AI TODAY (Aired 02-26-2025) AI Literacy for Leaders: Mastering AI in Business and Tech

[00:00:29] Speaker A: Welcome to this special collaboration between power CEOs, the truth behind the business and AI today, where we explore the evolving role of artificial intelligence in business and technology. I'm Jen Goday and I'm here with Dr. Alan Badot. We are your hosts who are diving deep into AI literacy. What every executive, entrepreneur and technical professional needs to know to leverage AI effectively. Dr. Baudot is a leading AI expert. He's breaking down the realities of AI from business strategy perspective to technical foundations. And we're going to talk about how AI is changing the way we operate, innovate and compete. But there's still a lot of confusion and misconception about what it can and cannot do. Over the next four segments, buckle up. We're going to have a frank conversation about AI literacy, covering everything from strategic, strategic AI decision making in business and implementation challenges to the technical aspects and infrastructure security. All the things that we have to think about that usually we don't like to consider. Whether you're a business leader looking to integrate AI or a technical professional developing AI solutions, this deep dive is going to give you the knowledge to navigate the AI landscape with confidence. Let's get started. Dr. Badot. AI. It's always seen as either this magic bullet or an overhyped trend. At least that's what I'm seeing. What are some of the biggest misconceptions you've encountered from business leaders with regards to AI recently? [00:02:04] Speaker B: Well, I would say, Jen. Well, first, it's great to be here, excited about doing this show, but really it's around just that there's an easy button for these things and whether it's deploying it, whether it's using it, whether it's taking your data and integrating it. You know, everybody thinks that it's easy and they underestimate the amount of time and knowledge that you need to be able to do it. I mean, that's fundamentally at the heart of everything else, whether it's training, whether it's the it, whether it's the software that really is the big driver. [00:02:35] Speaker A: Yeah, I agree. And a lot of times I hear about the magic bullet. They think a lot of executives think we're going to put this in and all humans are not going to need to touch this. But the reality is we always need a human in the loop. So talk to me a little bit about what it means for executives and entrepreneurs to become AI literates so that they understand exactly what it looks like. Once we have that magic bullet button, that easy button, like what, what do we need to know what is AI literacy and why is it critical in business today? [00:03:07] Speaker B: Yeah, and I would say the first thing that people think when we say AI literate, they think, oh, I need to understand how to program and I need to do all those, all those other things. And that's not it at all, has nothing to do with that. AI literate just means that you understand the nuances, the changes you understand, you know, the, the ramifications of doing something too quick. Like we always talk about, you get fomo, we're gonna, you know, we're gonna, we're gonna miss out, so we got to do something, but we don't know what, but we're gonna do it. And you know, it's, it's, it's those sorts of things that you just basically you have to understand how to, how to take advantage of it. And that's really, you know, at the, at the heart of it. And, and I know you see the same sort of thing all the time when, you know, your, your folks come to you and they're asking you about, you know, these, you know, how can we put it in, in our business? What do you see on the, on, you know, at the executive level where, you know, they're starting to assess AI, but they're really having time finding a fit for it. [00:04:13] Speaker A: Yeah. So before we, we find a fit, like, I think there's, there's a couple of components to AI literacy. Right. There's the conceptual understanding, what is AI, how does AI work and how it impacts our industries in general. And then the next component is kind of the practical application, which is how, how do, how does AI or the tools, how do they integrate into our business strategies and workflows? Like what, what does that actually mean and look like? And then I think the third aspect of AI literacy and something that comes up specifically in professional and technical industries is the ethical component. Because we're bound by regulation. I'm thinking like financial, legal, medical, we have, we have extra regulations that some of the non traditional industries don't have. So understanding the risks associated with AI, the biases and how to make that a responsible ethical practice along with the critical thinking, before we even press go on, what does it look like? I think those are the three components of AI literacy that every executive really requires right now. And quite frankly, nobody's talking about it. We're all getting pitched from all of these vendors about all these amazing magic things. But the reality is it's not just about knowing how it works, like you said, on a technical level, but understanding what Are its capabilities and limitations and what are the real world applications? How does that, what does that mean for me? So I'm going to just kind of kick it over to you because, you know the technical really well. And if you were to break this down into something that's really easy for executives and entrepreneurs to understand from a conceptual, from a conceptual level and how that practically applies, like, do you have like an explanation or an analogy that works really well? [00:06:05] Speaker B: There's, yeah, I would say the, the easiest thing for executives to think about is to take AI out of the equation and think about the user, think about your customer, what problem are you trying to solve and then go find the right AI. Because AI is changing so fast. I mean, you know, we saw yesterday that Claude, you know, was really 3.7 was released