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456- AI Still Needs Architects and Developers w/George Hicker · You've Been Heard

• 0:00
  Speaker A: If we're not using AI, someone out there in the business can go out there and with a prompt very easily and quickly create something and it will work. Now it may not work great and it may not scale, but it will work. And so they can get a working solution in just a matter of hours that used to take months to get. And so that's what I'm seeing more and more is that if it doesn't respond fast enough, if the business doesn't feel heard, they then the business is going to go out and use AI and they're going to develop their own solutions. And at some point they ask, well, why do I need it? I can go out here and use one of these AI programs and turn around and get something. And you hear in the news all the time companies laying off lots of people because they've got AI. So I think that's a real issue that's kind of in our environment today.
• 0:58
  Speaker B: After talking with IT leaders, I've come to discover one thing. And it's the same thing, you know, it's the same thing everyone else knows whether they want to admit it out loud or not. Nothing gets done in any company without it touching it. Nothing. But here's the thing. When everything's on fire, when nothing's working, when the ransomware hits, when the whole business grinds to a halt and it is completely indispensable, you're the most valuable asset in the company. But when everything's running like a well oiled Tesla, you're a line item, a cost center on a spreadsheet. You've done the work, you've built the career, you've earned your seat at the executive roundtable. But having a seat at the table is one thing. How much nicer would it be to actually be heard? This is is. You've been heard.
• 1:52
  Speaker C: All right, welcome back to you've been heard. Sitting in our guest chair is George Hicker, an experienced technology executive. George, mind introducing yourself and sharing what your experience getting to the leadership chair really looked like?
• 2:06
  Speaker A: Thanks Mike, really appreciate the time today. So, George Hicker. So I'm vice president of IT and business transformation at Renoco Beverage Solutions. And I believe if I remember the question was how did I get to the chair? I would say it's been a journey. Right. I started out doing application development. I got into a lot of different side things. Data architect, business analyst, project management. Really enjoyed the management side of things. And so I have spent several years doing management doing at Enterprise holdings or Rent a Car. They own Alamo and national as well. So spent many years there where they really helped groom some of my management experience. Through that, got into some consulting and different things and through those experiences really started leveraging some of my experience to help managers, especially CIOs. When I was doing consulting was kind of a virtual CIO helping do different guidance and leadence in that area. So at Renoko, really helping them modernize their solutions, especially their ERP and CRM solutions.
• 3:12
  Speaker C: If you don't mind. I'm just kind of curious, what aspect of the beverage world does Renoko deal with?
• 3:18
  Speaker A: Mainly coffee and tea and things. It's kind of exciting. I got there and I'm not really a coffee person myself, but thankfully I don't have to be there to work there. Love the smell of coffee, so it's great walking into the office and smelling the roasting. What they do is they really specialize in premium brand, premium blends of coffee. And so they service mostly businesses. They're not really focused on the coffee customer side of it that they're focused on B2B side of the business. So they sell premium grade coffee to a lot of convenience stores, restaurants, people that are serving large amounts of coffee. When we go into convenience stores, we're not really competing with Coke and Pepsi. They kind of dominate the market in the carbonated beverage. So what Renoko wants to do is pretty much own everything on that counter or cabinet next to it. So that would be the coffee, the teas, the lemonades, the frozen drinks and then all of the supplies that go in with that, even including some of the marketing and the wraps around the machines and some of that. So that's the beverage solutions. So they're really coming in to offer full beverage solutions from equipment, the coffee products, even the marketing that's wrapped around it.
• 4:27
  Speaker C: Cool. And that alone tells me a lot because it also points out the fact that you know your business more than just the technology that it takes to make some of this stuff happen, but that you understand their goals and their desires and what they're trying to do. You're a little anomalous. For a majority of the people that I've talked to in the years that I've been interviewing and that most of the people that I've talked to have either come in from a wildly divergent or have come up from like the help desk. Everybody seems to like started off with move out of the chair, but you were doing development and it sounded like a lot of the development and the data side of the house and then into the project Management. Did you start your journey into management from project management or was it leading the teams as you got more experienced in your capabilities?
• 5:21
  Speaker A: And yeah, and I'll go back a little bit. When I was a developer, started developing when I was 12 years old. So by the time that I was in my 20s, I'd already been developing for at least 10 years on some things. And so with that, as I was getting into some development, was really enjoying the challenge of it. At some point, it wasn't as challenging anymore for me. And I had a kind of a mentor who came up to me one time. He's like, have you ever considered management? I said, absolutely not. I'm a technical guy. I like the technology. And he's like, well, you should really consider management if you like a challenge. He says, when you tell a program to do something, it does exactly what you tell it to do. When you tell a person to do something, they may or may not do it. And I kind of wrote him off. And then a few years later, probably two years later, I was kind of getting bored with development. I kind of thought, well, I wonder what it would be like to manage people. And so I kind of started on that journey. My first project management position was actually managing people. It wasn't just managing the project, there was people reporting to me. And so I had a little bit of a taste of not only managing the project, but managing a small team of developers with that. And so as the opportunities grew through that, did some management training at Enterprise. I had even stopped getting my degree. I had started getting a degree in coding and technology. Stopped doing that because I was being paid more than what school was paying. I had to pay for school. So I went back and got a business management degree once I got into management because I realized I didn't finish my my degree. And so I went back and got a bachelor's of science in business management and just really enjoyed going back and having that experience as well through that. So it was a journey to get here. And again, I'm thank with a lot of people that are in my life, mentors that came in and kind of helped guide me and ask the right questions that set me up for this.
