1788 – Integrating AI in your operations with Art Powell of Trinsic Technologies,Inc.

Josh (00:00:05) - Hey there, thoughtful listener. Would you like consistent and predictable sales activity with no spam and no ads? I'll teach you step by step how to do this, particularly if you're an agency owner, consultant, coach, or B2B service provider. What I teach has worked for me for more than 15 years and has helped me create more than $10 million in revenue. Just head to up my influence.com and watch my free class on how to create endless high ticket sales appointments. You can even chat with me live and I'll see and reply to your messages. Also, don't forget the thoughtful entrepreneur is always looking for guests. Go to up my influence. Com and click on podcast. We'd love to have you. With us right now, it's Art Powell. Art. You are the founder of Intrinsic Technologies. You're found on the web at Telecom Art. It's great to have you.

Art (00:01:10) - I'm honored to be here. Thank you for having me. Well, it's.

Josh (00:01:12) - Going to be a good conversation as well. Tell me about the work that you do with Trent.

Josh (00:01:16) - And get to our friend that's listening to us right now. You stumbled on the right episode because we're going to be talking about stuff that's impacting all businesses right now, but maybe just kind of kick us off with, you know, kind of your impact in the world.

Art (00:01:30) - Yeah. So we're a technology services company and we've been in business for 18 years. And the whole thing is, is throughout my entire career, it's always been about implementing technology into I started into technology in the early 90s when it was really beginning to get into corporations. And the thing is, you couldn't just take a computer and just drop it into a company and say, we're integrated, right? Because everything was still a lot of processes were still manual. So you had to figure out how you can get the data into the computer, that how you were going to get the data out and the we're going to have these same challenges with AI. And this is one of the things my company is really beginning to focus on, and myself personally, is how do we integrate AI into existing companies? Because there are going to be caveats here.

Art (00:02:14) - You cannot take AI and just dump it into your operations and go, we're integrated. That's not how it's going to work, you know? And there are still limitations on AI. So the things companies need to be very wary of is just allowing employees to go rogue. With AI. They can cause problems. There's a lawyer right now, I believe it's California who's going up on a bar review because he submitted a paper that was created by AI and it made up presence for to support his case. Um, yeah. They did not like that. So he is getting reviewed for that. I don't think they're going to bar him, but I think he is going to get punished for it. So this shows you where the dangers of AI as Harvard recently just did a study where they were watching and this is recent since ChatGPT came out and they did a study to show how much efficiency employees were actually gaining. And what was interesting is that employees where AI works well, they were getting significant somewhere between 10 to 15, 20% efficiency.

Art (00:03:17) - But where I didn't work well, they were actually going the opposite direction. They were actually getting 15 to 30% inefficiency. And again, this is why it's going to be so important for companies to be very strategic and tactical about how they're implementing AI into their operations, because with the hallucinations and the other issues that it has, you know, areas where you like, are you going to put AI in your accounting when it hallucinates? And when we say hallucinations with AI, what we have to be really careful about and understand is that we say it's making stuff up, and we act like as if the AI is making stuff up and it knows it's doing that. And that's the problem with the hallucination. It's like with, you know, I'm a salesman, right? So people always tell us your master biases, right? Like, but they know like they're saying that because what they're saying is we know when we're making stuff up, right? Or we're expanding the boundaries a little bit, but that happens, right? What the problem with AI is it doesn't know what's wrong.

Art (00:04:17) - It has no clue it's wrong. Right. Uh, you could argue it doesn't even know what that means. Whereas with human beings, we either know we're wrong or we can discover that we're wrong and then correct it. And the problem with AI is it doesn't know that. And so that is going to be some significant challenges for operations. Yeah. The way they implement this and the way they bring it forward.

Josh (00:04:39) - Yeah. So in the companies that you work with where would you say attitudes are? We're recording this toward the very tail end of 2023. Where would you say most leaders attitudes are around AI? Is it skepticism. Is it concern? Is it you know, is is it passionate? We need to we need to be, you know, getting AI everywhere or is it somewhere in the in the middle, I would imagine.

Art (00:05:05) - I think it's across the spectrum. I think the biggest thing that's driving right now is FOMO, Fear of Missing Out. That is a very, very big one.

