you get a jar half full and you and you're gonna you double it, it's full. But how long does that take you that doubling to get it to half full? A long time. And then boom, it looks like it happened magically. No, it just it's a doubling effect. Same is happening with AI. When you get you have a critical mass and you double that, it's just wow, it's like whoa.

And that's the excitement of it. And we're in territory we've never been before. So humanistics. Well, I know that's so so so when the when the when the internet boom happened in the late nineties, it looked like it came out of nowhere. No, I was working when the early technologies that came out of DARPA, which is the Defense Department s structure, of hooking universities together to communicate.

Ryan:4:11
So it seems like it's come out of nowhere, but really there's this whole history behind of it.

Thomas:4:30
So it was there long before it was there thirty years before the the internet boom happened. So these things take time to to to get in the right places, get enough use, commercialize it so it's cost effective, and then boom, it happens because the people that are using it now weren't looking where it was developing. You wanna know what's gonna happen in ten years from now, go to the universities, go to the research labs, see what they're doing. And and then you can start seeing what the roadmap is. And people that invest in these areas, people that make serious money are watching.

Ryan:4:57
If you

Thomas:5:00
that the trail so they know in the right moment to come into those markets and help them grow. The people that are thinking it happens overnight aren't making the big money. They're just they're the ones who invest in a company and usually they lose their half their money 'cause they're they're like, it's so great. Mm-hmm. But the guys who really made the money got the out while you were getting in.

Ryan:5:17
That's it. They missed it. And so for you then, just just going back to humanistics at this point, because I want to dive back into that story because I find that super interesting. humanistics at its core, who I I know you mentioned that you only work with companies that are really toward the the end of the market, leaders in the industry. what is exactly the core service you guys provide and why did you cho choose that end of the market specifically?

Thomas:5:25
Okay.

Thomas:5:45
Two questions. So first of all, what what what is it we do? So so thirty years ago, I was working with a company in Silicon Valley that was taking a philosophical understanding of how people worked and applying technology to it. One of the things that we don't recognize is when you asked me to be on this podcast, you made a request to me. I accepted your request and we had an agreement. So then I'm going to show up and be here at this time, okay?

Ryan:5:47
Please.

Thomas:6:15
But these agreements are going on all the time, and we don't we don't track them. We don't even observe that they're happening, but they're still the backbone of what makes things happen. So we took a simple methodology that came out of philosophy and applied it a long time ago, but the technology wasn't there, just like with early AI, for us to be able to see enough of what people were doing in a digital way. Because most of the data was either siloed or it wasn't there. So people did a lot.

Manually, hello, could you do this for me? Whatever. And the power of the technology was very limited. Fast forward to today, we can virtually look at unlimited data sets in real time and notice what's going on. And the environment we are working with today is almost everything you do is digital.

And we have access to it because you people give permission easily to others to look at what they're doing. Right? So even the the major systems of corporations are are integrated because we want AI to connect to it, or want people to connect to it. So we have so we have the foundation. And the last thing that happened when COVID is a consequence of COVID, people don't realize. When COVID came, we became more formal in our work environment. So we couldn't see each other 'cause still that informal network of, hey, would you do this? Yes.

We just walked by somebody's desk wasn't there. So we have to send an email, Zoom meetings, whatever. So we now have almost everything in a digital structure. So we can observe it, make sense of it, and see what's going on because of it. So what we do is we are able to look at the existing data structures, the communications of organizations, and notice where things are not going where they're expected to be going. Things are not aligned to strategy, things are not aligned to purpose.

Things are not aligned to each other. This group's doing this, this group's doing this, and they're not coherent. And the consequences are significant. Many, many research organizations have said 50% of the money spent on people in organizations is wasted. 50%. It's a multi-trillion dollar a year problem. It's because we can't see it. We can't fix what we can't see. So we provide the way to see this, look at the consequences of it.