and now there's going to be another one released and we're hearing that, you know, you know, OpenAI, you know, well, they just released the, you know, O3, which is a new model. These models are changing so quickly that if you get tied to one, you're in trouble because in three weeks you're already, you're already, you know, you know, way behind. Right. And so you've got to be able to be flexible and adaptable. And so if you focus on the customer first and the problem, then you're developing things that are going to easily fit into, into that. And you know, that ties into your decision making, that ties into your resources, that ties into your, your, your staff and how you're going to staff those folks to make sure that they are AI literate or, you know, or maybe you don't need a senior level executive anymore. Maybe you can use a junior level executive. So it really impacts everything. [00:07:26] Speaker A: Yeah, you know, I'm thinking about this and I'm thinking about what I see from, from the strategy standpoint, they're like, where do we, everybody wants everything, all the things everybody wants to end, to end, integrate. And my answer is always, where does it make sense for us to leverage the tool first as a, as an executive? Like, what is going to give us the most either productivity bang for our buck? What is our goal? Are we looking for operational efficiency? If so, what are the processes that are taking up all the time? And what, and do we have everything in place? Can we automate that and have that workflow done and then expand from there? And I think one of the great misconceptions, and I think it's, it comes down to AI literacy. One of the misconceptions is that, hey, I'm going to put this one thing in and it's going solve all my problems. But the reality is it's a whole bunch of different solutions that you want to take and, and formulate for each of your processes. So as someone who develops this and you think about this a lot, what's an easy way for our executives and entrepreneurs to think about how do I determine what's that best first use case that aligns with my business strategy? [00:08:35] Speaker B: Yeah, we always, we always tell people start small. That's the easiest thing to do. Something that is going to, you know, take, you know, four to six weeks tops in order for you to see that any sort of investment is going to be worth it. You can develop your strategies around that after the fact. You can do a whole bunch of other things. But it's got to be relevant, you've got to have data and you've got to be able to do it fast. Because if it takes you six months to be able to do something, then you've taken too long. It's way too long to do a basic pilot for something like that. And you know, we actually, the call that we had yesterday with a potential, you know, for us, you know, I think you hit a home run like always when you had them thinking more on not just what I can do internally, but how can I sell that to my customer at the same time? So you've created a new, you know, source of revenue potentially for them. How can you, how can you help other people or you know, even, you know, explain that, that when you start small, you see the returns, but now here's a potential for roi. What do you tell executives to, to get them to think bigger than they normally do while starting small? [00:09:50] Speaker A: Well, you know, it's, it's really funny that you ask this because this is a, there's a twofold thing that happens when people are looking at AI. From, from my standpoint, most entrepreneurs don't like to let go. So they don't like to let go to people, let alone technology. So there's a little mindset shift that has to happen that, hey, look, this process is repetitive. What happens when you free that time? If I freed up 10 hours of your time doing all these low value tasks, what do you do then with that 10 hours? So it's about letting go, but it's also about, wow, I now have 10 hours I didn't have before and I can go out and generate new revenue. So then from there I go to great. If you integrate this and you see these improvements and now you have that time back, you're able to generate more revenue. What also has happened from an efficiency standpoint and the operating expense standpoint, I've optimized my bottom line, which is what we all care about. Bottom line is the money we keep. It doesn't matter how much we make. What matters is how much we keep. And so it's a mindset shift on two aspects. It's the I need to be able to let this go, but I have to see the vision of what's possible next. So it's a two step mindset shift and it really is about looking at what are my strategic initiatives, what are my goals for this year? Oh, your goal is to bring on an additional hundred employees and have them trained in your system. And right now you're doing that one on one. What can we do to make that faster, effective, more efficient? Free your time and then you're out sourcing more leads and having those sales conversations that's going to drive revenue. So it really is that one, two punch with executives. And so we have only about 15 seconds before we have to break for commercial. But Alan, based off of that and everything we've talked about, if someone is watching, an executive and entrepreneur is watching this and they want to improve their AI literacy today, where do they start? [00:11:50] Speaker B: Well, the easiest place to start is, you know, you can look internally to see what sort of expertise you have, but more than likely you don't have the expertise you need. So come out and get a, you know, a, a consultant look to, you know, go to these, you know, different courses. We have one we're offering April 2, where we're going to go through a process and help them understand where they're going to be ready and so get help. The earlier you get help, the more painless it's going to be along the way. [00:12:19] Speaker A: Absolutely. So you're going to want to stick around because we're going to be back after the break to really dive into what are the challenges, what are, how do we get ready for this AI