This podcast episode is sponsored by ChiefAIOfficer.com, offering training and certification through the International Association of Chief AI Officers. Interested in a new career or leveling up your value in the marketplace? ChiefAIOfficer.com can help. The following is an interview with Edwin Lesowski. Edwin is the co-founder of Adepto, a top AI consultancy recognized by Forbes alongside industry leaders like Deloitte. Since 2017, Adepto has been delivering cutting-edge AI solutions across industries, from natural language processing to generative AI. In this episode, we explore the evolution of AI adoption, how businesses can use AI agents to solve real-world problems, and the steps companies, large and small, can take to successfully integrate AI into their operations. We also dive into Edwin's perspective on the future of AI, including its potential to enhance decision making and drive innovation. And let's begin. Welcome to Using AI at Work. I'm your host, Chris Daigle. Each week we'll be learning how today's business owners, entrepreneurs, and ambitious professionals are getting more done with smart use of tomorrow's tech. Let's get started. Phoning in from Warsaw today for this. So thank you for being on the podcast today. Edwin, tell everybody a little bit about kind of your journey to where you are today.
SPEAKER_01Of course, cool. So Greece, first of all, thank you for inviting. My pleasure. And since then we were deploying different AI solutions to our clients. So as you also, Chris, noticed, we've been a while before ChatGPT was even introduced into the market. So we see how this technology was emerging for all the last years and how basically that being adopted today as well. Yeah.
SPEAKER_00So how long have the operations been, I guess, uh in Warsaw and New York? When did you open your New York office?
SPEAKER_01I mean, since since beginning. Since beginning, we've partnered uh with some uh people who are based in uh United States. So uh uh our our goal was basically you know um bringing our talent from Europe to United States uh overseas so uh we can collaborate with you know recognized brands and big big uh companies uh in in US.
SPEAKER_00And I noticed that you guys have been working with some very large companies and and an article I saw in Forbes from again before GPT 3.5 was released, you guys were on a list with some very heavy hitters like Deloitte on the top 10 AI consultancies. So how did that happen? And that's a quite an accomplishment.
SPEAKER_01I mean we we we see that uh also the work that small companies as Adepto are doing can be also recognized. Yes. Nice.
SPEAKER_00Yeah.
SPEAKER_01And uh the one thing I I would like to mention is we usually uh in some tenders or you know public RFPs or the uh project uh uh uh tenders we compete with such large players, yes? Nice. And Adepto in some uh cases is selected because of its you know expertise, uh agility, as well as uh you know how we approach uh topics in a very fast manner as we are a small, agile, and very fast company. Uh and we usually bring this uh approach and bring our uh you know uh flexibility into our clients' projects as well.
SPEAKER_00Are there any like particular industries or uh categories of product that you guys like to focus on? I would say yes and no.
SPEAKER_01We've been partnering with very different uh industries, starting, for example, from a real estate, uh going through a retail, manufacturing, uh entertainment industries, but also partnering with such interesting uh uh uh uh industries like aviation, airlines, aerospace. So it's really, really uh, you know, uh uh very different uh verticals and industries. Yeah.
SPEAKER_00Great. So as I mentioned before we got on the phone, my particular interest and uh I think the gateway drug for a lot of people into AI is the generative side of things. Now you guys have been doing way more advanced stuff than the than the average user for sure. And what I'm particularly interested in is when did you start to pay attention that generative first off, did you have any idea that generative AI's application would be adopted? So to me, it seems like it's everywhere, but I know that we kind of live in a bubble of the AI space. Um and also like what what when did you first notice that this is something that we really need to pay attention to?