Art (00:05:12) - And I think companies need to be really careful with this. Um, I think they need to take a step back. And again, like I said, be very strategic. There are obviously there's early implementers who want it immediately because, you know, they want the they want the potential strategic and efficiencies advantage over the competitors. But again, if you implement it incorrectly or implement it in the wrong way, it could actually backfire on you. Right? So I think but FOMO is probably the biggest driving thing right now, that when I talk to business leaders and CEOs and as they are really concerned, I'm meeting with a one of our clients, you know, next week. He's like, how I got to use AI. And we're like, whoa, whoa, whoa, slow down. Right. Like, what is the problem you're trying to solve? Right? Yeah. That's the key is people need to identify, do I have a problem that I think I can? Solve that technology presently cannot solve.

Art (00:05:59) - And then you need to really look at can the AI in its present state really solve it? AI is going to look AI and there skeptics, right? I tell the skeptics like this is like the same. You can't just be a lot of AI and stick your head in the sand on this either. There's two extremes to this, right? There's the extreme where it's like, oh, like this is nothing. Okay, well, no, that's wrong. And then there's the other extreme where it's like, AI is going to take over the world and destroy humanity, right. So I think that both sides are based on fear. Right. And I think you can't really make you don't want to make decisions based on fear. You need to get informed. You need to see where AI's already making progress. And if it fits your organizations, integrate it because it is going to give you, uh, if your competitors are using it and you're not in these areas where it is doing well, like with content creation and things of that nature, where it's making those employees much more efficient, then your competition is going to have a competitive edge.

Art (00:06:54) - So I think it needs to be, you know, again, the range of emotions is across the board. It is the two extremes that are tend to be problematic. But for those who will take a rational, very careful approach to this and look at the their technology stack and their operations and where it fit, they can gain great advantages. So I'm not one of these people that says like, AI is not going to make a difference. But listen, I'm not going to try to age you, but you probably remember some of the talk in the 80s and 90s with computers in the internet. I remember distinctly people were like, oh, it's going to overthrow all the dictators. It's going to.

Josh (00:07:30) - Wargames, man.

Art (00:07:31) - Yeah, that's right. It's going to make everything cheaper and cheaper and cheaper. There's going to be no more poverty. It's going to eliminate racism and inequality. Yeah. How did that work out? Right. So I think.

Josh (00:07:42) - We're finding our way our we're finding our way.

Art (00:07:46) - Because the problem with this is that everyone has forgotten the human element of all this. Human beings are still at the core of this, of our world. And that's not going to change with AI. What if you build something that is completely AI operated, say, a restaurant, and nobody shows up? If people won't spend their money there, it doesn't matter what you do. You know, I remember distinctly that Amazon was going to kill every retail outlet store in the entire world, which it did not. And by the way, what is one of the fastest growing commercial real estate markets? It is these outdoor malls. They're malls. I'm sorry. They're just outdoor malls. We have one. It's called the Domain. It's here in Austin. They're building one twice the size in Cedar Park and Leander over the next 5 to 7 years. So this this shows you that people still want to go shop. They still want to interact with human beings. Right. And I go out there on the domain, and it's not a bunch of 50 year olds like me.

Art (00:08:37) - It is a bunch of young Gen Zs and young millennials who are out there shopping. So this whole idea that AI is going to completely replace human beings, I think you have to keep the human element inside of all this. When you're making decisions on how you integrate AI into your operations.

Speaker 3 (00:08:55) - Yeah.

Josh (00:08:55) - And, you know, and I think that should be said to, you know, for folks that are apprehensive or they're just I don't know what their where their emotions may be, but you're not competing with AI, you're competing with other humans that are using AI.

Art (00:09:11) - Great point. And that's exactly right. And that's why I said you can't be alloted AI and you can't stick your head in the sand on this. It's just like the it's just like the it's the retail companies who didn't adapt who but the like Ulta, who uses the internet to suck my wife in there and spend way more money than she ever should be spending on a regular basis.

Speaker 4 (00:09:31) - Yeah.

Art (00:09:32) - Um, anyway, but the whole thing is, is they're so effective on their internet marketing.

Art (00:09:36) - Right. And that's the thing we need to understand. It was the retail companies. JCPenney had an online catalog in the 1990s of every single part that they and product that they sold. They did nothing with it, right? They didn't use it to further it. So this is going to be the same thing. It's the companies that it's not that AI is going to kill companies. It's that companies who don't use AI to adapt and to grow. They are the companies that are going to go away, just like the retail companies in the 1890s went away, that didn't use the internet and technology to adapt and to grow with it. And so that that's what people need to remember.

Josh (00:10:11) - Uh, tell me about one of your solutions through forensic tech is called Avos. And there's a separate website that you have just for this, a h v o s. Yeah. What's avos?