• 7:16
  Speaker C: Cool. The fact that you started at 12, it made me think of, hey, I wrote my first program at 12. I think there's a slight difference though, because I wrote my first program on an Apple IIe in a computer lab at school in 1981, and a computer lab at school was not the norm. It just happened to be a byproduct of the the city that I was living in at the time. And I never really wanted to be a programmer, but man, that first program, we were supposed to write a program to go do 99 bottles on the wall. And I was like, I didn't want to sit here and watch this thing count down from 99. So I made it where I could put in an input and told it how many times to count down and made sure it got it right and then called the teacher over, put in 99 and let us let it read away. So what was the program you wrote at 12? Just curious.
• 8:15
  Speaker B: Most IT leaders don't have an Internet problem, they have a management problem. Multiple locations, multiple providers, multiple bills, different contracts. We help IT leaders make Internet boring again by consolidating visibility, billing support and monitoring without changing providers or forcing upgrades. If nothing needs fixing, we will tell you that's vendor neutral advisory. Just go to you've been heard.com and answer the seven questions to simplify, streamline and save.
• 8:43
  Speaker A: Well, my first computer was a Texas instrument TI 99 4A that plugged into a TV and used a cassette player for storage. That kind of ages me a little bit. You know, I was learning the programming language was basic at that time and I was a kid, so I was looking at all these video games that came out, Asteroids and Pac man and different things that was coming out. I didn't even know if Pac man was out then, but definitely Asteroids was out. And so I was just trying to write some computer programs to get a spaceship to fly across the screen. And so it was nothing super business oriented at that time. It was more just how do I make a game or something. And so I made a lot of text based games where I think those were more common back then than some of the graphics where you'd ask a question, get a response and go somewhere and do something. And so it was a lot more of that type of aspect of programming. So it wasn't so much business when I was 12. When I was 16 I did have an opportunity at my high school to get into some cooperative education and go take some classes. And I started learning RPG and COBOL when I was 16, 17 and 18 type of thing. So I had several programming languages by that point in time that I was learning and getting familiar with.
• 9:58
  Speaker C: Okay. Although for me I walked away from that stuff early and didn't return until almost my 30s. And then now I've been doing it for a quarter of a century but sounds like you stayed with it throughout that. I'M interested because I've never really done much with them except utilize their services. What were some of the fun challenges that you ran into working at the car rental? What was the industry thing that you guys were trying to solve?
• 10:29
  Speaker A: Yeah, I would say that the car rental industry is a little bit unique in some ways. It's very popular and common when you think about it. But from a software perspective, it's not like you're renting a VCR or something video like they used to. And so there wasn't a lot of software out there. So you had to go out there and really they wrote a lot of their own software through that. I was actually hired to do some of the pricing engines. That was one of my very first projects for them. And if you think about ren rental cars and again, Enterprise, when I came on to them, was really heavy in the insurance replacement area, not so much the airports. And it was a much smaller company when I joined it. And so they were focused on how do you price and keep track of all the pricing agreements for all the insurance companies that they had arrangements with. And then usually on those insurance arrangements, if you're in a wreck, you take it to a body shop, your insurance pays for the rental. But they always come in and say, well, we can add these other things on. And so there's some of those charges that the renter pays and some that the insurance company pays. So then you've got to split that bill. Some of the bill goes to the renter, some of the bill goes to the insurance company. So keeping track of all of those different rates and pricing was pretty complex. And so that was one of the first projects that I worked on was how to price out, keep track of all those different pricing mechanisms and allow them to quote and put in different types of pricing strategies, strategies for whoever they were doing business with. And then they bought Alamo and National, got more into the airport market and things started getting mobile. At that time that was about the dot com time. And so then it was how do you get off of what we were doing and get into the Internet and reservations and the more modernization mobilization of things. And they've even changed their name now to mobile on that mobility because it is a lot more about being mobile.
• 12:22
  Speaker C: Yeah, because of work, I'm traveling on a regular basis like every other week and utilizing the mobile apps to get the rental and doing all of those things. And yeah, I could be doing it through the website, but now I also think of the complexity of what you started on and where it's gone to like today. Because now it's like that surge pricing and the dynamic pricing based off of demand and all kinds of other influences. If they know that there's some kind of an event happening and they can jack, especially if they know the event's coming way before then, they can start to ramp up those prices and do so much more. I too have to deal with the pricing and the storage utilization and pass through additional charges and stuff like that. That alone, without the dynamic pricing and pieces of it can. Yeah, it's a heck of a puzzle to solve.
