Using AI as a Thinking Partner, Not Just a Task Tool w/Eddie Irvin

AI can make you faster, but speed is not the same as better thinking. We’re seeing teams bolt AI onto yesterday’s workflows, celebrate a few automation wins, and then wonder why the big strategic breakthroughs never arrive. The shift happens when AI stops being a task tool and becomes a thinking partner that challenges assumptions, spots blind spots, and helps leaders make clearer decisions under uncertainty. 

I’m joined by Eddie Irvin, AI strategist and founder of Nashville AI Advisory. Eddie breaks down what “thinking with AI” looks like in real life, including why many systems default to agreeable responses and how to set expectations so the model critiques instead of compliments. We unpack practical prompting frameworks like the roundtable method, where AI generates multiple expert viewpoints and competing options so you can evaluate tradeoffs with human judgment rather than taking the first output as truth. 

We also get concrete about implementation. Eddie explains why context is the secret ingredient for high-quality AI results and shares a simple tactic: ask AI to interview you first to capture the details you forgot to include. From there, we explore use cases across manufacturing and industrial operations for diagnostics and root cause analysis, then zoom out to business strategy where AI can function like a boardroom simulator and meeting partner that helps prioritize risks, constraints, and next moves. We close with the risks of over-partnering, including overtrust and mental atrophy, plus how to build a healthy AI partner culture with training and clear boundaries. 

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Eddie Irvin LinkedIn: https://www.linkedin.com/in/eddie-irvin/ 

Nashville Advisory Website: https://nashvilleaiadvisory.com/ 

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Transcript

When AI Stops Being A Tool

Jim 0:05

For most of modern industry, technology has always been a tool. A calculator sped up the math, CAD sped up drawings, spreadsheets sped up the reporting. Every generation of tech made us faster, more efficient, more productive. But it never really thought with us. It never challenged our assumptions. It never pushed back and it never said, are you sure that's the right decision? And that's the quiet trap that many organizations are falling into right now. Because even as AI reshapes the landscape, most companies still treat it like a faster intern, something to delegate tasks to, something to automate the busy work, something to bolt onto yesterday's workflows. They're using AI to do, but not to think. But here's the truth AI becomes transformative only when it becomes a thinking partner, when it helps us explore scenarios we wouldn't have considered, when it surfaces blind spots we didn't know we had, and when it co-creates solutions instead of simply executing instructions, that shift from tool to partner is where the world revolution begins. And today we're driving straight into that frontier.

Meet Eddie Irvin

Jim 1:26

Joining me is someone who lives at that intersection of strategy, capability, and applied intelligence, Eddie Irving, AI strategist and founder of Nashville AI Advisory, who's been helping organizations move beyond the task tool mindset into a model where AI becomes a collaborator, one that expands human judgment instead of replacing it. We're going to be talking about what it actually looks like to think with AI, not just through it, why most teams are still stuck in automation only mode, and how leaders can build workflows where AI challenges, it critiques, it elevates the decisions that matter. Eddie, welcome. So glad to be here, Jim. For everyone, if you could do a favor and uh talk a little bit more about your background and also do uh uh talk about the organization you lead.

Eddie Irvin 2:22

Absolutely. So, yeah, I'm Eddie Irvin. I am based here in Nashville, Tennessee, and uh I've been a creative problem solver pretty much my whole life and got into tech about 15 years ago. Started with simple websites like everybody probably does, and eventually I got enough uh enough underneath my belt to then start tinkering with apps and kind of peeling back the layers. This is akin to when we were kids and we started taking phones apart or old devices and seeing what's inside, what's actually going on. I did that same kind of thing with software, and then started realizing how these things are built and what's actually going on behind the scenes that led to my app development career. And then in the midst of that, I pivoted to the planning part. If we're trying to build anything, whether it's software or AI automation or whatever it is, we've got to have a good plan. And so I realized that that's really where my zone of genius lies is connecting from the business owner's understanding or the leader's understanding of what they want to get done and their problem, and then mapping that into a technical solution. So, of course, when AI came out, my brain just about exploded and trying to figure out how I can harness this tool. And ever since then, I've been using it um a lot and trying to understand and flex with it and mold with it and shape with it to try to understand better uh how to how to play with it. And now here with AI Nashville AI Advisory, I advise and help companies really understand what they can do with AI as everybody is realizing, well, we've got to harness AI. But then the question comes well, what actually can we do? How do you actually use this thing? And that's where I come in to be able to facilitate that conversation and help people move towards building a solution that helps the business.