Art (00:10:23) - Avos is exciting. So, um, in sales, we have a saying it's not what you know. It's who you know.

Art (00:10:29) - One of the inventors of that product is a good friend of mine. And so he showed me the product six years ago. It's an AI itself. It's not. And it's something that's built on AI. It's its own AI. It was really exciting when he showed it to me six years ago, but it was very rough. And I remember distinctly telling him, if you pull this off, you're going to radically change things. If I'm being completely honest and I've told him this, I did not think he would pull it off. Uh, he did, uh, he came to me recently and and showed me the work. Order. Basically, in a nutshell, most AIS that we are currently dealing with use of what we call a large language model to train the AI. It uses a it uses basically a poor man's model of the the way our brain works. Okay. And then they train it with these large language models to train it. Okay. What he has done is he's developed an AI that does not use large language models.

Art (00:11:21) - And the implication of that is like, for instance, companies can have their own AI that's dedicated to certain problems and can be hosted on like $150 a month server. It doesn't need all the power requirements. Now, it's not an AGI. It's not a complete you know, it's not it doesn't do it doesn't do everything right. It's used to solve very specific problems, but it does them extraordinarily efficient. And the best example I can give is one of the benchmarks that they use is these impressions, these reviews, product reviews. Like they know if these impressions are good or not. And they use and they train AI on them. And then they compare with these known record sets so they know how accurate the AI is. So the leading AI engines using the large language models, took 200,000 records of these known impressions and trained them on it. It took about I think it was like, uh, I don't know, it was like 3 or 4 days to train on them. Then when they were done, they ran another 200,000 records to see if the AI can determine if these were positive or negative reviews.

Art (00:12:24) - The AI took four hours and got a 60% accuracy rating. My friend's product took 20,000 records, trained them on a brand new engine that had never been trained before. It took about two minutes to train it. He then ran it the 200,000 records in 1.8 seconds, and he got a 90% accuracy rating out of the gate. And so his product is significant because it's much more narrowly focused on the things that does, but it does them without all of the the resource requirements and the large language training that you have to do in other products. His product. Also, if you want to, let's say you have a specific problem and you want to add some more features to that problem it can solve, you can train it while it's being used. You don't have to retrain the whole thing from scratch like they do with the large language models. So he has a very distinct advantage over some of the current models because again, you know, Tatp is going to be great for chatbots and content creation, things like that.

Art (00:13:23) - But when you're getting to data sets and when you're getting to, uh, very specific problems that need to be solved, you know, it gets very expensive to use ChatGPT or Bard or the other ones. His product is going to be very inexpensive to do that. It will be very, very focused. And it'll be very, very good at what it does. Very exciting stuff. We're excited about it.

Josh (00:13:44) - What does engagement with transit typically look like? So, you know, who are you working with. And like are you just are you consulting around the AI strategy or.

Art (00:13:53) - Yeah, we're we're a consulting first company. We're a technology services company, but we're consulting first. So we always approach all of our relationships from the very beginning, from a consulting standpoint, because we are very strategic and tactical, and the way we adapt technology is our whole philosophy. It's about it's one of our values, which is empowering our clients on their journey to success. We and the only way you can empower people is if you really know, like where they're headed and what they're doing.

Art (00:14:17) - So every client we engage at a consultant level, part of that consultation that we do now is looking at AI and where it can potentially fit. We're in the process of 3 or 4 companies right now beginning to implement AI. We want to do this slowly and carefully. We're not one of these people who like to just rush into things. We want to get it in there, test it, make sure the employees are going to use it, make sure it's effective, make sure it's cost effective. Is the amount of money I'm spending worth, you know, the worth, the payback I'm getting on efficiency, right? Cost of ownership, all these things. So we look at this very holistically. We don't just have this, oh, you gotta have AI. It's like the cloud. We do the same thing with the cloud. We consult people like, okay, let's put these pieces in the cloud. It makes sense. Maybe these pieces don't. And that's why we've been so successful at cloud.

Art (00:15:01) - And we built our own cloud because we're insane. We didn't like we decided not to use the big three. We actually built our own cloud and for our company our size that that's pretty impressive, actually. And that's that side of our fence is growing because, uh, we deal with a lot of the SMB medium size market, you know, the, the, the 40 to like 500 employees. And they like that hands very hands on touch, very, uh, consultation oriented. So if people are interested in AI, that's how we we're like, okay, let's back up. What are you trying to what's the problem you're trying to solve? How are you trying to solve it? You know, and can I actually solve it?