integration? We'll be right back after these messages. Foreign welcome back to this special collaboration between power CEOs, the truth behind the business and AI today, where Dr. Alan Badot and I are deep diving all things AI literacy. Before the break, we talked about the AI misconceptions, how to get started, and there was one misconception that we didn't really discuss yet, Alan, and that's the misunderstanding or the belief that it's still early adopter mode for artificial intelligence. But statistically we're over 35 to 50% adoption rate at every level of market, from micro business all the way through enterprise. What industries do you see leading in AI adoption and what can other industries learn? [00:13:40] Speaker B: Yeah, and I think, I think too the, the, the misconception around that is that just because it's not successful doesn't mean that, you know, we're, we're not in that, in that new space. I mean there've been a lot of challenges, a lot of overhyping and that's really one of the bigger drivers. I think if you look at the industries where the regulation isn't as stringent for for example, maybe it's logistics, maybe it's, you know, the dynamics around that, maybe it's, you know, more back office, some financial. But financial can get pretty stringent too. But you know, those areas are really driving everybody else and they are non traditional IT businesses. Right. And, and that's the, that's the key thing, that's the, one of the important factors is that they are seeing problems. They have problems, they know they can solve it quickly and they're just doing it and that's, that's a big advantage for them. [00:14:41] Speaker A: Yeah, I see that too. I see it a lot. I mean I'm in coaching and consulting space and we've, I've been adopting and for the last couple of years and we've seen a lot in our space adopting because quite frankly in the small business level, and everybody says small businesses are slow to adopt, but the reality is is we can't find labor. We've had a labor shortage so we've had to come up and get scrappy and find other altern alternatives. And so we've turned to artificial intelligence, multimodal operational efficiency and the large language models to help us to grow because quite frankly we don't have enough hands on deck to do the things we need to do. And so I've seen it in those non tech savvy businesses as well. The other businesses that I see, I consult a lot with professional services industries, so financial, medical, legal, technical, and there's a lot more regulation in medical or legal. And so while they're exploring these, they're a little hesitant. They're a lot more cautious before they press go. But even these businesses are at the point where they're integrating everywhere they can integrate without bumping into that regulatory guideline. And now they're exploring, well, what do I need to do to be FDA compliant in my AI integrations, for example? So I'm starting to See that shift even, even more towards like, the clinical side and research and some of those other things. So it's really interest. The barriers to AI are different in different industries. Like for, for us. I, I come from medicine. I no longer practice, but I, I have a medical background. I practice for 20 years. And there are barriers. We don't want AI making clinical decisions for us, but the reality is they're able to present it in such a way and they're able to diagnose more rapidly than we as experts in our space can. So we're really facing this barrier of regulation compliance as well as, you know, what do we do here? What is going to happen to my job? What's going to happen to my team? So talk to me a little bit about the top reasons that you're seeing AI implementations fail or fail to start. [00:16:46] Speaker B: Yeah. So, you know, in your point on the regulation piece and the slowness of adoption is a very important one. You know, it was a milestone just a, you know, a few weeks ago where, you know, Johns Hopkins and the Mayo Clinic, they actually deployed the first production system, AI production system to look at X rays and to look at CAT scans. That's huge, right? I mean, that's a, that's a, you know, it doesn't sound like it, but it's, it's, it's huge. But, you know, I think the first part is you think that, oh, AI can't do it, so I'm not even going to try. So I'm going to, I'm going to focus on some other things, but I can't do it. I don't trust it, you know, all those other reasons. So we're not even going to, we're not even going to try to do the, the hard stuff when in reality, if they got some help, they would realize that AI can do it if it's done the right way. However, you can't rely on one tool potentially to solve all your problems. And so you get a salesman like normal, they come in, they sell you the world, you think it's going to do everything for you. And then reality hits when you start integrating it, that, oh, it can't do this, it can't do this, it shouldn't do this and never should do this, those, those sort of things. And I think they don't, you know, folks don't think about that deep of a, of an impact on your business. And it really, it really is, you know, going to be key to that. And I know you see it from the resource side, the Human resource side, trying to, you know, get the, get the right folks to be able to do the right things at the right time to make the decisions and the ones that are actually going to embrace it. You know, how do you, how do you help folks identify those resources or even restart a process in hiring those resources? [00:18:42] Speaker A: Yeah, I mean that's a, that, that's a million dollar question, isn't it? And, and so there's a lot of fear around AI replacing jobs. There's of fear from media over the years. Everybody thinks we're marching down the Terminator path or what have you. So there's a lot of fears, some of them rational, some of them a little bit more because of Hollywood over the years. And, and so as leaders, we have to first understand that we can't do this as a top down bully push. We have got to approach this from a different style