Jim 3:57

Excellent.

The Automation Only Trap

Jim 3:58

That is so critical because I think we can both agree that task-only AI, it's a dead end. It's it's it's going nowhere. And it's really a productivity trap. You know, the companies currently seem to be more celebrating, you know, the automation of AI, those wins, and and they're really missing the strategic breakthroughs that AI can bring to their organization, to their uh, to their enterprise. Why is that the case, Eddie?

Eddie Irvin 4:24

Yeah. Well, and honestly, when we first started with AI, it was not that great. It wasn't fantastic. And so we had to use it for more rudimentary things. Um, and as we're now getting better into new models, the fable just came out and it's it's you know, exploding everybody's head off that it's gonna thing so well, it's this is not gonna end, right? It's this is not the end of the road. The newest technology that is amazing for us right now will be old news in a year or three or five. And so now, as AI can actually help us think through business problems, can help us see things that we can't see, can bring to us a uh, you know, what I love to do is I love to do a round table with AI. Get me 10 different experts, have them think from all different angles and bring me some ideas so that I can think bigger. No longer are we sitting there with a blinking cursor and our own brain and just going, oh, I'm not really sure what to do. Or, you know, what we had to do is, you know, hire somebody who's got 20 years of experience to be able to help us think through it. Now, with the touch of a button, we can have, we can harness all of that power and be able to uh to use it. That said, we also need to be able to know what it is and what it's doing so that we don't just implicitly trust it. It is a tool that we use. It is not just, you know, this magical box. And so as we learn to use it better and as we facilitate that conversation with AI better, we start to see more of what it can do. And so this is really where people, as people learn more and more that they can go to AI to ask these deeper questions or to dig in, that breakthrough and that that uh that light bulb uh going on for them will then break into having more people uh use AI at that level.

Jim 6:03

Exactly. And there are still a lot of companies that I encounter, and I'm sure you do as well, that still kind of look at AI and it's you know, it's that initiative where they just go and they checkbox it off and everything. They're not really fully comprehending and understanding the competitive advantage that they have in their hands when they're incorporating uh AI. A lot of them are looking, oh, well, this is going to be able to solve the repetitive task. And really, that's that's really a misuse of it because it never really improves, you know, judgment that's utilized by uh leadership or managers. And then also when it comes to decision making and in future innovation, it's kind of uh over-reliance, really, of you know, like a what I would call the calculator effect, where you know, they're relying a lot on outputs, but they're really not understanding the reasoning. Why is that the case, Eddie?

The Roundtable Method For Ideas

Eddie Irvin 6:59

Yeah, let's talk about this. So, this idea of it being a tool, you know, if you give a pot or a pan to somebody who's never cooked a day in their life, they might boil some water, you might get a grilled cheese, you're not gonna get a five-star meal. And the same way, let's flip it, let's give a a world-class chef the worst pot and pan in the world, or something that's just kind of rudimentary, you're still gonna get a five-star meal or at least four stars, something fantastic because of all of the knowledge that they bring to the tool. And so there's kind of two things happening. Mainly, if you've got domain expertise, you've got domain knowledge, but you haven't brought that to AI, just by playing with it and flexing with it and trying to get new things out of it, you're gonna start seeing in different directions. Everything I've learned with AI has been literally just playing with it, trying something new, having an aha, and then being able to put that tool in my belt for next time. Like I just said, that idea of using a round table, which I use all the time now. It started with me realizing that AI was giving me one answer. I ask it a question, okay, here's your answer. I started saying, and if I'm disagreeing with it, then I'm going back and forth. I'm not sure. And I love using AI the opposite way, which is fan out for me. Give me all these different ways of looking at it. And now let me choose. Let me see this whole Rolodex of information, this rainbow of information, and I will then be able to pick out what I want. And only after I had that aha and had that moment to try that technique, now it's a tool in my belt that I can use and bring forward with me. So if you're trying to figure out in your business, what are, you know, what am I doing? How am I doing it? How do I use AI to a higher level? The fact of the matter is you just have to start playing with it. But then as you play with it, you start learning more and more of what it can do. And then you will then start going to AI for those things that you didn't go to before. People going to AI for simple tasks, they're doing it because that's what they think it can do. And as soon as they understand more of how it can actually help them, and they can they have that kind of that learning, just even that just one moment of an aha, then they can realize, oh my good, I had no idea I could use this tool this way. And then that opens up a whole new world for them.

