Your Engineers Are Resisting Your AI Rollout. 3 Things Turn That Around.

Your AI engineers probably hate that Your AI engineers probably hate that you're doing AI. And I say that not you're doing AI. And I say that not you're doing AI. And I say that not because I have some secret insight into because I have some secret insight into because I have some secret insight into your company. I say that because when I your company. I say that because when I your company. I say that because when I talk to people, right? Whether they're talk to people, right? Whether they're talk to people, right? Whether they're leaders, whether they're individual leaders, whether they're individual leaders, whether they're individual contributors, what I hear really contributors, what I hear really contributors, what I hear really consistently in any team over say 50 is consistently in any team over say 50 is consistently in any team over say 50 is that you really do have a collection of that you really do have a collection of that you really do have a collection of folks in your business that are not fans folks in your business that are not fans folks in your business that are not fans of AI at all, not happy that you're of AI at all, not happy that you're of AI at all, not happy that you're doing AI, actively resistant. In fact, doing AI, actively resistant. In fact, doing AI, actively resistant. In fact, there's a survey that came out of global there's a survey that came out of global there's a survey that came out of global companies that showed that a third companies that showed that a third companies that showed that a third globally admit to sabotaging AI. So, globally admit to sabotaging AI. So, globally admit to sabotaging AI. So, this is a very real issue. this is a very real issue. this is a very real issue. I want to talk today about how I address I want to talk today about how I address I want to talk today about how I address that concern in AI rollout. And so, I'm that concern in AI rollout. And so, I'm that concern in AI rollout. And so, I'm going to lay out for you what you get in going to lay out for you what you get in going to lay out for you what you get in this video. this video. this video. Number one, we're going to talk with Number one, we're going to talk with Number one, we're going to talk with leaders together. leaders together. leaders together. Even if you're not a leader, you want to Even if you're not a leader, you want to Even if you're not a leader, you want to be in on this conversation. This is what be in on this conversation. This is what be in on this conversation. This is what I actually tell leaders. I actually tell leaders. I actually tell leaders. What What do you say? How do you start What What do you say? How do you start What What do you say? How do you start to roll out this change? What is the to roll out this change? What is the to roll out this change? What is the commitment you need to make? Number two, commitment you need to make? Number two, commitment you need to make? Number two, we're going to talk about how you scope we're going to talk about how you scope we're going to talk about how you scope and actually set an a parameter for and actually set an a parameter for and actually set an a parameter for starting your change in AI and what that starting your change in AI and what that starting your change in AI and what that means in the details. And so, people ask means in the details. And so, people ask means in the details. And so, people ask me all the time, "Where do you start on me all the time, "Where do you start on me all the time, "Where do you start on AI?" That is your answer. You're going AI?" That is your answer. You're going AI?" That is your answer. You're going to find out. And why? And why that to find out. And why? And why that to find out. And why? And why that affects our whole team. Why that affects affects our whole team. Why that affects affects our whole team. Why that affects everyone here who's listening when they everyone here who's listening when they everyone here who's listening when they go through AI transformation.