SPEAKER_01But people who are in the industry for some time, we were aware of companies such as OpenAI. Before introducing ChatGPT, OpenAI had some kind of libraries, I could say, that we were using in our development or projects. They call just GPT, you know. Yeah. Uh and uh Chat GPT is an interface to to to like to the product or project we were building for years before introducing in 2022. Yeah. We as a company paid attention to the generative things again, I think since the company's uh beginnings. Even then, we were like it called a little bit different. Yes, we recalled such projects natural language processing project, natural language generation project, and in in those solutions weren't so smart as Chat GPT today, but this solution solved very concrete small problem related to text or natural language. Yeah? Yeah. Well even before that we were like you know aware of such technology, but the like really the perceptions of what is possible with this technology changed when the Chat GPT was introduced in the to my okay.
SPEAKER_00This is interesting. So me, I'm a non-technologist. Uh and as a matter of fact, when Chat GPT was released, I saw I I knew instantly, wow, huge opportunity, but not for me, because I don't know technology, right? Like I'm more of a a business uh business operations, business growth and scale. So the uh initially I was a little um I wasn't that interested, honestly, but you guys, I guess you were there before even the GPT paper came out at Google and you guys were already in AI. So you've been really witnessing this from an experiment, a theory to an experiment to uh a more interesting experiment to really something that's established itself, let's say, at this point, right?
SPEAKER_01Yeah, yeah, yeah. That's that that's accurate. And uh I mean this is how things were like moving. I mean, yeah. As it as as as you mentioned, like with Google and OpenAI, and all the large companies were working on such technologies for years, yeah. And we were just using that to solve like minor problems or smaller problems today uh with uh uh uh uh this technology that's already introduced into the market, we are capable to solve big larger scale problems. Yeah.
SPEAKER_00We'll talk about the um the actual impacts of what that looks like in before you were able to use these smarter versions of these tools and after. But I'm interested, has your client base changed at all since uh there's now this this kind of more accessible version of AI?
SPEAKER_01I would say not really. Uh-huh. Uh the only thing we've noticed that, as I said, like uh people, some people uh were not believing in AI before ChatGPT. I mean it was kinda okay, uh AI, this is maybe statistics, maybe some rules, maybe some coding, software engineering, plus data that that was AI, you know, uh before uh before ChatGPT. And all of that was about, you know, predictive analytics, just predicting some things like demand forecasting, you know, churn prediction, uh computer vision stuff, you know, like detecting objects, people, faces, uh, recognizing some images, uh, and moving to the text processing. ChatGPT showed to the whole world basically how powerful is technology, yes? And uh we were capable to do a lot before ChatGPT was introduced, but the challenge was also to you know uh uh make people believing in it, but now people become more creative when it comes to AI because they see in like in real life applications or actions how ChatGPT is powerful.
SPEAKER_00Yeah, as you were saying that I was thinking about that. Prior to like what happens if you're not a technologist, you're just a business professional going about your day, and you're not involved in the the architecture, the application of the technical side, it's a black box and you don't even understand that it it just gave you this business intelligence, right? But when it's in your face, when it's on your phone, when it's in your car, when it's on your screen, and anyone can say, hey, I'd like to build an app that does blank, and the techno that simple interface allows the technology to um again, not saying that they're on parody with a PhD in uh computer engineering, however, that has certainly expanded the awareness in the general public about this quote-unquote artificial intelligence. People have all kinds of ideas about what that means and what it means for the future and that sort of thing. So it's fascinating. We don't normally get people with uh as strong of a technical background as you on the podcast, and I have a lot of questions about that because my my position in business, at least, is that for most businesses that that that are in hearing about AI, machine learning isn't the top of their list. It's hey, how do we do this faster with fewer resources or whatever? So um a lot of times I uh I don't even uh pay much attention to the technical side because I know if they need that, we've got resources. Hell, now we'll call Adepto and you know, send you guys if you want the business. But um this is interesting. So the clients that you already have, I would imagine that that they've been gaining benefit, obviously, from the work that you've done. But now that they're seeing these things, is there a lot of repeat business? Are they saying now we want to introduce it into operations or now we want to introduce it into product design or anything like that? Yes, I would say yes.