of leadership. And one of the key aspects of AI literacy is AI leadership literacy. And it's also, and you've said this before, we've talked about this actually. It's not like we're implementing like an office or a word application. AI is like a virtual employee. So we have to consider that it learns from us, it learns our behaviors. We don't want to be interacting with it in a way that is snarky because then it's going to learn snark. We've talked about that before. But it's really important as executives and entrepreneurs that we are transparent with, hey, look, this is something, this is a direction we are going as a company. This is what it actually means. So it means us leading from the front, knowing our own, having our own AI literacy in hand and bringing that to our teams so they have an understanding that we're looking to augment our human capital as opposed to replace our human capital. Now some of those jobs, those admin jobs, yes, they're going to go away. However, there's going to be high value work. And I take this to clinical all the time because we've gotten so inundated with, you know, telemedicine and documentation and we're weighed down and we're losing 11% of our physician brain trust every year to burnout because we're spending three and four hours just in documentation because of regulation. If we took all of that away, what is the result? It doesn't mean we lose our jobs. We're not losing our physicians and our nurses and our nurse practitioners. That's not going away. What's happening is we as clinicians are able to get back to being human and spend more time with our patients, which is why we got into medicine to begin with. So it really is about how are the tools, how are we going to leverage the tools to augment what we're doing, take away the low value work and really focus on that high value human connection. And to the hiring piece, when we are leading from the front with AI literacy and bringing this transparent conversation to our teams, some people are going to raise their hand and say, I don't want to do that. I don't think this is the direction I want to go. And we're going to release them and love them along their way and say, that's that then, Then that's not, that's not where we don't really have a place. If you're not going to leverage the tool, we're willing to train you, we're going to provide everything. You knew there will be a transition, but we understand it's not right for you. And we're going to part ways in a, in a peaceful way and we're going to hire from a standpoint of, listen, we're leveraging these tools so you can get back to being the clinician in that example that you wanted to be when you started your medical school journey or whatever example that is. So I think, I think that it's a lot of education. It's us becoming AI literate as executives and entrepreneurs and then leading from the front and really allowing that AI literacy to bleed into all of our teams so they can see, you know, this is going to be something. Yes, it's going to be challenging. No, we're not going to blow smoke and tell you an integration isn't going to be seamless, because no integration is seamless. There's going to be a learning curve. However, we support you along the way. We're going to provide the tools and the training that you need and we're going to make sure that you're set for success. [00:22:27] Speaker B: Yeah. And I think AI is getting a little bit of a bad rap just because of the economic cycle that we're going through. People think that, oh, this is all AI driven and we're, you know, somebody's going to, you know, let go of 5,000 people. They were going to do that anyway because they got to cut costs. You know, if they don't hit their numbers on Wall street, then they've got to cut costs. And now a lot of that is being attributed to AI and unfortunately that's just not the case. Folks are having a hard time separating that. And I think, you know, if we take a step back and we say don't over promise when you're doing these things and that means don't over promise on how it's going to help your employees as well as your employees ability to do different sorts of things and skill sets around that. It's going to be, it's going to be a lot easier for folks to be able to grasp it. [00:23:16] Speaker A: Yeah. And I'm going to go back to what you said earlier in the first, in the beginning and that's start small. When we start small with the easier integrations, people get a little bit more comfortable. We want to give them the quick wins. When we think about what we're doing, we want to give our teams the quick wins so that they see, oh wow, yeah, I'm not being replaced, but holy cow, I don't have to do X, Y and Z anymore. I get to focus on A, B and C, which I really like and love and I can critically think. So there is going to be a little shift towards critical thinking and how do we, how do we monitor and who's in charge? Who is the human in charge of oversight of the virtual team, if you will, the AI team? Because that's going to be a new role that opens up and quite frankly it's going to, it's going to take either hiring someone in or it's going to take spending the resources to train our internal teams so they can handle that. And of course I'm just going to say something that you and I talk about a lot, but I want to drive this home to everybody. Watching your internal team that's never done AI before, cannot be the AI implementation specialist and strategist on your team because they don't have that repertoire. This is a place where you go to outside consults outside because the reality is if you think you're existing team this before in their purview, they're not going to learn it overnight. And they've not had this, this before in their purview, they're not going to learn it overnight. There are people who have been doing this for decades and we need to rely upon some of that external resource to make sure that we're set for success. We do have to take a quick break, but we're going to dive even deeper with examples of successful AI integrations. What does that look like and how