go through AI transformation. go through AI transformation. And number three, And number three, And number three, we are going to make sure that we talk we are going to make sure that we talk we are going to make sure that we talk through some of the really messy details through some of the really messy details through some of the really messy details when you go from it's a cute little when you go from it's a cute little when you go from it's a cute little pilot to this actually has to scale. pilot to this actually has to scale. pilot to this actually has to scale. What does that mean for people? What What does that mean for people? What What does that mean for people? What does that mean for our systems? How do does that mean for our systems? How do does that mean for our systems? How do we have those work together more we have those work together more we have those work together more productively? And I'm going to give you productively? And I'm going to give you productively? And I'm going to give you the like the secret sauce all the way the like the secret sauce all the way the like the secret sauce all the way across, right? I'm going to tell you across, right? I'm going to tell you across, right? I'm going to tell you what I actually am hearing from people, what I actually am hearing from people, what I actually am hearing from people, what I'm hearing from leaders, how I'm what I'm hearing from leaders, how I'm what I'm hearing from leaders, how I'm seeing the the pitfalls happen and what seeing the the pitfalls happen and what seeing the the pitfalls happen and what I do to go around them. So, I hope you I do to go around them. So, I hope you I do to go around them. So, I hope you have fun with this one. Think of it as have fun with this one. Think of it as have fun with this one. Think of it as like a sneak peek behind how I talk with like a sneak peek behind how I talk with like a sneak peek behind how I talk with leaders about this complicated issue and leaders about this complicated issue and leaders about this complicated issue and I wanted everyone to hear it, so I'm I wanted everyone to hear it, so I'm I wanted everyone to hear it, so I'm sharing it here. And we're going to sharing it here. And we're going to sharing it here. And we're going to start with number one, start with number one, start with number one, Which is the contract that you have to Which is the contract that you have to Which is the contract that you have to effectively sign up for with your teams effectively sign up for with your teams effectively sign up for with your teams if you want them to come along for the if you want them to come along for the if you want them to come along for the ride with you. And I say contract not ride with you. And I say contract not ride with you. And I say contract not because it's necessarily legally because it's necessarily legally because it's necessarily legally binding, but it is it is a commitment. binding, but it is it is a commitment. binding, but it is it is a commitment. It is a commitment that you will need to It is a commitment that you will need to It is a commitment that you will need to make publicly as a leader if you are make publicly as a leader if you are make publicly as a leader if you are going through the process of AI going through the process of AI going through the process of AI transformation. Which, let me tell you, transformation. Which, let me tell you, transformation. Which, let me tell you, the world consists of people who are the world consists of people who are the world consists of people who are either AI native in their organizations either AI native in their organizations either AI native in their organizations or people who are trying to be AI or people who are trying to be AI or people who are trying to be AI native. There really isn't a corner of native. There really isn't a corner of native. There really isn't a corner of the workforce anywhere that I have seen the workforce anywhere that I have seen the workforce anywhere that I have seen that isn't at least somewhere on that that isn't at least somewhere on that that isn't at least somewhere on that spectrum. You may be early in your spectrum. You may be early in your spectrum. You may be early in your journey, but you're still looking at AI.

journey, but you're still looking at AI. journey, but you're still looking at AI. There are folks way outside tech who I There are folks way outside tech who I There are folks way outside tech who I talked to who are like, "Oh, you know talked to who are like, "Oh, you know talked to who are like, "Oh, you know what? I used Codex to to rebuild a tool what? I used Codex to to rebuild a tool what? I used Codex to to rebuild a tool in my factory recently." Like, it's just in my factory recently." Like, it's just in my factory recently." Like, it's just happening. happening. happening. And so, in that world, what we really And so, in that world, what we really And so, in that world, what we really need is a commitment from leadership need is a commitment from leadership need is a commitment from leadership that they can stick to that reflects that they can stick to that reflects that they can stick to that reflects their commitment to the well-being of their commitment to the well-being of their commitment to the well-being of the team as a whole. Because if you are the team as a whole. Because if you are the team as a whole. Because if you are asked to participate in an AI rollout asked to participate in an AI rollout asked to participate in an AI rollout and you look at that AI rollout and and you look at that AI rollout and and you look at that AI rollout and you're like, "I feel like this is you're like, "I feel like this is you're like, "I feel like this is targeted at me. I feel like this is targeted at me. I feel like this is targeted at me. I feel like this is coming for my job. I feel like all they coming for my job. I feel like all they coming for my job. I feel like all they want to do is cut headcount." I want to do is cut headcount." I want to do is cut headcount." I encourage leaders when they are facing encourage leaders when they are facing encourage leaders when they are facing that situation that situation that situation to just very simply address the elephant to just very simply address the elephant to just very simply address the elephant in the room. I think so much of the in the room. I think so much of the in the room. I think so much of the time, if we aren't addressing the time, if we aren't addressing the time, if we aren't addressing the elephant in the room, people just aren't elephant in the room, people just aren't elephant in the room, people just aren't able to move forward. And so, I say, able to move forward. And so, I say, able to move forward. And so, I say, "Look, you can be really honest. A lot "Look, you can be really honest. A lot "Look, you can be really honest. A lot of AI coverage, especially in news of AI coverage, especially in news of AI coverage, especially in news media, is around AI and jobs. media, is around AI and jobs. media, is around AI and jobs. Now, there is no consistent evidence Now, there is no consistent evidence Now, there is no consistent evidence still still still at a national scale in the United at a national scale in the United at a national scale in the United States, which is the most farthest along States, which is the most farthest along States, which is the most farthest along in its AI adoption journey, that we have in its AI adoption journey, that we have in its AI adoption journey, that we have any impact on jobs. It doesn't matter.