Thomas:8:31
Take actions because of that. So now that's that's the structure that we offer. We offer it as a service. We plug it into applications that are already there that want to add this value to those applications, whether it's a sales force management application or a project management system or whatever, so that these applications can bring the true value. One of the problems, people continue to invest significantly in IT technology, right? But if you look at the ROI on that, two to three percent.

Now, would you s what would would you say that's a good return on your investment? Shouldn't be. You should be getting a much higher return because we didn't weren't solving any new problems. We're simply sort of surrounding the edges. And we're in new territory here, causing these things that we can't ever understand. Corporations consistently have the same problems recurring. We'll go see fix a sales problem two years later, fix a sales problem, two years later, fix a sales problem. It's a cycle. What if we broke that cycle, understood the root cause of this problem?

Ryan:9:06
Of not. No.

Ryan:9:11
Hmm.

Thomas:9:32
And fixed it now. And later we can look solve other problems, but that problem is solved because we can we can observe what's going on, we can fix it become before it becomes a crisis, and not have to go into a tr into a transformational thing to do that. This whole area of transformational technology or change management and the old no pain, no gain, nonsense. We work with what's already there. We integrate new ways to working in what you're already doing, so you don't need to retrain anybody.

It gives you the value that should have been delivered all along.

Ryan:10:07
So that's really interesting about obviously that there's this like interconnected web of communication that you guys are revealing and and understanding the problems and that kind of network. Can you give us an example of let's say a problem you walked into a business, you you you kind of realize that and how did how was it solved from there? Like what's the most common use case you you've typically seen?

Thomas:10:35
It it's it's it's it's across the spectrum. So the an organization just take project management. It's a great example. People think we have it solved today, right? Let's just take a all right. Well, here's the thing. That's what people say. Now the secret, the dirty little secret is how many projects are delivered on time, on budget, and deliver what people want? Not so many. So why are we still having this problem? That's the gap.

Ryan:11:03
That's the gap. Yeah.

Thomas:11:05
So because we can't see, we see these items as tasks, but these tasks are not in relationship to the commitments that are connected to. And the implications across the commitments, not only within the project, but across the corporation. So if your project's late, what are the implications of sales and marketing and production? All these things are impl implied, but we only see it in the project management system. So what if we could see the overall implications and consequences in real time?

Right. Just think about that. So today project management is project management, Salesforce Management, Salesforce Management, and so forth. What if we brought all these together in a way that leadership can see implications and crises and and and and consequences so that they can apply their resources to fix it before it's a crisis? Before we we're late and then we have to go apologize for late and then the competition clobbers us.

Ryan:11:58
How do you approach that? Do you start like where would you typically start? Would you start from the the data, realize the problem and then work backwards? Or what's typically your approach at how you actually enter that diagnosis?

Thomas:12:10
Well it it's it's there is no single approach. It's it's doesn't matter. Whatever w whatever wherever door gets you into where the problem is sitting. So our sales our sales number off, okay, let's go find out why. Our projects are late, let's go find out why. Okay. Our we have high attrition rates, let's go find out why. Right? Our our customer service scores have dropped. Why?

Ryan:12:24
Mm.

Thomas:12:36
People can g people can look at the why, but until you understand the network of commitments that are running here and the communications are it.

Ryan:12:42
It sounds so simple, but then you you you get into the network of commitments behind it and that's the bigger piece, right?

Thomas:12:48
And here's the thing, it was it what it's not logical to see that. Now here's why AI is such a a perfect storm. AI is built as a semantic understanding device. It understands language. Well, all commitments live in language. So it's it happens to be the right technology. At a moment we can apply this methodology that we've been perfecting for 30 plus years to be able to give it a small set of of

Ryan:13:07
Mm-hmm.

Thomas:13:17
proprietary instructions. It can go find what's going on by reading everything, making sense of it in the the the context of the network of commitments that are there. And then understand the implications of what it's discovering across the organization. Because we understand the commitments now, what's the incoherence and coherence of this against what matters to most of the organization, the strategy, goals, etc. So it's able to do both in real time.

Ryan:13:46
And this didn't happen overnight either, of course, right? This has been a a massive work in process. Hmm. The doubling effect. Yeah.