Jim 9:14

It really does open a whole new world. Let's talk about what it means to treat AI as a thinking partner and preparing for this episode and really researching uh you and a lot of initiatives and things that you've done related to uh this topic of AI as a thinking partner. Um, you know, I kind of saw a concept that uh it seemed to really kind of resonate related to the AI as a thinking partner. The first is that AI is a is a sounding board. Um, next AI is a scenario generator. Um, AI can be a devil's advocate, and AI is definitely a pattern spotter. Let me ask you, how thinking as a thinking partner, how can AI change workflows?

Give AI Context Not Just Tasks

Eddie Irvin 10:05

Yeah, that's a great question. I think the first thing that I'm that's coming up to mind when you're saying all this is realizing that all of these different AI tools are are kind of taught to act a certain way. They have certain guardrails, they have certain boundaries. Here's how you're supposed to act. And part of the difficulty of using AI as a thinking partner is a lot of these systems are built to pat you on the back. Hey, great idea. Hey, that's fantastic. Wow, I never thought about that. That's amazing. And this is what you want to break out of when you're using AI. You don't want pat answers, you don't want it to just pat you on the back. You want it to be a critical thinking partner. So you have to tell it what kind of suit to put on, what kind of brain power to put on, how it is supposed to think through and process for you. And so if you, as you're trying to use AI as a thinking partner, you want to be able to just establish the rules at the very beginning. I don't want you just agreeing with me. I don't want you just bringing me one line of thinking. I want you to bring lots of different lines of thinking. Again, let's think about what AI actually has. AI has the world's memory, not everything hidden behind a login or private data, but the idea of all this data that's on the internet, it can pull from all these different spots. So if you're asking about business consulting, for instance, let's say you're trying to grow your business, you're not sure what to do, you're not sure where you're stuck, whatever it is. It's got all this knowledge from all these different business advisors that have published blogs and videos over the years that said not all of them agree. It's not like there's just one answer from one business advisor and everybody else parrots that. No, it's that there's different schools of thought, there's different ways of thinking. So if we tell AI, listen, I want you to give me a fan, let's go all these different places. Now you're almost being presented this menu of like, here's how, here's many different ways as to how we could we could go about it. Some people are agreeing with you, some people are disagreeing with you, some people are half agreeing with you, and here's what else you could do. And then the other part of that too is to keep in mind that if you don't give it enough information, if you don't give it enough context, it won't be able to do enough processing that helps you uh really get through the problem because it doesn't have the ingredients for the problem. If I told you, make me a world-class menu and I gave you peanut butter, jelly, and wonder bread, your hands are tied, right? If I gave you the entire uh grocery store, now all of a sudden you can cook something up. So let's think about that in the context of asking for help with your business. If you haven't explained all the different ways you're stuck, where you want to go, where you're where you're uh where you're held back, where you're bottlenecked, who your team is, who you serve, the avatar for your customer, the different places you're online, whatever it is, all that whole recipe, that whole sorry, ingredient list of all the things that make up you and your situation, if you don't give that to AI, it doesn't really have that much to work with. It could still give you an answer, but it's gonna feel like AI. It's gonna feel vanilla. It's not gonna feel like the tool is actually doing a job. And this is another reason why people aren't using AI as a thinking partner, because they've tried just saying, help me with my business, but they haven't given it 10 minutes worth of context as to what's going on in their business. And so AI gives them an answer and they go, ah, you know, it wasn't really that great. And that's true, it wasn't that great. But the reason why it wasn't that great isn't because the tool is bad. It's because we left out the context. So anytime I'm trying to have success with AI and I'm trying to, you know, use AI agents more, use workflows more, have AI do more thinking and processing for me. My my first thought right after that is well, what does it need in order to be able to process that? What's the ingredient list that I've got to give it so that it can do what it needs to do? It can pass the bar, it can take these doctoral exams, it can it can do such high-level processing to be able to find and then kind of match the answer with the question. But if it doesn't have enough context, it's going to be stuck. So as we give it more context, give it more information, we are now uh winning. And the tool is going to just immediately feel better and smarter. And oh my gosh, because we helped it by giving it uh more ingredients.