SPEAKER_01Because when we see what is capable uh within again uh Chat GPT, you can think of a lot of use cases, but still in a limited way. Because you're limiting to the possibilities of what you already tried, used, and you can imagine okay, what kind of business problems I can solve with such tool. So uh today use cases are very repetitive, yeah, but with some unique solutions for some industries or departments. Yes? And let just just give you an example. After Gen AI was introduced, all the companies started building internal knowledge-based chatbots.
SPEAKER_00Yeah.
SPEAKER_01So everyone will start asking questions about the company, about compliance, process procedures, in separate. Employees using accessing internal knowledge, yeah. Yeah, yeah. But now it's starting, you know, expanding, evolving a little bit, and we starting adopting that to a niche specific, you know, use cases or business problems, like okay, what if AI can read our internal knowledge, internal database, documentation? And what if this documentation is way complex? Like it's engineering documentation with the code, maybe some graph architecture, maybe some technical design. Uh what if we can using that data start generating more complex information, like technical documentation, engineering documentation, simulations documentation. Yeah. What if we can start drawing architectures or very complex technical details using AI? Yeah. Those are very uh uh, I would say more complex use cases. Uh those use cases take more time to implement, but those use cases bring a real business value.
SPEAKER_00Yeah.
SPEAKER_01Not just you know, having and chatting with my company documents. Yeah. Bring more real value and automate complex but repetitive work.
SPEAKER_00I guess I hadn't considered that before. So that just to feed you back what I heard, the easiest step for them was access to internal information. Oh, where do I find this, or an HR policy or whatever, right? And then you're saying that because it had access to that knowledge, the next step was that it wasn't just request retrieval environment. It now, hey, I've got all this information. Let me start applying intelligence to what I've got in my database to identify, hey, maybe this. Oh, we shouldn't be doing this, all of that stuff. Is that I I guess I had never really thought about that ascension.
SPEAKER_01It starts becoming a real human agent. I mean, it's not it's not replacing the humans, but it's like real human uh uh augmented intelligence. Uh and it's really uh uh augmenting the work that we are doing on a daily basis, yes?
SPEAKER_00Huh. Interesting. So what are some of the I guess the standard stuff? If there's a business that's out there that is, in your opinion at least, I know you have the stronger technical background, but for somebody that was that had never considered that they had the either the environment to deploy AI or the budget to deploy it or the the talent to deploy it, and now that that's changed, what would you suggest to some to a business owner who is saying where do I start?
SPEAKER_01I would say uh start small but big, but think big. Uh so you always should consider some like larger or or bigger vision for your AI, you know, strategy. But I would never recommend to start so big because you should start with some smaller use cases, uh adopting AI in a very simple, you know, uh workflows or procedures, and as well making sure your talent pool in the company in the people are ready for this AI adoption. Yes, yeah, so you never should uh uh forget about the people who are inside the company, and because these people would be adopting this AI, yes, and the goal is here for them to not be afraid of the technology, yeah, but rather being open for adopting this technology.
SPEAKER_00How do you guys address that? That particular part about the change management, about the employee concerns.
SPEAKER_01Yeah, I would repeat again uh AI is not here to replace us. A AI is to support how we make uh decisions, how we make things faster, better, uh with uh higher quality, so that people can concentrate on a more complex uh decision making and can start just boosting our in all performance. Yeah? So that's to boost our performance.
SPEAKER_00Yeah. So for the audience listening, to to to you and and I, this is uh you know an exotic uh application, like, oh my gosh, it can it's capable of doing all these things. To Edwin, you know what's really going on. You were there before this was launched, you understand it at a way deeper level. So if you're not scared, we shouldn't be scared, right? Like as far as it replacing you. And I think that, you know, I'll I'll tell you, I I hear that a lot. AI is to augment and it's not here to take our jobs and that sort of thing. Um my my counter to that is uh beware of someone who knows how to use the tools, that's the more likely replacement for you, right? But um I guess I just thought it was lip service from the media and things like that. But coming from somebody who has a deep understanding of what the true capabilities are, been there, you know, for a while, uh if you're saying that, then I I I think there's a lot more credibility to that statement. Because it would make sense, obviously, for anybody who's an AI enthusiast to say, oh, don't worry, it's not going to take your job. Even if they had some concerns or they knew that that uh it would, it wouldn't behoove the perpetuation of of AI in the business and and that sort of thing. But to hear you actually say that's not how you guys see it at all, uh I think gives that statement, again, more credibility. So um aha moment.