Thomas:13:48
We built this just like we just talked about those those things take time. You have to build the foundations. So we've been building this foundation for thirty plus years in corporations all around the world. We've been doing it one applicate one problem at a time. Now we can take it and steal it.

Ryan:14:05
Yeah. So where are you guys at right now in terms of like the the company, headcount, the team? Like at what scale are you are you currently operating?

Thomas:14:17
So you know it's a great question, I'm gonna answer it before with the story first. So we're we're today, for the first time in the history of of of civilization, have companies of less than fifty employees that are billions of dollars in revenue. First time in history of humanity. Why?

Ryan:14:21
Please.

Ryan:14:38
I instantly think AI, automation, efficiency.

Thomas:14:38
Why do we you know Well that's a that's part of it's that's a that's a part of it. But the people think differently and AI helps them operate differently. You can't take the old way and just make it faster, because that won't work. You have to take you have to create a new way that and that's enabled by AI.

Right? So you need a legal department. Probably not. You could outsource legal. Long as you're keeping track of it, long as you're getting them involved in the right moments. You apply that resource. Stay with your core competencies and you do that. I always I'm gonna give you a a a a story that's blows my mind. We are in the third iteration of our technology. The first was a prototype. We hired a fancy company to do this.

And they said, yes, we're gonna charge you however many thousands of dollars to do that. Took them twelve weeks to do. Okay. Okay, we that they weren't gonna be the right team. So we hired some a group in India. I don't have anything against India, I like the Indian developer, but they have a particular process, and primarily it's very people-centric because they have low labor costs. They throw more bodies at it, so they can give you this thing for lower cost. they're gonna produce something for hundreds of thousands of dollars for us.

And take nine months to a year. We got another road that wasn't gonna work. So we then brought in a brilliant data scientist. He he personally developed using quad code, AI tools and so forth, the whole system in eight weeks that was gonna take one year and quarter million dollars. Yeah. Eight weeks. What? Yeah. So imagine that implication on everything we're doing.

Ryan:16:14
Eight weeks.

What's

Thomas:16:24
That then that's that's now we're hiring other people 'cause we but we we went from having not a system, but being told it's gonna take us a year, to having a system eight weeks later.

Ryan:16:33
So what do you attribute that most to? Is it finding the right talent? Is it particular areas of the market, making it a competitive offer? Or or how did you how did you shorten that cycle so much and how is that repeatable?

Thomas:16:45
That's a that's the the right question. So so the person that we brought in was not formally trained to develop software. So did they didn't have the old habits. The problem is people think I'm just going to speed up what we do. That's not the answer. That's you can do that. But what's the answer is how can I do this differently to produce a better outcome?

Ryan:16:56
Hm.

Yeah.

Thomas:17:14
So back to my example here, our not only did this person develop our system in eight weeks, while he was doing it, he also built a software development system for us to build other applications with at the same time. See, we these people don't recognize so you need somebody who thinks differently. So he can start up on on Friday night, when he checks out, he sets up multiple programming agents to work all weekend programming.

Right? It comes in it comes in on Monday morning and then the work's all done. And he reviews it now it's gotta be reviewed because we're not we're not stupid here. But you can you can put a whole workforce together working working over time without complaints, that's going to do but you have to think that way. I'm gonna do this here and this. You can't think I need to get the does of this other person. You can't think, we need to do it this old way. No, because those none of that is going to give you the benefits.

Ryan:17:44
he chills and drinks a margarita. Yeah.

Thomas:18:13
That's why we're here in business ourselves. So we're an example of this, right? Because we were clear about what we needed. We were clear about our process of the of our what our intended results were, and we looked for a new way to do this. Now we're building applications for other people using our technology. They can take that same value to their clients and their domains. We you talked about our business model. Our business model is to work with organizations who already have existing networks of trust with customers.