any impact on jobs. It doesn't matter. any impact on jobs. It doesn't matter. Because if, you know, Jack Dorsey says Because if, you know, Jack Dorsey says Because if, you know, Jack Dorsey says he's cutting thousands of people because he's cutting thousands of people because he's cutting thousands of people because he wants to focus on AI, will people he wants to focus on AI, will people he wants to focus on AI, will people hear that message? People hear, yeah, hear that message? People hear, yeah, hear that message? People hear, yeah, okay, maybe in the national aggregate okay, maybe in the national aggregate okay, maybe in the national aggregate statistics it doesn't matter, but for me statistics it doesn't matter, but for me statistics it doesn't matter, but for me it matters. For my ability to put food it matters. For my ability to put food it matters. For my ability to put food on the table, it matters. And they need on the table, it matters. And they need on the table, it matters. And they need you as a leader to be clear and say, you as a leader to be clear and say, you as a leader to be clear and say, what we are doing here is not designed what we are doing here is not designed what we are doing here is not designed to destroy jobs in this company. It is to destroy jobs in this company. It is to destroy jobs in this company. It is not designed to take away your role. not designed to take away your role. not designed to take away your role. Now, I have seen leaders do that in a Now, I have seen leaders do that in a Now, I have seen leaders do that in a way that reflects integrity. I've seen way that reflects integrity. I've seen way that reflects integrity. I've seen them do it by saying, we want to keep them do it by saying, we want to keep them do it by saying, we want to keep our company small and lean, and so our our company small and lean, and so our our company small and lean, and so our plan right now is to reduce the pace at plan right now is to reduce the pace at plan right now is to reduce the pace at which we grow, or our plan right now is which we grow, or our plan right now is which we grow, or our plan right now is not to hire a lot more because we have not to hire a lot more because we have not to hire a lot more because we have AI. So, there are ways that you can AI. So, there are ways that you can AI. So, there are ways that you can still talk about headcount effects still talk about headcount effects still talk about headcount effects without talking about cutting people who without talking about cutting people who without talking about cutting people who are currently there. When you are an are currently there. When you are an are currently there. When you are an enthusiastic proponent of AI, when enthusiastic proponent of AI, when enthusiastic proponent of AI, when you're able to use AI practically to you're able to use AI practically to you're able to use AI practically to deliver value, you're going to go deliver value, you're going to go deliver value, you're going to go farther in your career here. And if you farther in your career here. And if you farther in your career here. And if you are, conversely, resistant, if, you are, conversely, resistant, if, you are, conversely, resistant, if, you know, God forbid, you're you're know, God forbid, you're you're know, God forbid, you're you're sabotaging AI, sabotaging AI, sabotaging AI, those are going to be things that are those are going to be things that are those are going to be things that are going to jeopardize your career here.

going to jeopardize your career here. going to jeopardize your career here. And if you don't have a commitment to And if you don't have a commitment to And if you don't have a commitment to protect the people you have, you just protect the people you have, you just protect the people you have, you just don't have anywhere to start from. And don't have anywhere to start from. And don't have anywhere to start from. And if you want to have a chance to actually if you want to have a chance to actually if you want to have a chance to actually engage with the whole business, you have engage with the whole business, you have engage with the whole business, you have to be able to talk to them about the the to be able to talk to them about the the to be able to talk to them about the the goals you have for this rollout. Why are goals you have for this rollout. Why are goals you have for this rollout. Why are you doing this? And I find it is so much you doing this? And I find it is so much you doing this? And I find it is so much more compelling to use Jensen Huang's more compelling to use Jensen Huang's more compelling to use Jensen Huang's messaging than just about anything else messaging than just about anything else messaging than just about anything else because Jensen has talked very openly because Jensen has talked very openly because Jensen has talked very openly about the idea that he expects about the idea that he expects about the idea that he expects tremendous productivity gains from his tremendous productivity gains from his tremendous productivity gains from his engineers. But, he's not letting go of engineers. But, he's not letting go of engineers. But, he's not letting go of any of them, and he's told leaders any of them, and he's told leaders any of them, and he's told leaders publicly who are letting go of their publicly who are letting go of their publicly who are letting go of their employees that they just don't have the employees that they just don't have the employees that they just don't have the