• 13:20
  Speaker A: Yeah. And it's one of those areas where governments like to tax things too. So there's all these airport surcharges and taxes on rental vehicles. And so you've got to keep track of all of those. Right. So it's all the surcharges that go on, fuel surcharges, whether somebody's got to
• 13:37
  Speaker C: pay for the fuel or whether they're just going to return it or. And then. Yeah, dynamic taste. Oh, and then with the example that you started off with, if the repairs start taking longer than the extensions and tracking all of the extensions and what's allowed, what's not allowed, when does it change? Having to revisit it, Everything. All of those.
• 14:00
  Speaker A: That was a fascinating business. I learned a lot about the business and then I learned a lot about technology and changing and being adaptable. I mean, they were constantly adapting and changing to keep up with technology and that was really what enabled them to grow fast and to become the large dominant player that they are in the market today.
• 14:19
  Speaker C: I'm jumping over to your LinkedIn page real quick to look for it, but how long you've been with Renoko for? Not a super long time. So you're still learning some of that one, aren't you?
• 14:31
  Speaker A: I'm still learning the business and there's. Well, I feel like I know a lot. I'll go into a meeting and then I'll hear something like, well, I didn't know that. And so there's still opportunities to learn with it. I've been there probably about 10 months now or coming up on 10 months. And again, it's been a great place to work. Really enjoy the people there, what they've got going on. I can't say enough good things about the people there. I was really shocked and surprised by that piece of it.
• 15:00
  Speaker C: That's awesome. I have gotten to a point in my life where the culture of the organization is almost as valuable as the salary that I'm getting.
• 15:11
  Speaker A: Yeah, definitely. Because you're going into work and you're spending at least 40 hours, and a lot of times, if you're in leadership and management positions, that could be 50 and 60 hours a week. And so you need to enjoy the people in my mind, that you're working with and feel excited about going in and helping them. And for me, it's a lot about those people and those relationships. And it's a lot more exciting to go into work if you're energized by the people that's there than if you're trying to dodge the people and not interested in them. So having that healthy culture, I think is a very important thing in today's world.
• 15:47
  Speaker C: So with the leadership experience that you've had and a desire to work at an organization like that, then assume probably pretty safely that that's the type of culture that you also try to build within your department or your area of influence. Talk to me about some of the challenges that you've run into throughout your life doing that, like who were or what were some of the struggles that you overcame or learned from while starting this journey into management and dealing with that.
• 16:21
  Speaker A: Yeah, and I'll start with some mistakes I made real early on because that's really helped shaped who I am today. Early on I used to think, and I still am very logical. I probably too logical at times with things, but I remember doing an assessment and logically thinking through it and coming up with this is the best technical solution. And I was convinced it was right. Talked to the business about it and it did not go well. And it was because while it was the best technical decision and may have had the best technology, it just didn't fit the business needs. And I didn't understand that piece of the value of people being able to understand it and use it. And just because it can understand it and use it doesn't mean that a person that's in the business can understand it and use it. If they can't understand and use it, then the system's no good at all with that. So I learned very early on that it's not all about technology. The people is important and the people who are using the systems are critically important. If they can't use it or if they put bad data in the system, you're going to get bad results down. And it doesn't matter how great the technical solution and technology is, if the people that's using the systems can't figure out how to use it or they're trying to work around the system because it doesn't fit their business need because it's been configured or set up wrong. And so I learned very early on that understanding the business, understanding the people that's going to use the system and coming up with a simple solution is better than a complex. And I think sometimes in it, we have a lot of intelligent people that are very logical and they want to build very complex systems. And the more complex it is, we kind of hang our hat on that, of, oh, look what I did. I wrote this really super complex thing and 5,000, 10,000 lines of code, and it can do all these crazy things, but if the business can't use it, it doesn't matter how complex it is. And so I've learned over the years that simplicity is better. I've learned over the years that understanding the business and what they need to do, not over designing it, not overthinking it, is important. And so a lot of the challenges in what I work with today, with people, is how do we build simple solutions and not overcomplicate it? How do we listen to what the business is saying and just try to deliver that and not all the bells and whistles and fancy things that we are tempted to put on to it. That just takes more time and then creates more problems, more things, breaks, makes it more complex. And so a lot of times I feel like the simplest solution that meets the business needs is the best solution.
• 18:56
  Speaker C: And that touches in on some of the methodologies of current methodologies today. I mean, I know I too suffered from a lot of those same problems of wanting to add in all of the things or to create all of the protections into it, because it might break if they do this or might do this if they do that. And so suddenly this tiny little thing turns into the huge monotonous project that's going to take a year. And typically by the time I delivered anything, the user has moved on and has gone to a completely different problem. And they even forgotten that they asked you for help. And I got introduced to the term gold plating as I was working with project management. They're like, quit gold plating stuff and just focus in on the request. But I also think of like the Phoenix project and how the Phoenix project, how they talk about changing from that monolithic waterfall approach to the Kanban and the Sprints and the DevOps operations and yeah, the minimal piece, minimal deliveries with rapid prototyping or rapid advancement.