It takes a long time to build networks of trust. People don't realize we're what's what we're doing. We build customers up. We're doing one interaction at a time, building trust, trust, trust. It can't be done quickly. It can be done quickly enough, but not that quickly. Now, they've got the customers. We give them the capability to significantly increase the value they're giving to those customers without changing the basic structure they're already operating with. They don't have to re-plumb it.

Ryan:18:43
That's key. Yeah.

Ryan:19:09
So that's

Thomas:19:10
They don't have to tear it apart. They just add something new to it.

Ryan:19:12
Yeah. I mean it's a huge gear of leverage when you're you're offering that service to be able to come in and you're not gonna blow apart the business, you're not you know bringing new crazy ideas, you you're really just you know finding the the the best way to maximize efficiencies and and accuracy throughout their whole process. Do do you have like a specific way you guys like how does your pricing model work? Is it like contingent on what that person needs, the business needs, or is it flat?

Thomas:19:39
So really so

Thomas:19:43
So it's i it's it's it's not flat. So we our pricing model is that we get paid a fee for development that's basically our labor cost, and we get a revenue share on the backside.

Ryan:19:59
Amen.

Thomas:20:00
Alright, so if I'm giving, let's just say I I gave something to Atlassian who's got sales who's got job, I don't I'm not saying I'm doing that, but let's just use an example. So they add a capability that increases their revenue by twenty percent. I get a piece of that. So I provide them a capability that they didn't have to develop. I'm continuously evolving that capability so the c the the the capacity the customer has is increasing all the time. But we're in partnership together.

Ryan:20:17
Yeah.

Thomas:20:28
We're in partnership, those people are leading markets, giving them capabilities they they don't have. For even somebody like like Atlasian could expand, not just being in project management, but corporate management. Because we connect to all the data sources in the organization and give you that new expanded view. So everyone has new capabilities that were not there before. So our I said our model is is is is a very fair price that we're not making any profit on. We're taking the risk and getting it developed. And then we share in the revenue upside.

Ryan:20:33
Yeah.

Ryan:20:59
great model and it's a partnership isn't it? How how did you come has that always been the case or like how did you decide that was the right model for for this?

Thomas:21:00
It is. Well not always.

Thomas:21:10
Because I have a long time of things that didn't work. I mean, again, these things we do one at a time, right? Well, I have I've had I've had models like this, not as as well organized as this one. I've had models that are just customer venture models. Those are the hardest ones because you know that that that model says, What have you done for me today? Well, somebody else comes along. It's hard to uproot somebody's in a partnership this way.

Ryan:21:13
No.

Thomas:21:39
Yeah, because we're to g we're we're ev every day we're working together to make the what they're offering better.

It's in my best interest, it's our best interest as a company that what they're doing will be better tomorrow, always. So our our we're a hundred percent aligned about the outcome, the overall outcome, not just the product itself.

Ryan:21:52
Yes.

Ryan:22:02
Think that's really unique in just the space of business entirely, where you know a lot of it is structured. you know, we are the business providing you the service, you are our client. This sounds like much more of a you know coherent model, which which really invites trust. It's obviously a relationship, it's a real partnership. One of the things we love to talk about on the show, Thomas, is client acquisition. And you know, that there's typically no better client who comes in in our experience and with the guests who've come on this show, it always stems back to relationships, building that real relationship first. How how do you guys typically typically get your clients? Like what is that approach? What what does that process look like?

Thomas:22:46
You know, it as as you know, there's nothing better than a happy customer. You know, word of mouth is still the particularly in a partnership structure, it's a B2B environment anyway, and B2B primarily runs with with with word of mouth. You can you can certainly do advertising, you can do podcasts like this, and some people will find us that way. But most people find us because somebody we work with says, hey, you should give them a call. Hey, they're doing something for us that's really amazing.

Ryan:22:54
Happy customer, happy life.

Thomas:23:17
People we've worked with before, they say, what are you doing now? Okay, that's what let's go find out what you're doing today. So it it it's really based upon the more trust that we gain, the better, the easier it is for those people to say, trust them too, because I do. Now when I say that to their friends and colleagues, we already start out with a deposit in our trust account of that person. Right? So we're not w when the problem is challenges with advertising or social media is.