can your business integrate successfully in just a few moments? Welcome back to this special collaboration between power CEOs, the truth behind the business and AI today, where Dr. Ellen Bedot and myself are deep diving into all things AI literacy. We ended the break talking a little bit about misconceptions and what do we need to think about from a people standpoint and how do we lead from the front to ensure success. So, Alan, I wanted you to share an example of a company that successfully navigated an AI integration. And let's break down the lessons that everybody watching can learn and take from it so that they can plan their successful AI integration. [00:26:24] Speaker B: Yeah, I would say the easiest example is going to be really any company that's still in existence today that's doing cybersecurity, because as we have talked on our shows many times about the convergence of these technologies and the impact that it has on everything else. And cyber is a great example of that because whether it's your laptop, whether it's your job, whether it's your email, whatever that is, you know, there are so many different influencers and so many different points of entry where, you know, you can get yourself into deep trouble or get your company in deep trouble. And I think, you know, as we saw, like normal, the hackers take advantage of this new capability faster than the good guys. I think we've started to realize that we have to catch up. And, you know, getting. Whether it's a hospital that has had their data frozen and they can't use it and they're pretty much not working for the next two weeks, or they go to paper, right? You know, holy cow, you're going to paper. It's the, you know, back to the dark ages, right? I mean, you know, those are some of the things that have been those forces around it. And then you've developed now partnerships. The Deep Web, it's going out and it's searching the Deep Web and it's getting so much more information on that that now you can take all that together and you can actually start to do things and identify trends and patterns that humans cannot do. And, well, they can do it, but it'll take them about 100 years. That doesn't do any good if it's hitting today. And AI allows us to do that. And so, you know, you've got your, you know, I'll say this, that even Microsoft and, you know, with the cloud and Azure that they have and Amazon and the, you know, the cloud that they have, and federating all of those tools into one place has been a huge saver, especially for small businesses, because they don't have to worry about that as much. So instead of looking at I've got all these new errors and bugs and vulnerabilities. I'm running things in the cloud now. I can get a lot, a lot more done a lot faster and I don't have to worry about my computer anymore. And so those are just, you know, you're looking at cyber, but the impacts around cyber and everything else is really the, the driver for, for a lot of folks. It saves so much time that you can't even, you can't even imagine as a small business owner now. [00:28:56] Speaker A: Yeah, I, I, I hear that. I'm, I have, I have seen other areas and you know, one of, one of the easiest ones to bring forth is something that I've integrated and that I've integrated with many of my clients and that's, I'm an investor, I go through deals, I'm looking at property deals, I'm looking at business deals, I do acquisitions with my clients as part of their growth strategy. And so I've literally trained AI on my screening process. So now what does that mean when I get the leads and I get the data it screens and I don't even put eyeballs on anything that wouldn't pass my normal screen. Well, normally that would take between 5 and 10 minutes per opportunity, maybe even 15 minutes in the screening process per business. We're looking at acquisition. Well, when I get a list of 2,000 businesses, you can do the math. That's a lot of time. Well, I trained the artificial intelligence application to go through my first pass screening and then to my subsequent screening. And now I only lay eyeballs on the ones that I would go into a deeper level of diligence with. So I'm not wasting time building relationships with businesses that will never pass muster. And it saved me tremendous time. And so not only that it's impacting myself, but I'm now leveraging that with all my clientele. So my clients are having this done faster. They're not having to spend as much on consulting with me, which, I mean, if you're listening to this, you're like, well wait, you just shot yourself in the foot. They don't have to spend as much, much consulting hours. No, that's absolutely fantastic because it means I can increase my volume. [00:30:29] Speaker B: Yep. [00:30:29] Speaker A: I can work with more companies, help more companies to grow faster. And, and, and so it becomes more of a volume as opposed to a, I'm doing all this low value work and screening. It's not low value, it's high, but it's an automatable process. And then the other application that really comes to my mind very rapidly is on the legal discovery process so many times what we have now available to us is you can take, and I'm going to use the medical negligence case type because this one we literally just integrated with a client about a month and a half ago and, and it's been so tremendous for them because it would take 40, 60, 80 man hours to read these 4,000 page documents, come up with a summary, decide if it's a case screen for the case, then they pull out what is important and relevant to move forward. And now that can be done in literally minutes. The screening process is done like this case versus no case and go. So we're not wasting man hours trying to figure out is this actually a case that is viable, do I have a case? So we're not wasting that time up front on something that will never be revenue generating. And then if it is viable, what is the summary and what is important. And it literally pulls out where in the documentation to go. That saves everything so much time because it frankly takes a lot of time for people to