• 20:10
  Speaker A: Yeah, that was something I got involved in real early in the. When it was first coming out, before it was even called Agile we were doing a lot of rapid and extreme development where I got exposed to some of that and really enjoyed that because again, when you start getting things in front of the business sooner, then you can start getting feedback, then you can start adjusting. And so I'm a huge fan of rapid development techniques, whatever model and methodology you want to throw on it. Agile Scrum Kanban I mean, there's a lot of different flavors of things out there, but in all of those, if you can really focus on how do you keep it simple. Start with something small and manageable and then as soon as possible get it in front of someone so you can start getting feedback. The earlier you get feedback, the more you can start working with them to build the solution that they want. You're not as tempted to over engineer it because you're constantly getting that feedback. And I just find that it also helps build a more unifying team when people are working together versus going off and forgetting about it. Because the business forgets what they ask for too. They forget the requirement if they're not involved with it. And sometimes it's not that they forget, it's that the business changes. And a lot of times the business changes more rapid than I think the IT people understand. And so as the business is changing, you know, a requirement that existed three months ago might be different now because the business has changed over those three months. And if you take longer to three months to build something, there's a greater chance that the business is continuing to grow, to adapt all the time for sure.
• 21:38
  Speaker C: And the problems that they have are growing and adapting. And if we're not delivering fast enough, then they're solving it with whatever tool set that they have in front of them at the moment, which could be complete policy changes and modes of how they're accomplishing goals.
• 21:55
  Speaker A: Well, and that kind of gets into the whole AI space today, right? And this is something that I've seen with IT strategies. Even while we're doing agile, if we're not using AI, someone out there in the business can go out there and with a prompt, very easily and quickly create something, something, and it will work. Now, it may not work great and it may not scale, but it will work. And so they can get a working solution in just a matter of hours. That used to take months to get. And so that's what I'm seeing more and more is that if it doesn't respond fast enough, if the business doesn't feel heard, then the business is going to go out and use AI and they're going to develop their own solutions. And at some point they ask, well, why do I need it? I can go out here and use one of these AI programs and turn around and get something. And you hear in the news all the time companies laying off lots of people because they've got AI. So I think that's a real issue that's kind of in our environment today.
• 22:52
  Speaker C: For sure. There's that aspect of it. And you touched on a really important part too and that's the scalability. So the scalability, the shareability or the usability across multiple people, many of the one off users don't really understand what or why or what needs to happen to do that in an enterprise setting. I was talking with another and like I always point to cell phones and mapping or routing. Routing for an individual is easy compared to routing for an organization who wants repeatable, exacting and manageable and measurable routing so that they know did they follow or did they not. Because an individual, you tell me a path and I'm like cruising along and oh, that guy's going too slow. I'm going around and I'm just taking another path and the routing adjust. So a lot of the individuals don't understand what it takes to make something enterprise worthy. So okay, I'm interested. Where and how does AI apply to getting coffee into my Styrofoam cup at the corner store?
• 24:07
  Speaker A: Yeah, and I think it's making everybody more efficient and empowering people. And I'm a huge proponent of AI. I've been using it at least for a couple of years, probably longer than that, in certain capacities with it. So I'm not against AI. I think AI is something that we should be embracing. I don't even know that I would hire someone in technology if they're not using AI. I feel that strongly about it. But it's one of those things for me and you were talking earlier and to me the challenges with citizen development using AI is the scalability, it's the security and it's the supportability. Because when it breaks, who's going to support it? If that person that developed it leaves the company, how do you support it? How do you hand it off and do something else? And so a lot of that comes down to I think there's some responsibility that it has for how do we empower people who want to use AI to use it responsibly. And so one of the things that I'm working on at Renoko and we're not fully there yet, I mean this is a journey and we're still getting into it. But how do we use AI for citizen development where IT comes in and partners with them to help them build secure code, stable code that's scalable, that we can support. And a lot of that comes in with governance and best practices and coming up with standards. And how do you put a huge human into the mix so that you're not just relying on the AI? And so I think that's where IT can come in and really play a part for how do we educate people, how do we come in and partner with them? How do we learn that technology and use it ourselves so we're comfortable with it and we don't feel threatened by it, but that it's something that's a part of our daily lives? And there's not a day that goes by that I don't use AI in some capacity where I'm using it to write job descriptions faster, to write documentation faster, to review contracts or whatever else is out there. And hey, put this in plain English for me so I can understand it. I mean, there's just a lot of things that IT can do. Summarize these documents, find the holes in these things, cross reference all of these documents. Tell me what's the difference between those. So it's one of those things where I think AI can do a lot of good in the organization. And how do we empower our employees to use it both on the IT side of the fence as well as on the business side?