You start zero. Unless you're a big brand and then you have some assessment about your trustworthiness, but you start at zero. We don't start at zero ever. We always show up when somebody's has has has introduced it to somebody else. So these guys are amazing. So we start above zero. And then we can begin the process, accelerate it, and we start showing them. And we do it really quickly. We can show them a prototype of what will impact their business in days. They're like, my God. Yeah. We can do that.

Ryan:23:51
Social proof. Yeah.

Mm.

Thomas:24:15
And then we so we continue to take that and they can get in the market in two, three months with something that would take them two, three years historically. So we're always proving our value, because that's what partners do. We're not laying back and saying, we're partners now, it's cool. No, we're every day proving our value because it keeps us sharp. It keeps us in alignment with what matters most to all of us.

Ryan:24:30
Yeah.

Ryan:24:38
Yeah. That's such a good point. And alignment is so key. It sounds like it's not only in the way that you guys structure your service, getting everything in alignment, but also how you you approach actually getting new business and that relationship, that partnership. In in terms of how that actually looks in in the process from you having a an individual come your way who's really interested in your services, they already have that level of trust that exists.

Is it yourself who typically gets on a consultation with them and walks them through kind of how how you would implement in their space? Do you do you approach it as more of a diagnosis diagnosis session? Because these are all quite complex ideas for somebody to understand, like coming in brand new to this space. what does that look like? Is it mainly yourself and and how do you structure the process from that?

Thomas:25:35
Very good question. And this is another example of something we've developed, which is we we have a conversation. A transcript, we discovered something fascinating about transcripts. Now people look at it like a either a summary, which you get of that, or they look at transcript as a you know verbatim thing, but there's something else that you can use a transcript for.

Can use a transcript to have AI give you an understanding of what was meant by what was said. And you know the old saying the music is actually between the notes. When you and I are having a conversation, there's more going on than just the words because each of us are if I really listen through the lens of what can I understand here that I might miss? The subtleties of the conversation. Well, AI helps us not only get the meat, but also the things that we can't see, and we create a

Ryan:26:15
Right. The meaning behind it.

Mm.

Ryan:26:24
Yeah.

Thomas:26:33
Composite of what the need is from that. And we can quickly then build in a day or two, build a prototype of what will satisfy that composite. And take it back and show them. So what talking about build trust, I've listened to you and I come back to something for you that's like you may make a few changes, but like 95%, they got it. Well, how often do you experience that in life? Not so much.

Ryan:26:56
No, you're showing before you're telling.

Thomas:26:59
It's just totally just delivering on our front. We're delivering what we are to you before you spend a dime.

Ryan:27:08
And that is the structure of the whole relationship. And I again I I do want to just briefly go back to obviously we we spoke about how you guys have spent 35 years. Well, what I have here is 35 years behind this business that is two two three three years, three years in the making, two years in the making. Silicon Valley, a lot of

Thomas:27:30
Two years.

Ryan:27:38
trial, failure, as you mentioned. Out of all those years, what do you attribute? And I know this is a hard question. What do you attribute all of those years of research actually putting you best in the position to be able to do in today's day and age with AI where it is? What's the one lesson or or skill that you learned that has really compounded you being able to, you know, be where you are in business today?

Thomas:28:06
know, it's really simple and hasn't changed. Is it that to be curious about what you don't know? Particularly don't think that you've got the answer. Think, I'm always gonna be looking for something that doesn't make sense. When I find out I'm gonna be curious about what is it. If you look at every wildly successful person, deep curiosity is at the root you know that. So then that's the same here. So how when we started our AI adventure And where we are today is very different. If we started with where we were, with what we knew, and we began we originally were going to begin into starting a consulting business just to take my partner's consulting business of helping companies and do what most people do with AI, have it be faster. Have it do have need people need to be less trained because the system has the knowledge in it. We quickly weren't learned that that wasn't the game plan for this. There's something more here.