read through all of that documentation and then come up with a summary. So just that collapse time has improved the top line because now we're screening faster and we're only taking cases that work and we're able to increase volume without increasing staff. And so we've also increased the bottom line because we're not wasting time on leads that will never turn into anything. So those are just two examples. Very different industries, very different applications. But the time savings means we can do more volume work, get more revenue in the door and be more profitable on the other side. [00:32:26] Speaker B: Yeah, and you've heard me say it over and over again and it gets my hair on fire is, you know, somebody will say, oh, it only takes me 15 or 20 minutes a day to do that. That's not, that's not very, that's not going to help me very much. Well, when you add that up over a year, it sure does. And you know, time again is the only resource or currency that you can't get back. And 15 or 20 minutes a day is important. It really is. Because that's more time that you can spend with someone doing something with your customer. And you know, there are examples across every single industry. You know, we see it all the time. This is not a technology that can only be used in certain industries everywhere you just gotta look and, and, and folks just, you know, if they pay attention, they look at those sort of things, they will be able to use it somehow. [00:33:20] Speaker A: Yeah, and let me break down if you're watching. Well, how do I determine this? Is this a task or a process that I do regularly? It's done maybe multiple times a day or multiple times a week. And it's 15 minutes or 20 minutes, whatever that is. How many times do I do this task over the course of. I like this, the container of a year, I think that's an easy thing to do, the calculation on. And if I no longer had to do that, and, and, and I have an automation for that, what would I do with that extra time? And put a dollar amount to that? And what I love to do with, with entrepreneurs, because I work with a lot of entrepreneurs, is to say, hey, great, what is your time worth? So, okay, if you're working full time, which by the way, if you're an executive or an enterprise, I already know you're working more than 40 hours a week. But just if you're working 40 hours a week, take what it is that you're generating in revenue or salary or whatnot, and divide that by your 2080 and that gives you your hourly worth. And then look at the time spent on the process and ask if it's worth that hour per hour worth for you to be doing that task. And if the answer is no, we need to either automate or delegate it. Well, right now, with artificial intelligence, if it's a process that's repeated over and over again, you've got it dialed in. This is an easy automation to do. And it's going to act like it's almost like duplicating yourself. It's almost like duplicating yourself and your process. And it's so incredibly important that we take ourselves through that, but not just as ourselves at the executive level, but also on our teams. What are those daily processes and how much does that process cost? So we want to talk about roi. I know this because it's going to be a cost. Well, it's going to, it's going to take a cost to implement AI into your business. Yes, there is a project cost and there is an ongoing cost for implementation. However, how do we figure that out? What time, what is the time worth? How am I, what am I saving in that? And look at the difference between the two. And then apply this across all of your processes in your team. If you were in a process 100 times a week and it costs you 100 times to run the process, then you know you're spending, what is that? A one with four zeros behind it a week in that process. If an I can do this. And now it only costs $25 to, to, to run that process. Over the course of the week, you save 75% of your operational expense. And so that's very clear. Is that an roi? Yes. What time to roi? Well, here's my project cost, here's what I'm saving. It's going to take me three months or six months to break even. And then everything else is pure, purely improvement in OPEX from there. So a couple of easy ways to think about it. [00:36:00] Speaker B: Yeah, and, and, and that's, that's exactly right. Agree with you 100%. What do you, what do you tell executive Jen, though, that they say, well, I have to make a decision at the end of this process how AI is not going to be able to help me make a decision. I can't trust it. I can't trust the AI to do that. What do you, what do you tell executives, you know, when they're, when they're thinking like that? [00:36:22] Speaker A: Well, the reality is AI is actually really good at distilling information. It's really good at taking information. Now, I'm not talking about open source. I'm talking about if you have something that's trained on your data and your processes and your systems and you've put the right parameters in place and done everything right, and this is a sound integration, it's going to bring forth better data faster so that you can make a decision with more data than you would have had otherwise. We have to make thousands of decisions every single week. We know that as entrepreneurs and executives, if you're able to have more data points to make that decision, you can make a more informed decision and you make a higher percentage of good decisions very rapidly over time. And so it's about looking at this differently. It's a mindset. If you are, if you have to make this decision anyway, what do you, what, what are you spending your time? How are you doing this? And what level of data do you have? As opposed to looking at something that's analyzing every customer you've ever had and making a sales decision or a marketing message decision on that, you want them for data points. And quite frankly, humans can't do it fast enough. [00:37:30] Speaker B: No, and the other thing too is what, you know, everybody says, oh, it's got to be accurate, it's got to be accurate, it's got to be accurate. Yes, it