• 26:29
  Speaker C: Yeah, man, you're hitting all of those bright things. Some of those challenges that I've run into is the understanding of it too. But actually you made a point that I had to tap my forehead on and to try to remind myself. I'm in the process of interviewing people that are coming in. And like we spent an hour talking to somebody this morning and not once did we ask them their comfort and or use of AI. And I was like, you're absolutely right. I need to be checking with all of my inbound people, what do they think? Because I know teams that are afraid of citizen developers because they're afraid that they're going to end up owning it and they're not there while it's being created. They're not there knowing what the objectives are. All they know is, hey, here's this pile of vibe code. Figure it out.
• 27:23
  Speaker A: Yeah, and what if we swap that? And this is kind of where I'm approaching, right? What if you swap that around? What if instead of the citizen development being afraid and saying, I'm not there. What if you were there? What if you were having daily or weekly standups with the business while the business was developing it and you were reviewing the code with them and telling them and asking them to put design documents before they go out and prompt so that they're architecting it? Right. What if you started teaching them how to be more efficient at using the AI by coming up with an architecture and prompting it so that it produces better code and then being part of the code review? Then you're not getting something that's handed to you that you weren't a part of. You were part of it all along. You were partnering with them, guiding them through that process instead of just getting handed something and like, what do I do with this? Which I understand why anybody would be frustrated with that. I mean, right? Nobody wants to be handed something that they have no idea what it is and then be expected to support it.
• 28:19
  Speaker C: Yep, for sure. And actually, that's a great idea. I love the way that you're approaching it or thinking of it that way. We're approaching it and we're trying to do a little bit more of the controlled piece of it and trying to set it up where, okay, let the citizens and developers come up with the idea. Maybe let them play and try to do proof of concept at an individual level, but not at any level of scale. As soon as it wants to go into scale, then we're like trying to grab it and pull it to us versus working with them. One twist that I would make to what you said would be that to have that design phase that work with the AI for that work with the AI to build out the architecture, to build out the design, and then feed that design back into it to help then generate the code and the other things about it, because that helps instill that. That documentation piece of it that I know my team and many of the people around me, we're IT people, are not always the best at generating documentation. We just want to get in there and start doing. I don't want to sit here and write a document. I'll sit here for hours trying to perfect a paragraph or a chunk of code. But try to get me to write three paragraphs on why I'm making that code. No, thank you.
• 29:38
  Speaker A: Well, I think that's where AI can come in and help, because AI is great at generating documentation now. You have to proof it, you have to verify it. You can't just always tape it for what it says. But it's one of those things where I think there's a huge opportunity there, especially the people that don't like to document. And I'm one of those people, right? When I was writing code, I hated writing the comments and the documentation. That just wasn't challenging for me. I wanted a challenge and that wasn't challenging. And that's a part where I think AI can come in and really help produce some of that documentation, produce some of the architecture. I still think you need an architect that's looking at the architecture that the AI generated to make sure it's solid, scalable architecture. Because I'd seen flaws in what AI generates when it goes out and does things, but it can also find mistakes in things because we make mistakes. And so being able to leverage AI to validate our work as well as us validating what the AI is doing, I think is key.
• 30:33
  Speaker C: Leveraging it in the everyday stuff exactly like you're talking about. I had somebody send me a resume the other day for an industry that I have no experience in. And I'm like, okay. I looked at the resume and I'm thinking to myself, this is kind of lacking a little bit. And then I asked the person, hey, send me a, a link to the job that you're applying for. And then I took their resume and that link and I said, hey, AI, compare these two, make some suggestions, make some recommendations. What can we do to make this resume better fit for that job? And it came back with some wonderful answers. I mean, it radically shifted their thinking on how to produce that resume. And if you let it, you can let it write the resume, but you better proofread it. Because that reminds me, I had a coworker create our AI policies using AI, and one of the things was that anything generated by AI must be notated as being generated by AI, which these documents did not have. And he sent those documents out with, as far as I could tell, without reading any of them. He just, hey, make policies about this, make these ones and those, and. And then just forwarded them on. It was like, come on man, you have some fiduciary care in this.
• 31:54
  Speaker A: Yeah, you've got to watch it closely. And it's not always right. I mean, there's been times that it's giving some bad answers to me and I'll go back and challenge it and oh, you're right, it's amazing how polite it is sometimes while it's wrong on those things. But you do have to validate it, pay attention to it. I always think of it as an entry level person coming in and doing work and it can do a lot of work really fast for you, but you've got to validate it because every once in a while it makes some pretty big mistakes and if you just this cursory look at it, you might miss something really important. So again, one of those things I'm a firm believer in a human in the middle of it and again, I kind of see it like an entry level person to a job that while really smart and really good and can produce a ton of work really fast, every once in a while it just goes way off on the wrong path or puts something in that you just don't want in it.