Jim 14:21

Yeah. When you really think about it with AI, the human partner part of the AI partnership is requires really a mind shift, mindset shift from, you know, tell me an answer to help me think better. That's more of a truer form of a partnership.

Eddie Irvin 14:39

Yeah. Yep. And we have to we have to go to AI asking for that. Now, there are some systems that will kind of be pre-baked with that, or if you use a GPT somewhere else or you use somebody else's AI system, they may have already pre-baked it with, hey, you're a thinking partner, you're, you know, you're to do this kind of you're to do this kind of processing.

Ask AI To Interview You

Eddie Irvin 14:58

Um, there's another, there's another kind of tip or tool that I came up with a little while ago that has really been helpful to me that I've used over and over again. And that's when I ask the AI to interview me. I'm not really sure what information I need to give it. I just want to give, I want to give it information, but what should I be telling you? I can't even think of the question to answer so that it will have the right ingredients. And so basically, I kind of pause at the scenario. I want to do this, I want to figure this out, and I'm not really sure what you need to know, what you already know. So please come to me with 10 questions and after that, then do the processing. And so then I've got a punch list to just go through. I typically click dictate because I can get so much, so many more faster words out of my mouth. Um, it it types to me and then I dictate back to it. And then I'm able to just drop all this insight very, very fast. Uh, one side note about that too, if you're ever using the audio version of Chat GPT, it tends to give shorter, more pat answers because it's meant to be more of a conversation. Versus if you use dictate and then you use chat GPT like normal where it types back to you, now you can get a really, really comprehensive, uh detailed answer. So all AI isn't the same. Imagine it's kind of going to different modules inside itself, and some are better than others.

Jim 16:11

That's a great pro tip. Thank you.

Eddie Irvin 16:14

Yeah.

Manufacturing Diagnostics And Root Causes

Jim 16:15

Let's move to real world use cases uh across different uh different types of industries or sectors. The first I want to talk about is on the manufacturing and industrial operation. Really, where we see where AI needs to go is more in helping in within manufacturing operation, is more the diagnostic and also looking for root causes. Um, they can be utilized to really explore multiple failure modes. And when you think about AI challenges assumptions in anything from you know how machinery operates, um, you know, running at peak efficiency and everything like that. So when it comes to uh manufacturing industrial operations, what are some of the um the let's say the top uh AI partnership opportunities um that could be uh for a company?

