Latest / The Growth Project / Episode 395: AI Strategy Starts with People with Mike Rudinsky (Part 1)

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Episode 395: AI Strategy Starts with People with Mike Rudinsky (Part 1) · The Growth Project

• 0:00
  there's got to be a strategy from the get go of, hey, here's what the human does that only a human can do.
• 0:07
  And here's what I can do. And how that all works together and in place together. That's really important, because if you, you know, unintentionally conflate those or, or mix that up as all as a leader, you can inspire a lot of, a lot of fear. Yeah. Pretty,
• 0:30
  Welcome to The Growth Project, a podcast brought to you by AMP plus. Fired up to be here today. And just as we kind of get into this, I want to mention from the outset for our listeners that we are kicking off an, series here.
• 0:46
  So today is just one of three,
• 0:48
  episodes. We're going to be talking about a topic that, I don't know, a few people are talking about these days. Is that is that a fair thing to say, Mike? As as I introduce, you're saying. Yeah. So we're talking about maybe you've heard of it. I know. And and no better person to be joining the the show to talk about it than Michael Dembski.
• 1:09
  I want to make a quick note about Mike before kick it to you, my man, to say more about your background. But Mike has just recently joined,
• 1:16
  the practice over here at Elliot Davis and thrilled that he's here and really, really excited about the ways in which we are teaming up to bring, I think just a very unique, way of looking at AI.
• 1:30
  So before we get to that, Mike,
• 1:33
  tell us about yourself, man.
• 1:34
  Sure. Thank you for having me.
• 1:36
  My name is Michael Wolinsky.
• 1:37
  I have been in and around technology for about 25 years. My prior gig was with an education company. So we, had various different kinds of technology education for consumers, enterprise and the government.
• 1:55
  And obviously, the last couple of years has really been focused on AI. So we've done a lot of work there and enjoyed it. And, and I think it's it's been fun to learn myself. And as we'll discuss, you never quit learning.
• 2:09
  I hope you don't know. I wouldn't recommend it. I think it's, by the way, we typically think about adult learning.
• 2:16
  So, Yeah. And as we get into this conversation, Mike, I think the place to begin before we're even too much in the trenches, specific to all things AI. We've got to start with the people, right? We've got to start with the humans in in just right there. I don't know that as whether it's people, teams, organizations are asking the questions they're asking right now about AI.
• 2:45
  I'm not sure who is or isn't beginning there, but our recommendation certainly is. You got to start with the people. Yes.
• 2:52
  Yeah, I think I think more and more are finding out now that the technology has outpaced us by so much. Yeah. The technology's there. That's that's not the issue. The the human side is what.
• 3:02
  Yes.
• 3:03
  Focus on.
• 3:03
  And so, yeah, I think it's
• 3:04
  a great place to start.
• 3:05
  So I want to begin by, just mentioning that what do we know about people? And here's some things, you know, having been in the psychology space for a really long time now, and all of our team here at AMP was, has, has been and will continue to be fascinated about humans.
• 3:22
  But I'll start here. Humans struggle with anything that is unknown. Yes. Right. So what do we do? It doesn't matter whether it's AI. It doesn't matter whether. Oh, I'm trying to get to some place in a location I've never been before. And I'm not sure how to do that. When we are faced with unknown, these are the things that typically happen.
• 3:44
  So internal. There's biology to this. I as a human normally typically will experience things such as I see increases in cortisol in my brain. Right. So I'm presented with something that's unknown. Cortisol rises. What do we know. The ramifications of that are when cortisol rises. It has a direct impact in a negative way on my creativity, my problem solving skills amongst other things.
• 4:12
  What do I then? Do? I go into a little bit of kind of a flight mode, fight mode where I become more resistant, less pliable, less flexible. And what I try to do is kind of protect. And so when you're trying to protect, what are you not doing? You're not functioning with the same amount of open mindedness. You're less likely to ask questions.
• 4:37
  So I think it's really, really important we start there because all of that is highly relevant to how this conversation even gets going.