• 32:41
  Speaker C: Yeah, it's also very self confident and it's very assured that it's right because I've had times where I've had to push it back and it doesn't just, oh, you're right. No, I reviewed and I'm sure that, that I'm correct. I'm like, no, and here's the proof that you're not. Oh wow, I hadn't seen that yet. That's actually one of the first things I remember about like OpenAI. Oh, this model has only been trained through 2020 and it's 2023 so you can't ask it any questions about anything in the last three years. And, and if they're not trained up to very recently, all you gotta do is tell it, okay, go search the Internet, go look at these things now and then make a decision.
• 33:23
  Speaker A: But yeah, this is just in my personal life I use AI too. And so I was doing some investment things, just having IT do lots of research on investments and it said a company was private that I knew just had an ipo. And I'm like, well I know it's not a private and stuff. And I said they just had an ipo and the model came back and it was Claude and it came back and said, oh, I'm only trained until May of, of 2026. And that happened after May of 2026. So it's on the Internet and stuff with that. So it is important to know what models and to what time frame they go because you may need to go out to the Internet and do searches depending on the recency of something.
• 34:01
  Speaker C: So yeah, definitely pointing it at correct data sources or relevant data sources helps a lot too. So still curious how is the organization or you just starting the things for the organization and it's still got a ways to go because I'm curious what are the big Ideas that the CEOs coming to you and saying Hey, I can do this for us, make it happen. Because I know they've walked into my office or I've been around and they've walked into the office and told me that kind of statement.
• 34:33
  Speaker A: Yeah, the CEO is really great. I enjoy working with him. He thankfully doesn't come in and tell me exactly what to do on those things. But he hears things, right? He goes to conferences, he hears things and he comes back and shares them and stuff. With that, I would say we're still in a transformation journey at Renoko. And again, I came in 10 months ago and they had some older technology and we're still working to replace some of that technology that's been around for a while. And so as we worked it to modernize that, we're kind of in this really weird place where we've got some older like on premise servers and on premise technology, just a little bit of cloud and we're moving to where most everything's going to be in the cloud. But we're also in this great time because as we're doing this, we know where AI is at. So everything that we are building as we try to modernize is being built for AI technology. So as we're going through with the new ERP system, we're working with an ERP system and how can we plug AI into that from the very beginning? Not something that's an afterthought. As we're working with new CRM solutions, we're plugging AI into it from the very beginning with that. So we're at the place where as we're modernizing our systems, we're just at that right moment where we've got the opportunity to build these new systems with AI in mind. So that AI is not an afterthought or built on something that's already been around for three or four years before AI was taking off. So we're able to kind of reframe that. And this is probably through all companies, right? There's some people that are really embracing AI. They're going on and doing it personally. There's others that are kind of afraid of it or don't know how to start or what to do with it. And so we still have that mindset. We're working on how do we give every employee AI in the coming months. And so we have a plan that's in place where we can do that. And so we're going to be able to release AI to every employee who wants it. And it doesn't matter where you're at in the company, if you've got a office account with us, we're going to be able to give you AI so you can leverage and start doing something with AI. And so again, that's not something that we're quite ready to launch yet. We've got to do some licensing, some other things and get some things in place first. But once we get to that, and it'll be coming up here and very quickly in the next two or three months, I believe we'll be in a position to do that. We'll be able to give that access to people. Now, that doesn't mean everybody's going to. You start using it right away. You're still going to have those people that don't know what to do with it, don't know how to start with that. So that's kind of where we're at on the journey. Again, it's a journey. We're not there yet. We've got some people that are certainly leaving, leading and pushing the way. I've got some people that are probably way smarter than me in the AI space. And I'm challenged when I talk to them because they're always thinking a little bit further. And while I think I'm pretty advanced that I talk to somebody, I'm like, wow, that person knows even more than I do about it. So that's always a fun place to be.
• 37:33
  Speaker C: Yeah, it's always a fun place to be as a leader over a portion of the organization and have people show up that are further afield in areas. But we are supposed to know everything about all things too, so it makes it somewhat of a challenge. Although you've got innovation, so. Versus all things it.
• 37:53
  Speaker A: Yeah.
• 37:54
  Speaker C: One of the things that we found or have been surprised by is as we're educating the upper echelons and then getting them started on it, because they're going to be our first line of defense or our first realm of teachers. They will be working with their departments and their people. We're finding that they don't necessarily understand exactly AI in general. They're using it as the dashboard creators. Yes, they're trying to get through all of the information down to the specific things that they want, but they're just generating reports and dashboards from it versus all of the other potential solutions. It's needed and it's wanted and it's useful for them. But I. I've been surprised by that. Have you run into anything that's surprised you with the ones that are out there doing it, or are they the ones that are out there doing it. Are they vibe coding away and building things?