Eddie Irvin 17:11

That's a great question. So starting with this kind of thing, again, I don't have domain expertise in manufacturing, but here's the thought process. Sure. And here's how it helped a company do that. Basically starting from two different sides, right? We want a result, we want to have the magic of AI helping us, but what is it even gonna help us with? And on the other side, we've got a data set, we've got information, we've got inputs. And so I would start by just figuring out well, what inputs do we actually have? What can we actually feed into this system that will then give it an idea? Imagine you hire a detective to come in, a manufacturing detective, and they're the best in the world and they're gonna help you. You know, you lead them into the factory. Where would you be going first? What would you be showing them? Would you be showing them this one machine that keeps on messing up and is the bottleneck for entire operations? Would you be taking them to the C-suite because the company isn't growing and for some reason everybody in the C-suite keeps on getting pulled away and stuck? Where would you be taking them? And so as we kind of lead this detective into the company, you've got to start thinking from inside the company, where is my worst bleeding neck wound right now? We can't solve everything, but we can find a starting place. And that's typically what I'm doing at the beginning is what's my first handle? I need something to hold on to. I need to see a problem that is repeatable, that I can see it happening. And I also need to see the data that's going into it and the data that's coming out of it. And once we've got that, even if we don't know anything, if we don't really know much about what's going on, we can at least feed, again, feeding AI all of this information. Chat GPT, here's what's going on. I would talk to it for three or five minutes. Here's what's going on. This is what I see. Here's how it's manifesting. Here's the data that's going into it. I want you to watch out. This one piece of data is actually wrong, or sometimes it can be weird because of this other thing. Take a look at this, take a look at that. Try to kind of draw some conclusions here. And now we use the round table method or kind of the fanning out approach. Give me 20 different possibilities of what this could be. Get together a round table of 10 different manufacturing geniuses that all have that all kind of see it in a different way or all kind of have a different piece and part in this whole entire thing. And I want them to come with two ideas each. Number the ideas and give that back to me. Okay, so right away we've focused it. Right we go wide first. We're in the manufacturer, we're in this huge manufacturing facility. Where are we focused? Or okay, we're focused here. Okay, here. What are the pieces and parts? What are the ingredients? What's happening? What's not happening? What should be happening? What's wrong? Feed it, feed it, feed it. Get all this data in. And now, once we've kind of preloaded it with all this data, we've kind of put all the ingredients in the kitchen. Now go ahead and chop it up 10 different ways. Tell me what I can make from this. I put all the ingredients on the counter. You tell me 20 different things I can make with these ingredients. What do you think is going on here? And so, and we could also ask for priority. We could also ask for a leader at the top of the round table to kind of pick and choose the best ideas. We could see where those uh uh experts are aligned or not aligned. If we want to see kind of a map of like how certain uh maybe there's 10 experts and five of them are all thinking it's this one thing, okay, that's a great tool for me to be able to know what handle to pull on next or kind of what periscope to put up into a new world next to look around. And let's say that we find a kind of a starting place from that. We get the round table fleshed out, we go into that one very specific thing. We say, okay, I think it's this or this is what I want to work on. But let's let's imagine that we feel stuck. You know, okay, fine, I think that's it, but I don't know what to do about it. Okay, AI, ask me questions. I want to focus on fixing this this way. I like the round table. I like that idea you gave me. I don't even know what to give you. Ask me questions that can help me help you so that you can help me even more. Help me help you. I don't even know what I'm doing here. Help me help you. Tell me what you need from my mind or my team or my insight or my data so that you can start uh uh, you know, solving what this is. And maybe this is that the data already exists and we just have to pass it on. And now all of a sudden AI goes, oh, if you just did this, then there you go. Or it's AI says, Listen, I need this kind of data. And we realize, oh, we're actually not tracking that kind of data. We actually don't, we actually don't have that data set yet. Okay, now we set up a watcher system, we set up a software system, we set up a logging system to be able to then track that. And then every day we're ripping off that data, we're feeding it back in. Is this what you need? Is there something else you need? Then keep in mind, too, this is coming from a point of view of somebody who, you know, isn't working with manufacturing all day. The idea is that if you're working with, you know, working in AI with somebody who's got the manufacturing brain and also has the AI brain, now you're heading forward even faster. But I'm Giving you an idea of if you really don't know what's going on, you really have no idea where to look. Anything above that, where you actually have an idea where you actually think, oh, it could be one of these five things, you're already much better off. And again, you can use the tool more focused and more harnessed because it's it's it's it's riding a horse when you know how to ride a horse. It's cooking a meal when you know how to cook a meal. For me, it's building software. When I know how to build software, I can say, nope, I want it to go this direction. Nope, you misunderstood me. I want you to do this this way because I I know I'm using the tool in a specific way. I'm not just saying I have no idea. Make sense?

Jim 22:37

It makes a lot of sense. I appreciate you going. You went through a lot of depth and detail on that, and the information's amazing. So, what I'm gonna do is I'm gonna switch gears.

AI As A Boardroom Simulator

Jim 22:45

I'm gonna talk about two other uh areas with you. The first is gonna be business strategy and leadership. And this comes down to the concept of AI really kind of performing like a board, someone on your board and kind of working in conjunction with leadership. You know, in what way can AI be, let's say, either a boardroom simulator or work in conjunction with other board members, almost like it's its own board member for a company.