does. We understand that. However, there is a tremendous amount of value and consistency. If you have a service desk, for example, and you've got 40 different people answering calls and they're answering them differently or they're responding differently. Your metrics are not going to be very good with AI. You will get a consistent answer. And it also helps, okay, if it starts to go off the rails a little bit, you know, quickly where it's gone off the rails, as opposed to you have one or two folks on your call desk that are just, you know, going rogue and saying whatever they want. That takes more time and more energy, you know, and so consistency is the, is the underlying factor that everybody forgets about. [00:38:20] Speaker A: Yeah. And it's so incredibly important. And we want to know that our data is good. It's not good data if we have things over, all over the map. And we see this all the time with sales training. I mean, this is a, this is one of the number one reasons people like they, they engage with me and I help them with their sales training because they don't even have a process, let alone a script or anything for their human component. And then when you're able to train some, an enrollment officer, if you will, out of your AI and it's doing your process every time you now have the data of what's working, what's not, when are they, when are they leaning back or leaning in, and you can make those tweaks a lot faster. We do have to take a break, but we will be right back after these messages. Welcome back to this special collaboration between power CEOs, the truth behind the business and AI today, where we're deep diving all things AI literacy. We have talked about so many things. We've myth busted. We've talked about how, how we need to approach training our team. We've talked about what are some of the considerations for an AI integration and everything in between. But what we haven't touched on, Alan and I really would like to go into this because this is something that I wish every entrepreneur and executive would consider first. But most of them don't even know to ask this question. How should business leaders approach AI ethics and compliance to ensure responsible AI adoption? [00:40:10] Speaker B: Yeah, and that's the million dollar question right now. Because, you know, especially in the United States, we are behind from a legal perspective on getting laws and regulations out there for folks to be able to follow. I mean it's, it's, it's all over the place, quite honestly. And if you're doing business over in Europe, then you've got gdpr, which is somewhat of a help. Then you've got EU that have passed some regulations around AI and ethical use and, and those sort of things. But it is a real challenge for folks to be able to understand and embrace. And it's really across every single market. Again, whether you are using customer data to build, you know, different models and different, you know, methodologies to, oh, I'm going to use books to train or copyrighted material to train, there are so many different things for businesses to think about now. It can almost be overwhelming. It really can. So what I tell folks, it's really simple. Just, you know, is it something that is going to impact your customers? And if the answer is yes, then you have to make sure that you are putting the right guardrails in there to protect their data. Think about your own data. Think about everything, your credit card information, your PII data that's out there, you know, where you live, everything that is available now, you know, on the net. You've got to make sure that that's protected and you've got to make sure that you're not using it in a way that is going to compromise, you know, their, their identity or fake them out, that they're, you know, not talking with an AI agent versus a human. Because that's a, that's another thing. Transparency and ethics go hand in hand. And you know, if you're not being transparent with your customers, then from my perspective, you're not being ethical. At the same time, they should know who they're communicating with. They should know where the information is coming from. So those have to really go hand in hand. I tell everybody if you're using an AI tool, not just developing one, if you're using it, you need to have an AI ethics policy. You got to make sure that folks are using it the right way, getting the right information, getting the right data, and then you're protecting it on the back end. And a lot of times, like you said, Jen, executives aren't even thinking about that until after the fact. And I think that's causing a lot of heartburn that could be taken care of way earlier than what it is. [00:42:46] Speaker A: Absolutely. And now let's talk a little bit about the democratization of AI. Right? There's all of these open space source platforms. Every everything has an AI integrated. I mean, we've got Gemini, we got all of these models, right. But I'm thinking specifically of some of the recent ones that have hit the news and oh, it's free or it's cheap, that has a price. Folks read the terms and conditions. Like a lot of times your data is using to train their models or maybe even being sent to foreign governments. What do entrepreneurs need to know? Because a lot of them, we've become so accustomed to clicking agree to TNC without reading them that we don't even realize what our data is doing or where our data is going. What is your message to those business owners who are allowing their teams to maybe leverage some of these open source models? What do they need to pay attention to and what do they need to message to protect themselves as a business, their clients or customers, and make sure that they are ethically and responsibly utilizing artificial intelligence? [00:43:48] Speaker B: Yeah, and normally with software you get, you get accustomed to the Adobe, you know, type agreement where, yeah, okay, I've seen this box before. Boom. I'm just going to scroll down as fast as I can and I don't have to worry about it because it looks just like every other one. AI is different. AI