imagination to use their teams in the imagination to use their teams in the imagination to use their teams in the age of AI. They don't have the vision, age of AI. They don't have the vision, age of AI. They don't have the vision, the boldness to go out there and find the boldness to go out there and find the boldness to go out there and find new stuff for the company to get into new stuff for the company to get into new stuff for the company to get into and do. and do. and do. That's the kind of thing that I find That's the kind of thing that I find That's the kind of thing that I find really resonates. Is if you say, look, really resonates. Is if you say, look, really resonates. Is if you say, look, it's my job as a leader to come up with it's my job as a leader to come up with it's my job as a leader to come up with the larger vision for the company and the larger vision for the company and the larger vision for the company and what we're going after. I see AI as a what we're going after. I see AI as a what we're going after. I see AI as a tremendous productivity enhancer tremendous productivity enhancer tremendous productivity enhancer and and and then at that point you have you have then at that point you have you have then at that point you have you have their attention, right? You have trust their attention, right? You have trust their attention, right? You have trust because you are then saying, "This is because you are then saying, "This is because you are then saying, "This is not about taking something away. This is not about taking something away. This is not about taking something away. This is about expanding our horizons."

about expanding our horizons." about expanding our horizons." So, that's principle number one. So, that's principle number one. So, that's principle number one. Principle number two that I find that's Principle number two that I find that's Principle number two that I find that's really, really critical because AI is a really, really critical because AI is a really, really critical because AI is a whole org transformation approach. Like whole org transformation approach. Like whole org transformation approach. Like there there at some point it's going to there there at some point it's going to there there at some point it's going to come for the tools, it's going to come come for the tools, it's going to come come for the tools, it's going to come for the way you work. It's going to come for the way you work. It's going to come for the way you work. It's going to come for everything. And so, if you just for everything. And so, if you just for everything. And so, if you just start by saying, "It's going to come for start by saying, "It's going to come for start by saying, "It's going to come for everything. We're going to do AI as a everything. We're going to do AI as a everything. We're going to do AI as a whole. Everyone start doing AI." whole. Everyone start doing AI." whole. Everyone start doing AI." Well, one, people kind of don't believe Well, one, people kind of don't believe Well, one, people kind of don't believe it. And two, people don't really it. And two, people don't really it. And two, people don't really understand what that means for their understand what that means for their understand what that means for their day-to-day. day-to-day. day-to-day. And so, you have to start by saying, "We And so, you have to start by saying, "We And so, you have to start by saying, "We are picking a specific are picking a specific are picking a specific angle to get started on AI angle to get started on AI angle to get started on AI transformation here." Not just a transformation here." Not just a transformation here." Not just a generic, right? We're picking a specific generic, right? We're picking a specific generic, right? We're picking a specific place. And the way I tell people to pick place. And the way I tell people to pick place. And the way I tell people to pick a specific place is very simple. I said, a specific place is very simple. I said, a specific place is very simple. I said, "You need to pick something that "You need to pick something that "You need to pick something that actually drives your bottom line. It may actually drives your bottom line. It may actually drives your bottom line. It may expand revenue. It may cut your tooling expand revenue. It may cut your tooling expand revenue. It may cut your tooling costs. I don't know. But you need to costs. I don't know. But you need to costs. I don't know. But you need to find something that has a meaningful find something that has a meaningful find something that has a meaningful impact on your bottom line so you are impact on your bottom line so you are impact on your bottom line so you are motivated to pursue it. It needs to be motivated to pursue it. It needs to be motivated to pursue it. It needs to be tied to tied to tied to things that AI has already demonstrated things that AI has already demonstrated things that AI has already demonstrated to do well. If you If you're doing this, to do well. If you If you're doing this, to do well. If you If you're doing this, don't pick a new unsolved problem in AI don't pick a new unsolved problem in AI don't pick a new unsolved problem in AI as your first place to get started. We as your first place to get started. We as your first place to get started. We need to enable them to be more need to enable them to be more need to enable them to be more effective. And so, for you know, X effective. And so, for you know, X effective. And so, for you know, X routine calls we're moving to AI agents.