• 38:48
  Speaker A: Yeah, I think we've got a mixture of people. I've got two people specifically that I'm thinking of, and they're doing a lot of coding. And so, I mean, they go out and they leverage it heavily for coding and doing some solutions with that. But one of the things that I've noticed, and this is just in general, over the last couple of years, when people get introduced to AI and start using it, you tend to use it in one specific place, right? And so if I'm exposed to it in chat and how to have a conversation, then I start having a conversation with it and I get really good at it. And for me, AI is just having this conversation, this chat piece of it, right? And that kind of gets into the chat side of it. You got other people who get into it, and hey, I want to use it for coding. And so then they use the coding piece of it, and then what do they do? They use it all for coding. Right. And so what I find is there's people that, as they get into AI, that they get into this one little slice of the puzzle, or the pie, if you will, and that's where they stay. And they don't realize how powerful AI is in different dimensions and what it can do. And so that's one of the things that I think is an opportunity for a lot of companies is how do you create a forum, some type of an area where people can share different things that they're doing that might help someone open their eyes to the, oh, I didn't know it could do that type of a thing because a lot of times I think we just get so custom. This is what I do, right? I come in, I talk to it, and that's all I do. Or I use AI to summarize my emails. I got my summary. Okay. I'm using AI because it summarized all my emails, or I'm using AI to write code, and that's what I do with it. And so I think that's just a tendency that's out there is to kind of get locked into this, this little one segment of what AI means to me, and I don't think of those other things it can do.
• 40:37
  Speaker C: Yeah, well, and as you describe it that way, I'm thinking of Android versus iPhone, and once you get started on one and it's really hard to switch onto the other one because I get so used to thinking of this is the way you do stuff. You mentioned something earlier about applying AI to these enterprise systems from the jump. But it also makes me wonder about the enterprise systems that have been existing for a while and their blending of AI into it. So it sounds like you're taking standalone AI and bringing that into and leveraging it with the applications versus kind of leaning into the AI tools that, that the provider may have given you.
• 41:27
  Speaker A: Yeah, I'm a huge proponent of diversity and so you can certainly go in and use the AI tools that the vendor provides you inside their solution. But a lot of times you're locked into that solution and then you have to have that solutions, you have to have that license and then they charge you for that license. Almost everybody that starts off, there's a few companies, they introduced AI and AI was free and it was free for a month and then all of a sudden, oh, now you got to pay a premium for it. Right now it's a SKU and an add on. And so I've seen that happen with so many different vendors out there where it's an opportunity to make money. And how many AI technologies do you need? Right. I mean, if I've got an AI in my CRM and an AI in my ERP and an AI in the office solution and AI over here, pretty soon you've got all these disparate AI systems who don't know about each other. But a business runs as a cohesive unit. So what I'm looking for, how do I come up with using an independent AI that can plug into multiple tools so I don't have to have all of these separate AI things and I can kind of leverage information across what if I want to take use my AI to get information from my ERP and my CRM. If I'm locked into a CRM with one AI and into another one, it gets really complex. And a much simpler solution. I come back to simple. Right. A much simpler solution is I've got one AI that knows how to reach into the ERP and knows how to reach into the CRM and can pull that data together and do something with it and give me an answer.
• 43:01
  Speaker C: Yeah, makes sense. I have yet to really run into the agent agent interactions and how to leverage those and what to do about that. And then it seems like some of these organizations are doing what they can to make sure that, that their tool is better than what we can apply ourselves. But one of the things that I'm thinking of is that trying to bring all of the knowledge into a central location and then put the AI agent in the middle of that so that you can ask any question like you're talking about for the full organization across any of the domains. But we got to be careful too. We've got to have the layers of, of authority and role based access and all of that fun stuff too.
• 43:48
  Speaker A: And I think that's where the MCP and I think this is one of the things that's really changing things, is that model context protocol where you can go out there and for the companies that are starting to build that into their solutions, and a lot of them are where you're able to use MCP to set up your AI to reach into those organizations, you certainly have to make sure you've got the right security, the right permissions to see the data. And that's not always intuitive. So again, I think that comes back to it's role of making sure that as we're building these solutions that we're not going out there and just releasing data to anybody that has access to that AI. And how do I go in there and secure that AI, making sure that the person using the AI, I know who they are, I know what permissions they have access to. So when they go out to get the data, they're getting the data that only they should have access to and not someone else's data.
• 44:41
  Speaker C: Yeah. And one of the other things that we're worried about and we're keen on actively protecting is also not only the ability to get to that data that they should be able to get to, but also making sure that they can't cause ripples in the pond outside of the areas that they shouldn't. And also protections in to make sure that it's not done en masse without major checks. So I was talking about people creating dashboards with it and so they create a dashboard of all of the things that we've done and oh well, you know what? I don't want these, remove these from this. And suddenly the system interprets that as delete these records from the source system. Because it could and it wouldn't necessarily communicate that until, hey, I removed them. You told me to. You had permission to.