software is different because you're taking the ingest to the models, which is really the human input going into these models and then the AI is doing something and then it's generating an answer. Well, it's important for you to know who owns that answer when it comes out of the AI model. A lot of open source, you know, with Deep Seq and those, you do not own it. As the, as the small business, if you're using that, that's a problem. That's a big problem. And with some like Deep Seek, you don't even own the input. You think, oh, I'm going to put this in there, it's my data. No, no, according, no, when you read their agreement, you do not own it. And not only that, if you cancel your subscription or your, your, your access to it, they get to keep it. They don't, they don't delete you. They keep all of your data and can continue to train on that. So I tell everybody with AI, it's going to save you a lot of time and a lot of heartburn. Just get a quick legal opinion on it. They've probably seen it before. Let them read through it really quickly and say, no, I wouldn't do that if I was you. Because you know, it's going to, it's going to have long term impact to how you're using your data from your customers. So get some help. That's the easiest answer. Get a little bit of help, spend a little bit of money, save a lot of heartburn. [00:45:34] Speaker A: Well, and save a lot of expense. Let's be real. If we don't own something and we're putting something out and then, and then later on, oh, it turns out that you didn't actually own that response or that now your data is not yours. I mean, we've seen it so many times with these open source where people were putting their own ip, their protected information in and basically giving everyone else the right to use it. That's no longer your ip. Everybody has access to it now and there's nothing you can do about it. There is no recourse for you because you put it in there according to their tnc. So. So it's really important that we not just not just scroll past and sign off on anything when we're talking about that. Alan, we're getting kind of to the end of our episode. We have talked about a lot of different topics. We really have deep done a deep dive into artificial intelligence and business. What is the technology? What are the use cases? How do we think about this? What are the pitfalls and how do we avoid them? And what do we do to set ourselves for. Is there anything else you really want to cover before we close? [00:46:39] Speaker B: Well, I would say, you know, the biggest thing to think about is if you focus on your customer, it's not that hard. It really isn't. It's really making sure that you're hitting what they need to do. And, you know, there are folks like ourselves that are good at that. Get help. You don't have to come to us, but get help. We're offering, you know, a really an overview of how you can think about this from a technical, a business and a customer perspective on our April 2nd showcase. But you've got to think there first because everything else is really going to fall from that. Because you don't want to use AI for the sake of AI. Not in business. Education. Fine. And universities. Fine. You're developing something cool in business. We're moving too quickly for you to think like that. [00:47:27] Speaker A: Yeah, I couldn't agree more. And so that event happens April 2nd in Houston, Texas. It is literally bringing together the tech aspect, the business aspect, the strategy aspect, the legal aspect, and wrapping it up in a bow so you know exactly what you need to do and have the resources to get there. I couldn't stress more if you are thinking about putting this integration in or you're looking at your next integration. It's a great way to ensure you have the right readiness, the right guardrails, everything you need for success. And that's including change management, the people side, the process, everything. It would be really good. If you're interested in that, reach out to myself or Dr. Allen. We are both on LinkedIn and we both respond to our direct messages on LinkedIn. I'm also on Facebook. You can message me on Facebook as well. And. And we'll get you the information for that. Because it's so important that we do this the right way. And when we spend a little bit on prevention, we save a lot of heartache and money that flows out the door when we're having to attempt to cure a problem that we didn't think about. So we are at the end. And Alan, I love action steps on power CEOs. And so I'm going to ask you, what's the action that everyone watching can and should take today? [00:48:47] Speaker B: Go try an AI model. Just try one if you're scared. Just try one. Look at something that you haven't picked up before. Try something new just to get familiar with it so you're not scared. It's not going to eat you. You're going to be fine. [00:49:05] Speaker A: I love it. It's not going to eat you. I'm going to say watch some prior Power CEO episodes. I've interviewed Alan several times. We've covered some of these topics in deep dives and way more topics than what we even covered today. And go to Alan's show and watch AI today. It's free. It's a free resource. Educate yourself. When we have education, no one can take that away from us. Get knowledgeable and take that action today. We're learn one new thing today. Not tomorrow, not next week, but today. Because listening to us is great. But if you don't apply it to your business or know how you're going to apply it to a business and take that action, then you're the same tomorrow as what you were today. So let's take today, put that into action and do that without further ado, all good things come to an end, including this extra special collaboration between power CEOs, the truth behind the business and AI today. But don't worry, we will be back same time, same station for both power CEOs and AI today. Same time, same station next week. We will see you. Until then, win today, win this week, and we'll see you next time. [00:50:15] Speaker B: This has been a Now Media Networks feature presentation. All rights reserved.