routine calls we're moving to AI agents. routine calls we're moving to AI agents. For more complex calls, that's what our For more complex calls, that's what our For more complex calls, that's what our AI agent humans are for. Or they say, AI agent humans are for. Or they say, AI agent humans are for. Or they say, "We want to maintain the human touch. We "We want to maintain the human touch. We "We want to maintain the human touch. We want humans to talk to humans. But AI is want humans to talk to humans. But AI is want humans to talk to humans. But AI is going to be working in the background to going to be working in the background to going to be working in the background to go through the CRM, to go through the go through the CRM, to go through the go through the CRM, to go through the customer records, to go through customer records, to go through customer records, to go through everything about our policies so that everything about our policies so that everything about our policies so that every call is a fantastic call." And every call is a fantastic call." And every call is a fantastic call." And when you sit down and you figure out when you sit down and you figure out when you sit down and you figure out that area of impact, And it doesn't have that area of impact, And it doesn't have that area of impact, And it doesn't have to be customer service. There are some to be customer service. There are some to be customer service. There are some initial ones that are in engineering initial ones that are in engineering initial ones that are in engineering because they feel like they have because they feel like they have because they feel like they have technical skills and all of that to technical skills and all of that to technical skills and all of that to implement. You got to pick that and to implement. You got to pick that and to implement. You got to pick that and to talk about it publicly. Even if it's talk about it publicly. Even if it's talk about it publicly. Even if it's just for that one team, you're saying to just for that one team, you're saying to just for that one team, you're saying to the whole company, we are starting this the whole company, we are starting this the whole company, we are starting this journey together. This is where we're journey together. This is where we're journey together. This is where we're starting and why. So, maybe we're starting and why. So, maybe we're starting and why. So, maybe we're starting in customer service, maybe starting in customer service, maybe starting in customer service, maybe we're starting with an engineering we're starting with an engineering we're starting with an engineering project cuz we want to learn AI native project cuz we want to learn AI native project cuz we want to learn AI native ways of working. Those are two common ways of working. Those are two common ways of working. Those are two common ways of of starting. But either way, you ways of of starting. But either way, you ways of of starting. But either way, you talk about the scope, talk publicly talk about the scope, talk publicly talk about the scope, talk publicly about what success looks like. Success about what success looks like. Success about what success looks like. Success should not just be people use a tool. should not just be people use a tool. should not just be people use a tool. Success should be something about the Success should be something about the Success should be something about the way we work changes so that our customer way we work changes so that our customer way we work changes so that our customer experience gets better, so that we are experience gets better, so that we are experience gets better, so that we are able to deliver more value to the able to deliver more value to the able to deliver more value to the business, something that's going to be business, something that's going to be business, something that's going to be tangible and meaningful beyond just the tangible and meaningful beyond just the tangible and meaningful beyond just the activity changing. That's really, really activity changing. That's really, really activity changing. That's really, really important because what we find is that important because what we find is that important because what we find is that if you just talk about the activity if you just talk about the activity if you just talk about the activity changing, you get into the position Uber changing, you get into the position Uber changing, you get into the position Uber got into very publicly earlier this year got into very publicly earlier this year got into very publicly earlier this year where Uber said, "You know what? We are where Uber said, "You know what? We are where Uber said, "You know what? We are really regretting the amount of money we really regretting the amount of money we really regretting the amount of money we are spending on our tokens." Because are spending on our tokens." Because are spending on our tokens." Because they just been encouraging usage like they just been encouraging usage like they just been encouraging usage like crazy. And that sends a very crazy. And that sends a very crazy. And that sends a very contradictory message to team members as