Eddie Irvin 23:16

Yeah, that's fantastic. I actually really like this compared to the manufacturing example because we get to see how what we're mainly doing here is we're switching the data set. So if we're again, let's be in the let's be in the manufacturing company, but now we're up in the C-suite. We are not talking about logging for what temperature that machine is and why it bends the piece of metal the wrong way and what's going on. This is wasting a bunch of time. Instead, this is why is the company not growing? What do we need to have the company grow? Where are the constraints? What's costing us too much? Where's the friction? All that stuff that the boardroom members are thinking about. It's a different set of data. It's a wholly different set of data, still within the same company, but a different layer of data. So, again, here we're feeding it the right information. If I'm a boardroom member, again, let's let's just imagine we we pull in a detective board member. Hey, you're gonna be temporary board member today. Welcome, you know. Okay, well, I I'm a board member now. What am I thinking about? What do I care about? What do I want? It's different than the technician down on the floor with the actual machine. They want different things, even though they're part of the same ecosystem. And so there we are then feeding the financials, we're feeding, you know, the the friction points, uh, we're feeding the goals where we want the company to go. If we want to make our shareholders happy, where are we aiming for? Uh, and have we modeled out what that looks like? What what's the what are the top 10 things that could prevent us from getting there? Are those being attended to? Are we proactive to try to solve against those things that could pull us down? Are we going after trying to figure those things out, at least the top three, proactive towards ripping down those walls so that we can move forward? Whatever it is that then goes into that data set to be a board member, we then kind of feed it that. It's almost like we're making a play and we're kind of setting the stage. Here's here you are, here's what you're doing, here's your lines, here's what you need to think about, here's what you need to be. And that is a whole different world than manufacturing down on the floor. And that said, too, you know, I would probably say if we can feed it some of the some of the communications uh with the company, absolutely recording the board meetings and having all those go into it, uh, we could have a tool in the midst of a boardroom meeting. When we're in the boardroom meeting, having AI listen and be able to speak, or probably what I would do is I would just put it up on the uh up on the wall and have it basically taking notes of what's happening in the boardroom, like an organizer of what's going on and helping us rate and prioritize what's the biggest problems, what's the biggest friction points, what's the biggest things that we want, and how are we gonna go after them and basically just design prompts for different sections of that screen to be able to hear the conversations we're having along with all the background data we loaded into it. So it's not starting just in that meeting trying to figure out what's going on, but having two years of financials and understanding where we're trying to go. And then at that point, this is now where we get into the business of making AI better. Very often, it's not that we turn on the AI system and great, it works, and there we go. Instead, we're focused on trying to make sure that it has everything it needs, and then once we get it usable, we then can make it better. And this is really where a lot of AI systems uh fail is people don't realize that it's a living, breathing system. Imagine if you hired somebody, you brought them into your organization, and you said, Hey, welcome, uh, goodbye, go do whatever you got to do, right? They would say, Well, what you got to train me, tell me what to do, give me an example of what you want me to do. Even if they were fantastic at what they've done before, they still need context, they need to know where to find the files, they need all this information. And so what happens with AI systems is we build it, we build our best hypothesis, and we start using it as soon as we possibly can, fully expecting it to not be final, to not be perfect. But then in recognizing where it's not perfect, in recognizing what it's not doing that it needs to do or where it did it wrong, or whatever it is, there's always, you know, 80% it did a great job. 20%, this is the gold. What did it not do? Why did it not do it? What is it missing? What do we want for next time? And then we take all those learnings that goes back to the developer, the AI strategist, they build it into the tool, and now we have another meeting, another board meeting. Now we've got version two on the screen. Oh, it's better. I can tell it's better. Oh, great, wonderful. Same 80%, 90%, great. What's the 10%? What's the 15%? Ah, but it could be better if, right? Whenever I'm building software for AI, that is my main tool that I'm using, is but it could be better if. And of course, diminishing returns, and you don't want to do, you know, play this out to death. But the idea is that that's how we make it better. We start with all the stuff we've talked about, the ingredients, who it's playing, you know, get going, all these kind of techniques for displaying the data and the feedback. And now we loop. Do it better, do it better. Here's how I want you to do it better. Here's how I want you to do it different. Here's what I want you to show me instead. Stop showing me that. Show me this instead. Bring this forward, turn this down, make this bigger, turn whatever it is. We're playing. It's almost like a canvas that we can mold and shape. And after a little while, it gets good enough that we go, Great, it's helpful, fantastic. And we put that to the side as a usable asset to the business. It's not something we're gonna have to go babysit, it's something that's solid that can work for us. And now, 8020 rule, we look uh we look around the business, we try to find something else that is then the kind of the next lowest hanging fruit, and then we play the same same game there.