SPEAKER_01A good good good point and good uh uh you know um like uh that you said Chris, like we should be more scared of the people who are capable to use AI, you know, in their work at scale rather than just you know be uh afraid of AI, yes? So the people who are capable of using it and are you know uh uh capable of adopting AI for their day to day work are really you know the future.
SPEAKER_00If you're enjoying this episode and want to learn more about how to start using AI at work, we've made it easy for you. For just one dollar, you can have full access to the chief AI officer community, which will give you additional training, custom software, daily training calls on AI tools. Using AI automations, getting more from your Chat GBT sessions, and the business of being an AI consultant. Simply go to chiefaiofficer.com forward slash insiders to accelerate your AI journey. Now, back to the episode. Yeah, they're gonna get more done, they're gonna get it done faster, and they're gonna get it done at higher quality. Yeah. So I guess at a minimum, that would be the recommendation for people is learn this not just not because it's exotic and it's shiny object or anything like that, or because you can do the fun stuff, make songs, make pictures, whatever. That's great. But really consider this application, as the title of this podcast suggests, to on how to use it at work, right? So um do you guys, when you go into a company, is there AI strategy? And when I say strategy, I'm talking about the context of okay, the business has a strategy and someone has evaluated where does AI disrupt it, where does AI enhance it. By the time you guys get involved in companies, has that conversation already happened internally, or do you participate in that conversation? I think both. Because you're dealing with you're dealing with big companies, right?
SPEAKER_01Like you're yeah, I mean, in some cases, we are engaged into these uh conversations from very beginning. I mean, because some of the uh um like uh companies would like to hear this outside advisory, you know, like to to hear this uh the company uh outside the organization and uh to make sure uh uh uh they are not making the mistakes that you know other companies made, for example. And and and we can advise uh on uh how to start in a better, faster, and smoother way. Uh in some cases, uh this strategy uh already uh you know uh uh created, designed, and also influenced by people inside the company, because a lot of also uh big companies employ very talented people who you know are uh experienced in AI and can also guide that uh uh these companies, you know, um inside. Uh in both cases, uh uh uh uh we take uh and participate in uh some conversations and uh we advise uh you know uh how to make less mistakes while adopting KI.
SPEAKER_00So just for context, the the average business that you deal with, what where are they in the strata of enterprise down to small to medium business? I think 80 or 70 percent of our clients are like enterprises. Yep. Okay, nice. So obviously they have bigger budget, access to talent like you guys, like Deloitte, like other you know, big consultancies that the uh firms below them can't afford, and just there is a limited amount of talent on the marketplace anyway, so they just can't get access to it, right? So what advice would you have for those companies? Because same thing. They they have some talent in their business for sure, um, but they don't have the big budgets to access the recognized thought leaders necessarily.
SPEAKER_01I would repeat think big, start small. You can really think of very small, and I would say repetitive use cases and workflows that can be automated very quickly without, you know, those big budgets, uh long timelines, etc. You really need just to understand your your business and challenges and the problems and uh the pain points inside the company and just try to map how AI can influence each of them. Yes? Uh because it's not really about creating custom solutions today.
SPEAKER_00Yeah.
SPEAKER_01It's it's really in 90% use cases, it's really understanding what is our pain point and how we can solve that with the available technology.