• 4:45
  Yeah, I agree, and I think we're talking about a subject that isn't like learning, you know, potentially a fun thing or an enjoyable thing.
• 4:55
  Very true.
• 4:56
  Yeah, there's there's there's apprehension here. That there might be fear here.
• 5:01
  And so layer that on top into this becomes, this becomes even more challenging
• 5:04
  without a doubt. Without doubt. And so even let's just focus on this one part, Mike, is I think when, when we're not sure about something and you think, let's just kind of take that and drop that into and think about the landscape of an organization in who's being who's on the frontlines of figuring out, sorting out, making decisions, driving strategy in any organization, the leadership.
• 5:29
  Yeah. And if you don't, a leadership team, particularly in a senior leadership position, what do you feel like you need to do, if not all the time, but certainly most of the time is present is though you know what you're doing, right? Lead with confidence. Be certain. Be assertive. It is really hard to do that right now with respect to AI.
• 5:54
  Yes. And because it is and because leaders don't want to be uncertain. They want to be confident. Like what are they less inclined to do? Ask questions. But we're not going to behave that way. Right?
• 6:09
  Right. And I think on top of that, asking questions, there can also be the sensation that I'm really behind.
• 6:15
  Yes.
• 6:16
  Just.
• 6:17
  Yes.
• 6:17
  Am I asking the wrong questions or should I even be asking them? They're also maybe that sensation of should. I've been asking them a year ago.
• 6:25
  Yes. Right. And is someone going to figure out that I should have been asking these a year ago and I wasn't. And when they figure it out what's going to happen next.
• 6:36
  So I want to ask some questions that I think are relevant and that I don't know if people are asking these or not. They might be reluctant, but I want to ask them anyways. And I think you're the perfect person to answer some of them. So here's my first one. What is AI? You know, like that's where my mind begins to go is like like when we say that and we're all talking about what is it?
• 7:00
  And like, how did it even get going?
• 7:02
  Yeah. One way to think about AI is that it is a, a super good, super fast guesser. That is guessing based on reading every book, every article, every recipe, every email that's out there, and predicting an answer. And, and the way it does that because it has read a lot of books, a lot of articles, all of that, it has recognized patterns in all of those things.
• 7:36
  And so it is guessing what, you know, in the, in the work context, you're writing a sentence and it's guessing what the rest of that sentence is based on patterns. And see and learn from all those articles and books and pictures and all of that. So a very good guesser. As it is a good way to
• 7:56
  kind of a very I mean, based on my experience as an excellent guesser and some of the output I've seen from, some of the AI that we're leveraging.
• 8:05
  And, you know, Mike, the other part of that is, and I'm hearing this a bunch from people. I know you're way, way more familiar this with this than I am, but although some people in the last six months have discovered, for example, that, hey, copilot can be really great, or maybe on the personal side, like, hey, ChatGPT has been this cool.
• 8:28
  Neither of those things got off the ground six months ago. So let's do a little history here. Walk us through some form of like the history of when this started and how we
• 8:40
  got here. Oh, is. Yeah, a long time ago. But if we look at recent history, the initial models, some things like ChatGPT, and learned a particular data set in the beginning, it was a smaller data set of certain number of books or articles or something like that.
• 9:00
  And as the years have progressed, now, it is learning on more and more data and, and to the point now where it's learning, maybe closer to all of the things out there, all of the articles, all the books, all of the pictures, all all of that. And so as it is able to learn from more things, it gets sharper.
• 9:25
  And it can provide better guesses. And so that's what you're seeing. And that's why, you know. ChatGPT. The popular versions of it are only a couple of years old. But even since then have gotten so much, better and sharper and able to guess better and sharper and provide us with, with even more value. Even in the last couple of years.
• 9:50
  But this has been around for a long, long time. The idea of artificial intelligence, or the idea that a machine could start to mimic human tasks and things that humans do, has been around for a long time. We've just seen it, you know, an exponential rise in the last couple
• 10:06
  few years. Yeah. And I think, I mean, everyone has a different idea of what the word rapid means, but I certainly feel like at the moment we're in a space where there's some rapid advancement that's happening with these.