Jim 28:49

Got it, yeah.

The Risks Of Over Partnering

Jim 28:50

So let's let's move and talk about the risk of over-partnering with AI. What are some of those risks that might be um might manifest if you're let's say overly partnering with AI into your company?

Eddie Irvin 29:07

Yeah, this is where uh this is where we can really go off the rails, right? If we are trusting AI too much, if we're just saying, I don't know, you know, you figure it out, there's kind of a handful of different things. First of all, um, it absolutely can be wrong. And in the same way, if you hire a consultant to do something, right, they're not always going to be right, they're not always gonna be just telling you exactly the right thing to do. They'll make a mistake or they'll miss, they'll they'll not see something, or you know, things will change that they won't understand. Same thing with AI. But we can also uh we can also atrophy in our brain. This is actually real, that as we are now reliant more on AI, we then stop working out those parts of our brain that give us the critical thinking, that give us this deep thinking. And so that ends up meaning that that ends up meaning that we actually get weaker in our thought process. And so this is this happens over time. This is something we'll see in the next five, 10, 15, 20 years. I'm a little bit worried about my myself because I use Chat GPT all the time. You know, what am I losing by having AI do it? And I'd like to I like to think that I'm I'm able to then play strategy more. I can play higher strategy and what are we doing, and then it can take care of all the nonsense down below that I don't want to worry about anymore, the syntax of writing code, you know, looping and all that. I don't want to worry about that anymore. I just want to focus on what are we aiming for here. Uh, but the idea is that you know, AI is gonna tell you you got a great idea, and here's exactly what to do, and that's it. And you've got to always hold at a distance. I know what's doing and I know why it's doing it. You can see the system happening. It's supposed to tell me that I've got a great idea. It's supposed to tell me confidently that this is what to do. And as you understand that, you can kind of hold it at length. You then always have to be asking yourself, do I like this answer? Is this what I want? Is this kind of what I'm looking for? Is this making me think in a different direction? Is this what I'm aiming for? I was just doing this the other day, trying to work on ads and ad angles. And it came back with, you know, here's what it is. And I was saying, sure, but I don't want to do this because of this. I had not given it context for this one thing I didn't want to do. So it was saying, go ahead and do this. And I said, Oh, I forgot to tell you, this thing is what I'm trying to not do. So then kind of reshape it. And I went back and forth and back and forth and back and forth. We use it as a tool, we use it at a distance, and we never just implicitly trust it. We've always got to apply our own brain power uh to it to get the best result.

Building An AI Partner Culture

Jim 31:29

So for companies, we read a lot in the news about stress within companies and organizations because of AI. How do you build an AI partner culture inside of a company?

Eddie Irvin 31:45

Hmm. Let's let's dive deeper into the question there. When you say AI partner culture, you mean all the different teammates are using AI themselves? They all have got their agent. But tell me a little bit more about what you're seeing here.

Jim 31:56

Yeah, when you're looking to like you, like the idea of AI as a partner versus just being a tool or resource, you know, really incorporating AI into the system. Uh, a lot of the head trash right now is that, you know, AI is going to take up, take my job. There's a lot of stress and everything like that. But a lot of times I think is uh people don't realize exactly the benefits of partnering, um, incorporating, working with AI. And so for there to be that culture to be developed, there has to be certain type of uh base elements of it. You know, what might you recommend to a company to help them incorporate AI, but also work on the culture side so that you don't have to worry about the stresses and all the issues related to AI adoption?