SPEAKER_00That's great advice. So you mentioned computer vision earlier, and when I was checking out uh what Adepto does, that's an area of operation for you guys as well, correct? Yeah. How does that intersect with what your client is like, is that a completely different client than the enterprise side, or is it usually because you mentioned aerospace, you mentioned aviation, that makes a lot of sense, but how about some of these other industries?
SPEAKER_01The computer vision, it's a vertical of the technology. I mean, it's like so it's the it's the part of EI. This is where we uh just you know uh not analyze the text or read text, we uh analyze images or we teach computers to see as humans, see, yeah. Uh and uh there is also a bunch of different use cases, uh, I would say in such industries like uh, for example, manufacturing, where computer vision is making a real impact when it comes to the production quality assessment, when it comes to the logistics part, uh, when it comes to the adopting also smart robots into uh manufacturing processes or uh uh uh or some of the workflows. So there is a really like different uh bunch of different applications of computer visions that can be adopted also on uh uh uh uh uh uh your operations.
SPEAKER_00Yeah. Yeah, I I you're operating you're you're definitely applying artificial intelligence in a much broader spectrum than I am. Fantastic. Um so this is making a lot of sense to me because mo like when I go into a business, uh I'm looking again to impact uh SGNA, sales general, administrative, op ex, things like that, not necessarily um such creative application of what you guys are doing. So it makes perfect sense that of course compute AI could power computer vision that could identify the smallest defect in a chip or something like that. Um when I get into that scenario, I'll give you a call. Yeah. Um because it it's it may come up. So I'm interested in how you guys are interacting with the clients beyond the technical side of things, and and in particular, the are are do they know what they need when they reach out to you, or are they saying, hey, come in here and tell us where it can work?
SPEAKER_01Both scenarios. Yeah. Uh both scenarios, because it really depends on uh what is their maturity level when it comes to the data in AI. Uh and we are not talking here about the whole company usually. Right. It's rather how this particular department, not business unit, which is reaching out to us, is you know uh ready for the AI or or you know its usage. So um, and we basically cover both scenarios. Uh in first, we we came in and we looked at the data, we look at the process and we say, okay, this and this and this can be automated with uh that and that technology. Um in second case, uh we came as uh we come as uh uh like uh technical implementation expert, and we say, okay, uh this problem can be solved with this uh technology.
SPEAKER_00Yeah. Very cool. It'd be fascinated to sit in on some of those meetings because again, uh the the new user like to them it's it's typing or talking to a device and getting thought deliverables, not necessarily the physical nature of the things that you guys are working with, like computer vision. It's pretty fantastic. Um AGI is uh one of those topics that certainly the stated mission of a number of the the companies out there to to pursue and achieve AGI. What do you think the timeline is before we see something to the point? Because and I'll tell you from a business context, Ed when I'll tell you. There's been a lot of chatter about agents from the from the release of like, I don't know, early 2023, but nowhere near the capability. This autonomous agency in particular, right? 2024, there's companies out there that say that they they create agents, and they have agentic activity, but they're not autonomous agents, right? And 2025, from from the business perspective and the conversations I hear, it seems that A, the word agent has entered the vocabulary a lot more, um but also an expectation. People getting into this are expecting that autonomous agency, which does not exist as far as maybe in some a few rare cases, but not you know commercially available mainstream to the average user. What do you think is is a realistic kind of scenario related to timeline on capabilities of that type?
SPEAKER_01Yeah, I mean we expect uh 2025 to be the year where we uh will see this real disruption and maybe a real change from what we saw with 2022.
SPEAKER_00Yeah.
SPEAKER_01So 2025, I mean we real we expect to see a really, really differently behaving model capable of more, you know, activities and uh um than when we uh saw starting 2022, yeah. Yeah. We still do not expect that to be uh fully autonomous AI, yes. We still um expect to be that limited, and we still uh believe that will be uh need some adoption. What I mean by saying adoption, that means that uh the solutions that we will see, we will will be capable of doing a lot, but with the proper instructions, proper, you know, uh uh coding, and with properly fitted data. Yes. Uh but I don't see yet to be that you know fully autonomous, uh general artificial intelligence. Yeah.