Ryan:28:57
So w when was that? When d when was that shift that you realised there was more? Just three years ago.

Thomas:29:00
Two years ago, just two along along this two No, that's the the two years ago we started with what I just said, and we said, What's the impact of AI? We had the same preconditioned everybody's guy. it's gonna be faster. True. But that wasn't the point. It as as we started working with it, we started seeing things that we'd never seen before because we could see a broad array of of what was going on in an organization and start seeing signs that we weren't able to see. It's like, what does that mean?

And the whole understanding of coherence and its value in this arose. not only are we paying attention to this network of commitments people are in, the coherence of those commitments to the things that matter. And being able to look at the signals that let us know early when things are drifting. If people go to a meeting and they agree that they're going to go somewhere, but they never say, How's this aligned to our strategy? Who asked that question in a meeting? Nobody.

But what if your system is what what what if the system came back and said, by the way, this isn't aligned to your strategy. You may want to change your strategy, go do that if you want, but realize this is this the right thing to do now? 'Cause we have so many this is efforts that are in corporations and organizations of all types that we get dead ends with. We try it, drop it. Imagine what the cost is to morale for all the things people start that never count for anything valuable to them to them or to the organization. They feel I don't do anything as

Ryan:29:58
Be a weird question to ask, naturally. Yeah. Hmm.

Thomas:30:28
Here it matters. Well then their self esteem drops, their sense of meaning and purpose drops. Not useful for the kind of organization that you I you or I would want to build.

Ryan:30:38
Yeah. It's something that is so often uncalculated and not thought about.

Thomas:30:42
Totally. It lives in in the place of the unseen.

Ryan:30:46
Yes, the unseen. How does AI actually see past this? Like I'm I'm trying to put it together in my head and I'm sure the audience is trying to think about this, you know, because AI is a new concept for all of us. How would you describe it to someone who's more of a visual learner? How AI actually in like sees this network of promises?

Thomas:31:07
So t two things. First of all, let's think it has to do with the context you give it. So the context might be I'm gonna focus on a project. So you give it all the project plan information, including the Slack channel comments, including emails about it. All those are contexts. So you gather the context, and then you provide the methodology of these are commitments, this is what commitments look like. what coherence looks like, and this is what you're gonna be measuring, and you package all that into a bundle and you send it off to the to the LLN, along with the specific question you've got. I don't know about Dreft, I know about so then it takes all of that package and makes sense of it and feeds it back into our system to then be delivered to a customer through a report, through a alert, whatever that happens to be. You can have things that are scheduled to do that every day do this. Why today do that?

Or I'm just I'm just looking something that's discrete. Whatever it is, it's all about context with methodology with the prompt that gives you an answer. It's a s anytime you sit to A AI, it's taking context and a prompt giving an answer. We add methodology to it to have it be specific to the area of concern that we have.

Ryan:32:26
So interesting. It's obviously this is something that is is there anyone else doing this like you guys are doing it right now?

Thomas:32:35
Not to my knowledge.

Ryan:32:37
Not to your knowledge. Why do you feel that is?

Thomas:32:39
Well, so this whole understanding of commitments and organizations, I've been working with it a long time, I licensed technology a long time ago. But it never got what people call stickiness. And the reason I think is the things we said earlier in this conversation, because data was distributed, our digital footprint wasn't big enough. Yeah. And we we were we were more informal in our way that we did work. When those things changed, which is recent.

Ryan:32:52
Mm-hmm.

Thomas:33:09
Then nobody took COVID and one of it. Just just AI, one of the things that AI is doing is making people choose, or people are choosing, to allow AI to access almost everything they've got. But there isn't hardly anything that exists that can't be connected to your personal AI. So therefore the world became accessible. So

Ryan:33:10
And COVID was the main thing you attribute to that? One. Hmm.

Thomas:33:38
We could apply this methodology, which we were doing for a long time, but before it had to be used in a bespoke manner. I'm going to solve this one problem for you. What if I could solve a much bigger problem for you with no effort? Using your data. Couldn't do it before. So that's one of the reasons people haven't done it, is because it wasn't possible. And we just happened to show up at a particular day in time, two years and a month ago, and say, let's let's look at this, devote ourselves to doing that.