• 10:19
  Although we will quickly be obsolete with that statement. Right.
• 10:23
  Yeah. Yeah. I mean, I think if we had done this a year ago, we would have said, yeah, the the models had learned on a big chunk of, of, of data. So a big chunk of books or a big chunk of it and, and you know, now as we sit here today, it's like, oh geez, I mean, it's over and on, all of that, that existed and now it's creating its own.
• 10:42
  So the more we use it and interact with it, the more it's learning. And and then, you know, able to make better guesses. The point where I think it's very natural for a human to go. Is it is it like us? Is it, is it understanding? Is it magic and you know it right now? It is just recognizing patterns and all of that and helping us to predict what the what the next, what the next word might be in a work context.
• 11:11
  Yeah, that gets really interesting because it's it's helping me finish my thoughts or my sentences. And that sometimes does look like magic. But it's not because I understands or feels or that kind of thing. It's just that it's recognizing patterns. Yeah.
• 11:24
  Well, and these are obviously what I would consider to be kind of foundational, fundamental questions that we're asking right now.
• 11:31
  I think you got to start here for a number of reasons, right? I mean, what do we know about the brain and just the psyche of people that in the absence of delivering clarity to people, they will draw their own conclusions, some of which in this space particularly will be the ones we don't want them drawing. Another reason I think this is so vital is to kind of level the playing field of like, oh, okay, we now have a shared understanding amongst people of what this actually is.
• 11:57
  When it started, how it got think you must have that. And my I think one reason why this is so important for organizations to be thinking about this is people don't want to sit in a class and raise their hand and ask, what is a. And nobody wants to do that. So it's important that we go first in thinking about how to fill in the blanks for humans.
• 12:17
  Yeah, I think it's great. But like, like a lot of things demystifying as much as we can and just talk about it. I mean, have conversations. You know, it's like those. If you can explain it to your kids, you might know it well enough. And with AI, it's like, genius the least. I very tough to explain.
• 12:35
  It's a it's a very abstract thought and and challenging. So yeah, we are all doing our best to articulate what this, what this actually means.
• 12:44
  And I think this is one where in some cases our kids are explaining it to us.
• 12:48
  They're showing us how to use this every new day. And they're curious. And yeah, that's a biggie.
• 12:53
  They're curious and, and they're just saying, let's let's see if this can help here or there or think this way.
• 13:01
  So, Mike, let's think through this a little bit. There's obviously different categories or contexts in which we're seeing I show up, be leverage, be utilized. I think for a lot of folks that's starting on the personal side, but I may or may not be right about that.
• 13:19
  Can you talk about that a little bit? Yeah,
• 13:20
  I think the personal use case, hopefully that's where you start to get comfortable and start to try things, you know, especially if it's if it's on your phone, that means it's walking around with you, which is really great. And so the ability to ask it a question, and get an answer back is, is great.
• 13:38
  Yeah. I think you and I were talking about whether it's whether it's planning a trip or. Yep, fixing something. It's an incredible resource. And you start to understand, thing is things helping?
• 13:51
  Oh, yeah. I mean, we recently took a family trip to Europe, and I don't know how I would have gotten by without it, honestly. I mean, it was our travel guide.
• 13:59
  Well, and it's it's fun because you can approach a problem and and it's like you have a, a very knowledgeable, very patient teacher, you know, with you any time, which is fascinating.
• 14:13
  Yeah. Yeah. And so and even in those examples, one thing that I think is really important is as you're thinking about what is this and has it integrated in my life worth pointing out right now, some really positive applications of how this can do things that we all care about that can make us more informed, more efficient, right?
• 14:33
  Those are good things. Really good things.
• 14:36
  I agree. I mean, if you think about one of the one of my favorite use cases is on the health side. And if you think about a, you know, the human brain can only contain so much. But if you think about a, a doctor who is leveraging AI, I understands every case file of every patient of all time.