SPEAKER_00Okay, this is this was a helpful uh question because now you know we're at the level of artificial narrow intelligence. I can ask it something, it can do a very specific output. So this would be uh for the a way for people to look at it might be that the the term agent and its application in 2025 is just a more robust application of narrow intelligence. Yeah. Okay. So, folks, if you are expecting agents to uh be that situation where you can just type something in and it takes it full stream down the, you know, runs the ads, uh, writes a create, like not there yet. However, Edwin, the architecture of that would probably be a series of agents that were chained together or in a flow. Yeah. Okay.
SPEAKER_01Yeah, yeah. I mean uh you should think of a genetic approach of something like understanding your query in a way that it can divide that into smaller tasks, it can run those tasks, it can make a decision while running these tasks and get back to you with the summary result. Yeah.
unknownOkay.
SPEAKER_01That's really what we have today, but it's it's it's it's really uh needs uh human control, yeah. Yes, yeah. Uh but what we expect it's still to we will have to build the the these chains of you know agents, uh and uh as I said, we need to feed them with proper, for example, business data to make good business decisions, yes, or decisions that are in the chain of uh actions. Yeah, uh it can be easier to build an agent that can shop for you because it's your pattern obvious task when it comes to the consumer action, but it's always will be more difficult to make sure AI can make a proper and very uh you know uh good business decision. Yeah, as soon as it doesn't have this big picture and content.
SPEAKER_00Yeah. Oh, interesting. Because uh different than the conversation that we had at the beginning, which was create the bot with access to the company's information. Oh, wait a minute, I've got this pool of information, how can I act upon it intelligently? The difference is that we're now going to be in these smaller discrete environments where it won't have all of that external context within which to consider its next action or its step. Is that accurate? Yes, yes.
SPEAKER_01All the decisions that agents will be doing should be based on some context and data. And when it comes to the company data or its decision making, it's really huge context. Yeah, yeah, yeah. It's a bunch of data, it's a bunch of conversations, yeah, it's a bunch of meetings, etc. Yes. So I mean we moving with small steps to this, yeah, yeah, and we are moving to the, I think, to the place where AI will really take this whole huge, you know, I can call it knowledge graph, maybe, yes. Yeah. So like uh uh information structured in a way that machine can understand properly. Afterwards take some uh of their decisions, but I think it still will take some time, yeah, to to to be in this place.
SPEAKER_00So again, not being a technical expert, let me let me see if I get this architecture right. Or tell me if this tracks with logic. Based on what you've said, it seems like the best case scenario for a company would be that they would have essentially their own LLM with agents operating, you know, powered by whatever model, but but the data set would be weighted towards their internal information. So you could still benefit from the agents, but it's not drawing from the 34 billion parameters or whatever in Llama. It's drawing uh it could be, but it's at least more specifically or initially considering the company's data. Yes, yes, that's pretty sexy.
SPEAKER_01Yeah, yeah. And the whole company's data is structured, for example, in a sound again, knowledge graph, and its knowledge graph represents like companies' brains, yeah and with some neurons, connections, relations. Yeah. So machine interprets that very precisely, and it really knows where and how to make those proper uh decisions.