• 45:35
  Speaker A: Yeah, and I think that's the danger of, I'm going to say over relying on AI. It's a very powerful tool. But at the same time, if you make a mistake, and I'm going to say we're the ones making the mistake because we're the ones telling it to do that, we're the ones not putting in the right controls. If we go out and make the mistake of saying something and it executes it and all of a sudden it's deleted all the data. Right? It's on us. I can blame the AI all I want to, but at the end of the day it's my job to fix that and to make sure of it. And so I think that's where we have to take responsibility as the humans in the equation here to what is our responsibility. And our responsibility is to make sure when we're building these solutions, we're putting the right controls, we're putting the right checks and balances in there with that, and certainly making sure you've got backups and the ability to restore when you're doing things to protect. And I'd say the other part of that's testing it. So much of what I see happen sometimes with AI is just instantly in production and we're not thinking to put it into a test environment. And how do I test this thoroughly before it goes into a production environment? And it's just so much easier to go build it into a production or I'm doing this and I'm not even putting it through the proper test controls. I mean, that's not anything we would have done years ago, right? I mean, if I'm building software, I'm not just putting it instantly in production. Hopefully, I guess people that do do that, but best practices world of software development, we should be testing it and I think we should take those best practices that we've done in software development and apply that to AI that when I'm building these AI agents and building these things, it's go out and do massive amounts, especially if they're doing updates on data or changes or insertions. I should be testing that. I should have a testing environment where I'm running that AI, running the code that's been generated from that before, just giving it live access to a production set of data.
• 47:30
  Speaker C: You caused me to have yet another little epiphany. And yes, we should be testing all of these things. And one of the challenges that I've run into in testing is the volatility or the velocity of some of the data that so like in the test systems, they're always quiet, they're always like, here, let me put in an order, let's see, let's follow that order from cradle to grave. And but it's in such a perfect world that it, you don't get to see what it's like when that system's under load and you're putting in that order and what are the times. So I, I, the epiphany was, hey, I wonder if I can get AI to help me design playing back a data stream while doing testing.
• 48:15
  Speaker A: That's exactly what I was thinking as you were talking is, wow. Because again, I was kind of getting hit with it too when you were talking through that, wow, what could we do with that? Because I hadn't thought of that yet. Right. So that's the fun part of these conversations. Right. As you talk to people about what you can do, you start getting new ideas and you start saying, well, I didn't think about applying AI to this particular area. And so that's where I really of like having conversations with people about AI because as you hear from one person, you get ideas that you can now take and do something with that.
• 48:44
  Speaker C: Yeah. And it's back to your idea of or your how do we do this? How do we break people out of first use case syndrome and expand their knowledge, expand their experiences? How do we get them off of analog TV into the world of cable? And so one of the quick thoughts that I had had and am planning on doing as I get released to the general public within the organization is those lunch alerts. Just get the group of people who are interested and just say, hey guys, we're meeting today for an hour. Every. Anybody that's interested, you're welcome. But if you've been doing anything with it, bring your ideas, bring your successes, bring your failures. Let's just talk through them. And it's going to be that idea sharing just like we just had in what we just did. Because people start to go, oh, I didn't know I can do that. I'm allowed to do that.
• 49:38
  Speaker A: Yeah.
• 49:38
  Speaker B: And cry.
• 49:38
  Speaker A: Our company had lunch and learns all the time on the AI. I learned so much from those, from just people coming in and sharing. And sometimes people would share what they were doing in their personal life. I remember one person saying, I would tell it what I have in my refrigerator and then ask it for a recipe for they covered those items that I had in my refrigerator and it would come up with these things. And so I think just tearing those simple ideas gives you new ways and new insights on how you can use it. So I love the idea of learning from other people.
• 50:06
  Speaker C: Yeah, I didn't warn you. So I'm just going to drop it on you and then close out things after that. Thank you so much for sharing your experience, your ideas. But what do you think we as IT leaders will be talking about in 18 months that we're not talking about today?
• 50:22
  Speaker A: And I'm going to flip this around just a little bit. And. And everybody's talking AI today, right? And so I think that's the dominant thing. And I honestly think we're going to come back in 18 months and flip it around the other way and talk about what about the developers and the architects. Because at the end of the day, there's limits to what AI can do. And I think it's going to continue to get better and better, but I think we're going to come back and we're already seeing that there's already been some news articles where people laid off a bunch of people because AI was going to do it, and now they're hiring the people back. And so I think what we're going to find with the AI thing in about 18 months with things is we're going to come back and realize the value that a strong architect has. Right. I need an architect to validate the architecture that the AI is generating. I need a strong developer that can validate what's going on that the AI is doing. And so I think we're going to come back to basics, and we're going to come back to the people. And who are those key people that we need to kind of oversee the AI that we're now starting to rely on. And again, there's so much focus on the AI. I think we've lost track of the people. And I think we're gonna come back around and realize those people are very important to the process. We need those people overseeing, guiding it.
• 51:35
  Speaker C: I'm gonna have to say, I think you're right, George. I think you're on the nose again. Thank you so much for your time today. Truly appreciated the conversation.
• 51:45
  Speaker A: Thank you very much, Mike. I appreciate being part of the community. It was an honor to be on today, so thank you very much. Really enjoyed the conversation. Sam.