• 14:58
  And so the idea that that could help that doctor who's in the room with you understand your symptoms better because you know, it contemplates everything is really exciting. I mean, I think from a from a consumer standpoint, that's where I have the most hope. And it's like, oh, this is this is great. And this is really great.
• 15:16
  So let's, let's stay on the personal context here for just a second in, you said a few minutes ago that what I truly is, is it's a it's a great guesser. Right. It's filling in blanks. Can you give us an example of how that's actually working or what it's doing to fill in blanks?
• 15:35
  Sure. Kind of a silly one.
• 15:38
  But if you said the cat in, you know, our brains go to a feeling. And whether it was our childhood or reading a reading a book or reading a book to our kids. And so those emotions come back and it feels. Yep. For AI, it has learned based on all of the things that it's read and seen based on patterns.
• 16:02
  It's like, well, it's the hat, the cat in the hat. It's not. It's, feeling the emotions that we're feeling. It's just a pattern. It's like statistically, the probable next words to what you're typing is the hat. So we've gotten hat. And that's the difference. And I think that's the difference between humans and a machine. But it is pretty cool.
• 16:22
  It's like how you read my mind. It's like, well, no, it actually just found the highest statistical, idea of what would come next. Just a math equation based on patterns. And so that's kind of the difference.
• 16:38
  And so in that case, the am I saying this correctly? The agent is the agent doing that. Is that an agent.
• 16:44
  No, this is just a model.
• 16:45
  This is a model okay. Okay. And so the models have been built have been informed. Like talk a little bit about like how did the data get there.
• 16:57
  Oh that's a loaded question. So we'll just to clarify models, let's use ChatGPT just as an example. So that's that's the model okay.
• 17:09
  It learned based on a lot of public texts of the internet, books, articles, things like that. The reason it's a loaded question is it learned a lot of things that could have been I.P., could have been created by humans. And so interesting. It learned quite a bit. And, you know, kind of a controversial subject only because it's like.
• 17:34
  Well, who owns all of.
• 17:36
  Yeah, they
• 17:36
  did all of that. Yeah. And so, from the human side, it's like, well, I authored that book, you know, that was I wrote a book. You should pay me for the book. So, so anyway, so controversial. But but that is, how the model learned, based on everything it could possibly consume.
• 17:55
  And so you saw early days, people tagging certain books, certain pictures, certain things, so that it could help, the computer, and the machine understand what it was looking at.
• 18:10
  And so there is, if I'm understanding you correctly, there's also a manual ized, approach to how models have been built of people, literally just like dumping files and, and data into the models.
• 18:23
  Is that right?
• 18:23
  And so when you look at the sizes over time of how much data it has in the model, it has grown. I mean, even at even in the last six months, it's grown exponentially. And so now these models are just gobbling up all the data it can possibly grab. So that it is a better guesser.
• 18:43
  All that's in service of I just want to be able to guess better and predict better. And so it's not only grabbing all of the, words and data and things that already exist, it's now starting to even produce some of its own now. And all of that's getting mixed in. And and, you know, the, the is how much context, how much data can it can it possibly get.
• 19:06
  Because the more it knows, the better it will be able to guess
• 19:11
  is that literally a person at a workstation dragging files into something it used to be? It used to be. It
• 19:18
  used to be. And now it's helping us. I see it, but it is helping us, feed it more.
• 19:25
  Okay. That's wild. Yeah.
• 19:29
  I mean, it's crazy crazy to think.
• 19:32
  And I know, like, the psychologist over here is asking the questions that are elementary as can be. And I'm sure you're thinking like, well, yeah, that's how they did it. And but I think it's probably a good image of just where the world is right now is, is the things that I, as the novice, are wondering about. I think a lot of people are
• 19:50
  sure these are all good questions.