SPEAKER_00So, man, I do these podcasts, uh honestly, I do them for the audience more than I do myself because I feel very comfortable in the narrow application that I do, generative AI and business operations and stuff like that, right? Um this conversation has been outside of my normal dialogue because it's there, it's it's forced me to uh introduce technical considerations that I just don't normally have a need to, right? And not not that's I'm not wired that way to be interested in the the the technical nature of things. However, I can tell you that this conversation has led me to have a a couple of ahas for sure related to the um the this concept, this paradigm of agents, uh, right? Like the clarity of the fact that autonomy is not expected or even necessary or inaccurate in the the the term agent. That was a big one for me because I was um and then this understanding of yeah, there's plenty of the relevance AI and crew AI, there's fantastic tools out there for the average user to be able to do some stuff, right? Um and but I I hadn't considered, oh my gosh, if I isolate or have uh weight the data towards my own company's data, the absence of um it there's no longer the absence of this uh context-specific intelligence, which I don't get if I go use an off-the-shelf, right? If I go pay $39 or $99 a month for some sort of a, yes, I can get agency activity, but it it is like baby steps compared to what you're able to do when you're specifically targeting or weighting again the the data training on, wow, that was to you that may be basic stuff, but to me that was uh that was actually a big aha. I understand it a lot better now.
SPEAKER_01Yeah. Yeah. I mean AI is an exciting field, yeah. And it's it's it's really evolving a lot in those last years. Uh and we hope, you know, uh to see that we'll uh be adopted more and more, yeah.
SPEAKER_00So do you in in particular in 2025 do you expect to be uh I don't know, what what would be do you expect to be doing a lot of you and your team in 2025 related to AI?
SPEAKER_01Yeah, I think uh a genetic approach to solving some real business problems would be uh uh the main topic, maybe not from the beginning of 2025, but uh uh I would rather see as AI in 2025 not just you know like input-output solution, like giving a prompt and getting the output, but rather making some decision or action in the background.
SPEAKER_00Nice. This has been fantastic. Hey, so uh uh as you've seen, I had an aha moment, which I love. We we uh we call it when people start thinking in AI, right? They they make that, oh wow, can I do this? Um that happened on this call. So thank you very much for that, Edwin, for me personally. Um, how can do you produce a lot of, or does your company produce a lot of uh research or or thought leadership or anything? Because I I would like to follow the progress on this with your company.
SPEAKER_01Yes, we are. Uh we are producing regular uh uh newsletters and uh like uh white papers on our findings. We're also uh doing uh the the our own research on how companies are using AI in their businesses, not always related only to our client base, but also outside of you know uh our our client base, but rather looking on the on the market in general to give uh to the market this general overview. Yeah and we really uh uh invest a lot in um RD internally, as we have our own RD department where we build and test a lot uh the technology which is available in the market uh basically to understand and uh uh uh uh get this feeling, what is possible uh with this technology.
SPEAKER_00Is that what we can find start plugging into that research would be at Adepto, your company's website? Okay. Wow. Well, awesome, man. Thank you so much for dialing in and taking the time out of your day. I I I imagine you're probably pretty busy uh and the time zone for sure. Again, this has been uh I've got a lot to think about, Edwin. Any closing remarks for the audience? Yeah, it's exciting, really.
SPEAKER_01Yeah, and uh uh it's we we we live in a really exciting uh uh uh world today. Uh and uh I hope you know that that our conversation also inspires you know uh uh your listeners. Nice how how how to use and how to think, you know, uh uh about AI. And maybe you know uh uh we can also start exploring what is the possible and how uh they can just plug in that in their daily lives.
SPEAKER_00Yeah.
SPEAKER_01Well uh yeah. Think, think, think, think big, start small, and that would be my you know final remark here.
SPEAKER_00Man, that was great advice. And again, I had a couple of perspective shifts because this conversation is out like I don't have enough. I've got a great knowledge base on the operational and business side, the the tactical, like the tactile side of business, not so much on the the technical. And this has been um a gift. Thanks so much, buddy. Thank you, Chris. Talk soon. Thanks for tuning in to using AI at work. Don't forget to subscribe for more conversations about how to use AI at work. And a special thank you to our sponsor, Chief AI Officer, for empowering businesses with AI education and training. Visit their website for free AI readiness assessment and AI strategy guide to help you get started using AI at work. That's www.chiefaiofficer.com. Thanks to our producer, Evan Solier, for making this episode possible. Follow us on Twitter at handleusingAI at work. And visit www.usingai at work.com for free resources to help you harness AI in your role.