Josh (00:15:36) - Yeah. Um, so what would be an example of a of a company you've worked with and what was the challenge and what what where did you bring them to?

Art (00:15:44) - Yeah. So we're in the middle of like, for instance, we have a call center that is one of our clients.

Art (00:15:50) - And what they want to do, they have some AI products that reviews the calls, but it does it. It takes time to do it. And they may not get a warning like that. This call needs to be reviewed until several days, right? What we're doing is we're setting this up so that it will give almost an almost live feedback. So in other words, shortly after the call is made, they will know like we need to look at this call. It could be a problem. So we're really shrinking the amount of time that that it takes to know that there's a problem with a call. The other thing that we can we're working with as a laboratory company, they have a huge data set. They do DNA, they do drug testing, and they track it by DNA. And so they think there's a lot of correlations there that they can't see. They have massive amount of data. And we're setting this AI engine up to begin to look at and pull correlations. So these are the type we've got a couple of others.

Art (00:16:47) - But these are the type of things we're looking very specific problems that we feel the AI can do way more efficiently than the large language models, and do it at a much lower cost.

Josh (00:16:58) - Uh, Art Powell again, your role with the company or the founder and, uh, you've been in business now. Forget it. Nearly next month, 20 years. So congratulations on your longevity. You're doing something right.

Art (00:17:13) - Thank you. We've always been relationship driven. When I started this company, I actually have a sales background. And my wife said she made the. I actually owned an insurance company. My wife said she made the worst mistake in the world. She bought me a computer. I got hooked all into it. And then she said she made the second worst mistake she made in her life. She said, you need to choose between the insurance agency and computers. I sold the insurance agency, got into computers, never looked back. Uh, I love technology, but I have a human perspective on technology.

Art (00:17:40) - We have a saying we one of our taglines is technology built by humans? For humans, technology is here to serve humans, not the other way around. And sometimes when you hear them talk about AI, they talk like as if the AI. We're here to serve the AI and we're like, no. First of all, we think that trying to replace huge swaths of your employees with AI is is morally wrong. You know, we just have a problem with that. We think they are. And, look, we're about to head into a massive labor shortage, uh, because we are about to depopulate. China is going to go from, uh, 1.4 billion people to 850 million people by the year 2050. Look it up. It's stunning. India is now the most populated, uh, country in the world, not China. It's. India has already topped China out. You know, the only reason the U. Not the largest growing segment population in the United States of America is dinks. Dual income.

Art (00:18:29) - No kids. We. The only reason the United States is even slightly growing in population is because of immigration. We are shrinking our population so fast that that's not even going to be able to keep up. In 4 or 5 years. You're going to need AI to keep your A and B employees. If they can't do their job efficiently and quickly and do it well and not be stressed out, you're going to lose them because your competitors will use AI to do that. And so this is going to become very, very important as time goes by that you that people are using AI to keep those key employees because the, you know, you look we basically went through a recession, a slight recession this year and we did it with low unemployment, and they're talking a real recession next year. But I don't think unemployment is going to go above, you know, six, 7%. You and I both know in our day recessions were like 12, 15% unemployment. So this labor shortage is real. It's going to be persistent.

Art (00:19:22) - And you're going to need to have the type of work environment that's going to attract the real talent.

Josh (00:19:27) - Wow. Your website Trinity Telecom t r I n s I c Telecom. When somebody goes there what would you recommend they do? I know you have some pretty good resources on your site here.

Art (00:19:40) - Yeah. And they can link on if they look in any of our services there's a form they can fill out. If they want more information. They can also email sales at Transit Tech Comm. If they're interested, they can follow me on LinkedIn. It's, uh, art patriotic. That's simple. So they can follow me on LinkedIn. I do post blogs there regularly about AI and where I think AI is headed and what's going to happen. I'm just about to post one. It's called the AI Revolution is not here yet, so they can look for that. And then I will be posting in the near future that we are going to be starting a podcast to talk about technology and AI integration, based on the lessons I learned, and to try to help predict like what is going to be on.

Art (00:20:16) - But we'll be we'll be announcing that in our website, in the podcast.

Josh (00:20:20) - Exciting. All right. Again, Art Powell, founder of Trends Technologies. Again, the website Forensic Tech. Com Art, thank you so much for the conversation.

Art (00:20:29) - Thank you, Josh, for having me. It was great.

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