• 19:51
  I mean, we're we're trying out whatever analogy, if you want to use the surfing analogy for, for those of you who've served, it's, it's trying to catch up to the wave. You've got to be going the speed of the wave to catch the wave. And so I think we're all just trying to catch up and even, you know, very gracious of you to say that I know what I'm talking about or have some sort of expertise, but, you know, we're all just trying to learn as fast as we can.
• 20:14
  This thing's out ahead of us already. And so again, we're just trying to keep up.
• 20:19
  So let's let's pivot a little bit.
• 20:22
  Is there anything else you wanted to touch on? On the personal side, before we pivot and think about organizations a little bit? No. Okay. So let's let's begin down that path a little bit.
• 20:31
  Sure. And there is a lot to that, to say the least. And maybe the place to start would be here. I know you've, you spent, lots of time in the trenches and helping organizations think about how to solve for this and train for this and educate this. From the very beginning, if I want to make my best attempt to kind of, like, take myself into the stream of consciousness in the inner dialog of people within organizations who are certainly aware that AI is increasingly becoming a thing.
• 21:10
  Where are people's minds going from the very beginning as they contemplate what this might mean in a variety of ways?
• 21:18
  Yeah, that's a it's a great question. I think the more you learn about this, the more you understand the possibilities and what I can do. And that can be very exciting. It can feel like a, a secret weapon or it can supercharge you.
• 21:40
  It can also be scary. Because you can start to get that feeling of, well it can do that. Now, why why would they need me. Where is my value. And so you can quickly go to that. And, and a lot of times those things happen simultaneously. It's really exciting. And also slightly terrifying all at the same time.
• 22:00
  And so I think from, from the human standpoint, I think a lot of folks are wrestling with that every day. And it's a completely fair thing. And I will say, the further you get into this. The more exciting and potentially the more scary it gets.
• 22:15
  Yeah. And like, let's let's study humans here for just a moment. When we are faced with something is that is both exciting and scary.
• 22:24
  We are way more likely to learn scary. That's I mean, we just know that.
• 22:29
  Four out of the five basic human emotions are negative emotions. That's highly, impactful when it comes to our internal dialog and our self-talk. We know about 70% or so of our thoughts day in and day out tend to lean negative.
• 22:44
  And so you think about the environmental impact. When I say environment, what I mean is the type of environment and culture I've created within an organization. We're now presenting things to people, whether we're actually presenting them or not. They're being presented. And what are we saying? This is exciting. It's also scary, you know, to who is it exciting to?
• 23:06
  Who is it scary? It depends what seat you sit in. But there are a lot of people sitting in seats who are way more focused on scary than they are excited. And I'm sure you've seen plenty of that.
• 23:16
  We have, and we've had a little help on the scary side, from Hollywood, from from from books and and yeah, that's that's all real.
• 23:25
  And it's, it's hard to argue where this could go. So you've got that there are other real concerns around the physical environment and what it takes to do this. And so there's a lot of very valid concerns of what this is and where this goes. On top of the personal concerns of where do I add value, where do I fit, which I would imagine, you know, is a very pre I very normal, you know, worry.
• 23:54
  You know this is just amplified now.
• 23:57
  So okay let's pause there for a second mic. And let me do a quick sidebar here, but I'll come back to where we are. You know, we here we are grateful and kind of our history of amplifiers. And in the years that we've been doing our thing and partnering with organizations all over the world, and here is a chorus that I've heard many, many times from many organizations across all industries.
• 24:21
  It's some version of this statement. Our competitive advantage is are people or our people are our secret sauce or or powered by our people. There are many ways you can say this slice, this, dice, this. What they're talking about is, hey, we think of us, our people as our real, where we win. Now take one step back. Some people are serious about that.
• 24:52
  Some people aren't. It's kind of like everybody says they care about culture. Some do, some don't. But let's just focus for a moment on the ones that are serious, the ones that really mean this. When they say this of they think they're people, they believe their people are their competitive advantage. They are the things and the ones. And this is what amplifies everything that we do.