Learn what we learned along the way about doing things differently, and came up with something that is now the foundation for the business.

Ryan:34:13
I mean it it really sounds like the perfect storm. You know, when opportunity meets preparation, that that really sounds like things just aligned at the perfect moment. All that research, all all of the experiences and mindset.

Thomas:34:17
I think so.

Thomas:34:26
Kind of experiences, yeah. So you know, you know, how often is it that somebody hears about a new actor new to them actor and they claim the overnight success, but they've been working twelve years, fifteen years, twenty years, nobody knew about them, all of a sudden they're an overnight success. No, they were just in their moments, showed up and people got to see them, but they've been doing their work every day. Doing whatever it took to get to that place where they could be discovered, they could be in the right place at the right time.

Ryan:34:50
Yeah. It's just breaking through that threshold. But people don't see the the amount of force that's required to actually get to that point. So cool. Thomas, I I want to end looking forward. I know the business is obviously you're two and a half years into it. things are really lining up nicely at at this day and age. Over the next 12 months, what is the one thing

Thomas:34:55
Precisely. That's it. That's it.

Thomas:35:02
Okay, good.

Ryan:35:17
you guys are really looking to accomplish. Wh where is your effort going mainly in this journey?

Thomas:35:24
is it's it's simple. We can we're gonna work with the leading companies to bring greater value to their products. And so we're we're just gonna do keep opening doors, keep showing people what it is, keep building use cases so those companies can see the value. So we're just we're our heads are down, we're we're focusing, we're growing our teams so we can do more of this work. we're gonna expand our business development activities so we can talk to more people. The thing you our processes now can all be activated through AI. So I can bring somebody onto the team to do biz dev that doesn't need my knowledge about what the system does because the system takes care of that. They just need to know how to have a good biz dev conversation, to understand what really the customer wants, what they care about, so we can bring what we have to them so that we can show them that we can help them accomplish that.

Ryan:36:12
Absolutely. It's such a a refreshing mindset to have in a day of you know AI when everybody is thinking this tool is the next answer, this one is gonna do it. And you know, it's people forget about the people, people forget about the people behind what that AI is actually doing. And there's so much leverage, so much efficiency to be made. it's honestly exciting. I I'm excited just from this conversation. Yeah.

Thomas:36:35
Well you know, just one one side sidebar about that. Ford Motor Company laid off s a large number of their engineers because AI is going to replace them. Guess what they did? Hired them all back. All of it come back back. Because we made these quick quick leaps, but AI isn't you or me. You know, here's the one thing that AI isn't. It can never experience anything. It only knows the data about it. You and I are experiencing beings. We humans are different than

Ryan:36:47
Yeah. Too fast.

Mm-hmm.

Ryan:36:59
Mm-hmm.

Ryan:37:03
Yeah.

Thomas:37:04
Machines and that difference far more important than we realize.

Ryan:37:10
so true. I feel like that gives me confidence as well. You know, in in this crazy, crazy day and age where things are moving a thousand miles an hour. it's you know we we got to remember how special we are as humans and and what ai, even though how great and amazing it is, what it can't do, what it can't see, and remembering how unique we all are.

Thomas:37:14
Yeah.

Thomas:37:32
It's here to augment humans, not replace them. Yeah. Humanistics. So that's 'cause we don't forget that part. Ryan, it's gr great being with you.

Ryan:37:35
Exactly. Yeah, that's brilliant.

There you go. The human part. Mate, I had a blast. I you opened my eyes so much. I know the audience is is also going to have their mind opened, blown. I don't know how you want to phrase it, but I think a lot of people need to see this in business. and mate, really, really insightful audience. Thank you so much for watching. Until next time.

Thomas:37:54
That was good.

Thomas:38:01
You're welcome. Yeah, you my pleasure. Yeah. Yep.