Eddie Irvin 32:45

Yeah, yeah, that's a great question. When we're still when we're starting to talk about this, the first vision that comes into my mind is all the tech we already do adopt. All the tech, we kind of like it's like duh. Like, I know how to use a calculator, and nobody's pulling out an abacus at a at a corporation anymore to add things up, right? Everybody's got a phone, everybody's got a calendar system, everybody knows how to use Zoom or Teams or whatever it is. There's so much tech that we just have learned how to use. And you saw in COVID how people who were gonna drag their feet for the next four years to learn how to use Zoom, to learn how to go remote. In two days, they're on Zoom and they're remote and they're figuring it out because they had to do it. And this is really what's happening with AI now, you know, whether we like it or not. And again, I'm on both sides. I'm excited and I'm also scared about this tool. I'm certainly not just gonna lay down and just not do anything with it. I'm gonna be more proactive to try to understand it. But uh really the solution here is helping your team get equipped and understand how to use the tool, how to use the tool safely, how to use the tool intelligently. Um, you know, let's let's I love the calendar, I love the uh the calculator example because it's almost so ridiculous. Let's imagine that everybody in your organization organization uses abacuses, and then somebody else comes in with a new calculator. And people are going, oh my gosh, well, what you know, if we use that, then we're gonna be out of a job because we're using this abacus, and that's what I know how to use the abacus. So if I have to use a calculator, oh you know, oh no, it's gonna be all over for me. Sure, maybe, or we use a calculator and we all move faster, right? I want to be bullish about it. I want to be excited about that we can we can move faster and do more in the same amount of time. And yes, jobs will be lost, things will change, but we almost can't stop technology from advancing. I suppose we could all burn it down and go live on a farm, but that's you know, anything less than that is is we we're gonna have to use this tool. Let's imagine another example. We're all horse farmers. We're all of us are horse farmers, it's our whole well-being. And now here comes Henry Ford with a car. Now, of course, we know how this played out. Cars came, cars are great, cars are helpful, and now there's a whole industry based on cars. Now we got to make the cars and key and tune the cars, we've got gas stations, we got this, and we got that. Things will continue to change. AI is also not the last thing we'll see in our lifetime. It feels like it's this new thing and it's blowing our brains, but probably it's not gonna be the last thing that we see. We have to adapt. And so, in your organization, we want to make sure that we're training our people, we want to make sure that we're equipping our people, that we're helping our people uh understand how to use this tool because as they know what to do with it, as they harness it, the whole organization is gonna be better off. And really, we want to be able to train up people to use AI because again, why would we let them continue to use abacuses or you know, try to figure it out with a calculator? No, here's how to use a calculator. You should know how to use this, at least rudimentarily. Here's how to start with it. And so uh we need to train, we need to equip, we need to clarify boundaries where we want them to use AI, what information is okay for them to put into AI, what information is not okay to put into AI. There's so many different parts and pieces here. Uh, but the fact of the matter is your people are already using AI somehow. And so if you have the conversation, if you if you if you bring people together and have a focused conversation, um, that is really where then the organization as a whole starts to be able to move in this in this positive direction.

Closing Takeaways And Sharing

Jim 36:20

Eddie, I love how you pulled everything together with the historical perspective and also related to technology as well. As we close out this episode, everyone listening, we have to realize that the you know the the future is human and AI working together, that collaboration, because the winners of the AI revolution are going to be those who think with AI, not those who just use it as a tool. And it's super important that we do understand that. Also, we often realize, like Eddie had mentioned, you know, AI isn't really not here to replace human thinking, it's here to amplify it. Eddie, in closing out this episode, is there any message or anything we didn't talk about during our conversation that you would want to leave with the listeners?

Eddie Irvin 37:11

Yeah, I appreciate that. Um, I mean, ultimately, I think it's important to be able to see the duality of it, you know, that there are things that are are negative about AI, and there's also things that are really positive about AI. It's not just one or the other. And as you play, as you get on the bike and ride it, you will see things and be able to do things you haven't been able to do before. And that's really exciting. For me, that's been so exciting to be able to understand all the different things I can do with AI now. And as I learn those things, and I can dream in new directions, I can do new things in new ways. And if you're creative, you're creative, you're an entrepreneur, you're in business, you want to help your organization, being able to understand more of this tool is is so, so helpful. And uh, and I wish you well to jump in and just play and figure it out. Uh, it's it's uh it's been fantastic uh to be able to try to understand what this tool can do. And this is really only the beginning.

Jim 38:06

Yeah, it is an amazing journey. Everyone, please. Uh, if you uh know anyone, a colleague, associate, a connection who would find value in listening to this episode, please make sure to share it with them. And also too, feel free to message uh the podcast on any questions you might have. In the podcast description, I will have links uh to Eddie and also to uh to his initiatives and uh also to uh the organization that he uh does uh found it and works with as well. Eddie, thank you so much. Thank you, Jim. It's been a pleasure. All right, everyone, have a good rest of your day. Thank you.