• 25:13
  Now put that on one spot here, just for a moment. Let's now think about here's what we're saying about people right now. They're worried. They're uncertain. They don't know where this is headed, although part of them is excited. They're also really, really scared. So when you think about the collision of all of those realities, and then you've got people who are trying to make decisions on how do we bring this into the mix?
• 25:42
  What are the impacts of those organizations that are starting with strategy? Right. Like, so like if you're beginning with how do we integrate this into our workflows? Like what are the right agents we need to be leaning on like, wait a minute, I thought your people were your competitive advantage. So talk us through that.
• 26:02
  Yeah, it's a tricky one.
• 26:04
  I think you've got to really, as a as a company, as a leader, really talk about your why behind using AI and your intent. And that's really important. So that you have a North Star. And so for example, a why or intent could be so that we can, get a competitive advantage in the marketplace.
• 26:29
  So which is a totally fine one, which is totally fine.
• 26:32
  Yeah. We are going to use AI to our advantage with that type of North Star, which is the one obviously we recommend. You and your people are oriented towards that. So I becomes kind of a it is a tool. It is a way to enable you to get there. There are plenty of examples of companies that we call that opportunity AI.
• 27:00
  Let's just say that that's one end of the spectrum. The other end of the spectrum is what we can call efficiency AI, which is exactly what it sounds like, which is we're going to use AI to become more efficient. And in simple terms, that that means cost reduction. In simpler terms, that means human reduction, people reduction. And so, it's really important at the outset to understand why you're using AI and what strategies you have.
• 27:34
  Obviously, we hope that it is for opportunity for advantage in that regard. What you're telling your team is this is a tool. We're using all of our people. We're using, all of our talents to get to this goal. The humans are still very necessary. The humans are still a very important, because we're trying to get to this goal.
• 27:56
  But you can see if your goal is efficiency AI, then the humans, rightfully so, could go. Well, wait a minute. Yep. This is all just a play to cut costs. Yeah. And and that is, you know, that that is scary.
• 28:12
  All right, so let me put you on the spot with a question if that's okay. I mean, I've been doing that this whole time, but let's keep going right.
• 28:19
  In your experience, Mike, of, advising many organizations over the years on this topic, what percentage of those organizations without advisory and counsel had the right insight and understanding of how to begin and frame up this whole initiative? And what I mean by that is the psychology, all of this. What percentage is orgs had that figured out?
• 28:48
  Very few, very few. And I think, we're seeing a lot of that play out now. With AI adoption that didn't work. And, and I think a lot of organizations are now wondering why. And I think that goes back to what you were talking about, of going, well, the organizations made up of humans and,
• 29:16
  who are afraid to ask questions.
• 29:17
  Right.
• 29:18
  And so, they are acting like humans and, and and so it's really important from the get go to be able to articulate your strategy and intent around. Yes. And that's, that's yes. You would be able to, bring folks along with you, motivate them, hopefully even inspire them. As a leader, that's what we what we hope to do.
• 29:48
  And but then I'll start with a very clear, a very clear strategy.
• 29:55
  Yes. You know, to what degree has the organization. And by that I mean particularly those senior leaders. To what degree have you really thought about, gotten feedback on, and planned for your specific messaging campaign to the broader org, like how how have you pressure tested that who's who's spoken into that?
• 30:17
  Have you thought about what it's like to be on the receiving end of that? And I don't mean you spent 20 minutes putting some notes together before you jumped on a team's call to let people know, we're rolling this thing out. That's not preparation, but the groundwork that must be laid within organizations in order to and be used to.
• 30:38
  Be careful how we say this in order to not just like, thrive. I think our point here, Mike, is in order to have a chance.
• 30:45
  Yeah, I completely agree with that. I mean, if you think about so and I know we've got we've got two more episodes, but if you think about it, it's, on the personal side,
• 30:56
  we are asking ChatGPT questions.
• 30:59
  We are prompting. And saying hey I, I'd like an answer with this in the, in the work part of this. We are prompting and, and stringing prompts together into a workflow. And having it do meaningful work for us.
• 31:20
  But there's got to be a strategy from the get go of, hey, here's what the human does that only a human can do.
• 31:28
  And here's what I can do. And how that all works together and in place together. That's really important, because if you, you know, unintentionally conflate those or, or mix that up as all as a leader, you can inspire a lot of, a lot of fear.
• 31:47
  Yeah. Pretty, pretty quickly. And I think that that's happening to the point where, the humans are understanding now that it's like, well, wait a minute, are you just trying to get all of my institutional knowledge and all of my experience out of my brain to just program the machine to do my job for me?
• 32:07
  Is that what you're asking me to do? And so, yeah, when done right, the strategy is, well, we're we're going to ask AI to help do some of the more mundane or lower level tasks so that you can focus on higher level ones. Okay. That's cool. As a human it's like I might even like that. Yeah.
• 32:24
  Right. That part so that I can focus on the higher level ones. But if you don't have that North Star as a company of we're going to use a high to our advantage. Yeah. And we know where that human fits and we know where the AI fits. Yeah. That can get messy pretty quick.
• 32:38
  And back to my initial question.
• 32:41
  In your experience, very few organizations or getting that right.
• 32:46
  And I think we're seeing we're seeing that now. And you're seeing everything from a lot of wasted investment of just buying tools. Yeah. Hoping people would adopt. Yeah. All the way to the other extreme of the resistance. Yeah. From humans who are saying, no, I'm actually not going to do it.
• 33:04
  Or even worse. I'm going to sabotage the AI, because if the AI is that good. Yes. And I get replaced.
• 33:12
  Yep. So. Okay, I want to come back to that in just a second. But because I want to make this point. We've seen this many times over the years. AI is not the first thing to come on the on the landscape here where organizations felt the pressure to go fast.
• 33:26
  That's not new. There's so many reasons that that pop into the mix, where organizations feel like we got to go fast. Okay. They then typically say the cost of not going fast, and they're making their best attempt at like, what would be or could be X or Y or Z, whatever that might be. Mike, what I hear you saying is more organizations need to think about the cost of going fast.
• 33:54
  Yeah, because where does that normally end up?
• 33:56
  Yep. And that is you know, AI is not magic, right? You know, it it it does not understand or feel like a human does. Putting too much on AI or relying on AI for too much is really dangerous. And yeah, we can go to a whole episode to what can happen when I does not get it quite right.
• 34:22
  Yeah. And here's the kicker is it sounds really confident.
• 34:27
  Oh, I'm sure it
• 34:28
  sounds really confident when it's not right. And and so not having the humans in the right places. Yeah. Yeah. Yeah.
• 34:35
  And just just to say this again, clearly, whether it's AI or any subject we're talking about in the absence of clarity, we will almost always default to some form of a negative conclusion.
• 34:48
  Right. We become threat focused and that's what's happening. Right. And so you go back to what you mentioned a second ago. There are many plenty of examples of the humans sabotaging AI. Why might they do that? Plenty of reasons why they might do that. One of them is no one's told them. What the heck's going on here?
• 35:06
  Sure.
• 35:06
  And they're scared.
• 35:07
  They're scared.
• 35:09
  And who can blame them?
• 35:10
  Oh, yeah. You can't. You can't at all. And that's why this. This really comes back to. This is a human thing. This is a
• 35:17
  leadership completely,
• 35:18
  you know.
• 35:19
  And why are they sometimes not given the clarity? They don't know how to know. No one's told them what they need to do it.
• 35:25
  No one's told them how. Yeah. And so what you end up with is they have a lack of clarity. The leadership has a lack of clarity of, like, even if I wanted to do that, how would I do it? Not to mention the fact I think you're probably better off having someone else do that, or at least partner with you in it.
• 35:42
  Right. And let's back up a step to the leader. The leader. Scared?
• 35:47
  Totally scared. You
• 35:48
  know, I think, you know, I, using AI can cause you to have to shift identity, you know? Yeah. Like, I always used to do this part, but now you're telling me I need to do this part anymore. So where exactly do I fit in?
• 36:02
  That starts at the top. Yeah, I think you've got, you've got the CEOs of the folks building AI who are like, yeah. Yep, I'll replace me. And so so it starts at the leadership side. So in terms of, helping them get their mental model and their head straight before rolling it out to, to to the rest of the company, is is an important first step that I think a lot of folks didn't know how, but also didn't didn't take the time to do and now feel like they're behind.
• 36:34
  So it's like, now let's go even faster. Yep.
• 36:36
  All right. So I'm going to offend somebody with this. But I do it anyways right. So as you're saying that and to be totally clear, I'm not talking about anything specific in particular, but I just see in my head this scene in which we have a senior leadership team reporting back to a board of some kind.
• 36:56
  Kind of the overarching plan or strategy for AI, in which neither the leadership team nor the board knows what they're doing.
• 37:02
  That's correct. And you write that the mandate is do I? Yeah. People asking you to do I don't know exactly what that means. And the people receiving that are, you know, respectfully don't know about that. Right.
• 37:14
  And look, there are plenty of examples where both the team leader team and the board know what they're doing.
• 37:18
  We realize that. But there are plenty of others where they don't. Sure,
• 37:21
  sure. And in understanding the capabilities of this, this is a this is a day to day thing. So the things that were might have been true six months ago certainly are true now. And the capabilities aren't there. So are there. So even if you were on top of it and didn't know all of it, even a certain point, tomorrow's a brand new day, and we'll have new announcements and a new model and new capabilities.
• 37:44
  So it is even if everybody knew what they were doing. There's still the need to evolve and, and, you know, keep
• 37:52
  rolling and. Yeah. And today is not the day for that conversation that we'll be getting into for episode two, in part will be talking about, well, what is the mentality you should have to give your organization the best chance to be successful?
• 38:08
  And that's where we will really be heading. But, we've got to have we've got to have the right mindset toward all of this. Okay. So I know we need to be moving toward the finish line here in just a moment, Mike. But as we, kind of begin to put a cap on this conversation, and really, today's goal was to just establish the fact that, although we're calling this a technology innovative, you know, challenge, we've got to be sure we're starting in the right place, and we must be sure we're not just seeing this as a tech thing, because it's not just a tech thing.
• 38:43
  Yeah. And to your point, for for the companies out there that say people are our greatest asset, this company is built on people. That's a pretty natural place to start. You know, if if you truly believe that to your core and that's one of your values, let's start there.
• 38:58
  Let's start there. All right. I hope we haven't shared too many people.
• 39:02
  This is not. Yeah. And I'm saying that, you know, just to bring some levity as we close here. But I do think we want to be sure that we're framing this in a way that we're we're thinking about this in great detail. And ultimately, that's what we want to be able to do. As a team and as a firm as we, do our work to advise organizations.
• 39:26
  But we want to I just want to challenge our listeners to be thinking about this in every facet of your life, thinking about this in your personal life. For those of you that are a part of families and have have kids and things like that, like how much have you thought about the ways in what you're trying to equip and and educate your kids on what this is and what it's good for and that type of thing?
• 39:44
  There's just a lot here. There's a lot here.
• 39:46
  I mean, we are we are part of a very exciting, exciting time. It could be a little bit scary, but, I mean, exciting time. This is a once in a multi generation, you know, thing. So.
• 39:59
  Okay. Well, Great start. Thank you. It's fun to hear you leverage your expertise and do your thing in this, in this realm.
• 40:07
  Because I'm a student along the way here. But yeah, thank you for joining us. We're always grateful to our listeners for, tuning in. And be sure you, prepare to tune in again for episode two, and our next conversation will be, taking the next step forward, kind of in this arc of getting into, well, what is the way we want to be thinking about all of this?
• 40:31
  And how do we want to be instilling the right approach and mentality for, both people and organizations to thrive? Which is absolutely possible and where we want to be headed. Cool. Awesome. Thank you guys. And we look forward to next time.