contradictory message to team members as contradictory message to team members as well, doesn't it? Because if you've been well, doesn't it? Because if you've been well, doesn't it? Because if you've been told and you're somewhat skeptical, "Use told and you're somewhat skeptical, "Use told and you're somewhat skeptical, "Use AI. Use AI. Use AI." You finally use AI AI. Use AI. Use AI." You finally use AI AI. Use AI. Use AI." You finally use AI and then you get told, "Well, don't use and then you get told, "Well, don't use and then you get told, "Well, don't use AI because we have a token budget." Now AI because we have a token budget." Now AI because we have a token budget." Now it's extra confused and you just feel it's extra confused and you just feel it's extra confused and you just feel like leadership doesn't know what like leadership doesn't know what like leadership doesn't know what they're doing. You got to pick where you they're doing. You got to pick where you they're doing. You got to pick where you focus, right? You got to pick and scope focus, right? You got to pick and scope focus, right? You got to pick and scope the nature of the engagement, make sure the nature of the engagement, make sure the nature of the engagement, make sure it's meaningful, it's meaningful, it's meaningful, talk about it publicly to the whole talk about it publicly to the whole talk about it publicly to the whole business, make sure you prepare for it. business, make sure you prepare for it. business, make sure you prepare for it. That's That's where budgeting for it, That's That's where budgeting for it, That's That's where budgeting for it, make sure you've got the talent on the make sure you've got the talent on the make sure you've got the talent on the ground for it. And the talent, I want to ground for it. And the talent, I want to ground for it. And the talent, I want to give you a a special note on that one. give you a a special note on that one. give you a a special note on that one. You need to pick a spot in the company You need to pick a spot in the company You need to pick a spot in the company where you have a passionate supporter of where you have a passionate supporter of where you have a passionate supporter of AI in a management or middle middle AI in a management or middle middle AI in a management or middle middle leadership role and they need to be able leadership role and they need to be able leadership role and they need to be able to get excited about this. If they're to get excited about this. If they're to get excited about this. If they're not excited, if if your team level not excited, if if your team level not excited, if if your team level leaders are not excited about the AI leaders are not excited about the AI leaders are not excited about the AI rollout that you're doing, the director, rollout that you're doing, the director, rollout that you're doing, the director, VP, all the senior folks, is not going VP, all the senior folks, is not going VP, all the senior folks, is not going to matter. Like they can talk all they to matter. Like they can talk all they to matter. Like they can talk all they want in meetings, but if team-level want in meetings, but if team-level want in meetings, but if team-level leadership isn't passionate about this, leadership isn't passionate about this, leadership isn't passionate about this, nothing is getting done. Nothing's nothing is getting done. Nothing's nothing is getting done. Nothing's getting done. And so I want to remind getting done. And so I want to remind getting done. And so I want to remind everyone who hears this, everyone who hears this, everyone who hears this, AI is at the end of the day a personal AI is at the end of the day a personal AI is at the end of the day a personal transformation journey as much as a transformation journey as much as a transformation journey as much as a public transformation. And so public transformation. And so public transformation. And so part of the story that we have to tell part of the story that we have to tell part of the story that we have to tell as leaders in this conversation is that as leaders in this conversation is that as leaders in this conversation is that we are going through this together we are going through this together we are going through this together ourselves. So leaders are needing to ourselves. So leaders are needing to ourselves. So leaders are needing to learn how to use AI and talk publicly learn how to use AI and talk publicly learn how to use AI and talk publicly about what works and also what doesn't.

about what works and also what doesn't. about what works and also what doesn't. The third thing that I want to talk The third thing that I want to talk The third thing that I want to talk about about about is I want to talk about the ongoing is I want to talk about the ongoing is I want to talk about the ongoing nature of these AI tools and evolution nature of these AI tools and evolution nature of these AI tools and evolution and how you know that you are able to and how you know that you are able to and how you know that you are able to get real value and use that real value get real value and use that real value get real value and use that real value to drive sustainable change through the to drive sustainable change through the to drive sustainable change through the business. And so this is this is an business. And so this is this is an business. And so this is this is an entangled conversation, but stay with entangled conversation, but stay with entangled conversation, but stay with me. AI is evolving quickly. We all know me. AI is evolving quickly. We all know me. AI is evolving quickly. We all know that. I talk about it all the time. AI that. I talk about it all the time. AI that. I talk about it all the time. AI agents are capable of doing more and agents are capable of doing more and agents are capable of doing more and more complex jobs. As they do those more more complex jobs. As they do those more more complex jobs. As they do those more complex jobs, we have to evolve our complex jobs, we have to evolve our complex jobs, we have to evolve our harnesses to work with them. So that harnesses to work with them. So that harnesses to work with them. So that means we have to evolve how we call means we have to evolve how we call means we have to evolve how we call tools in in our whole company. That how tools in in our whole company. That how tools in in our whole company. That how we how we use data in our whole company. we how we use data in our whole company. we how we use data in our whole company. All of these things that are essentially All of these things that are essentially All of these things that are essentially systems of information processing in the systems of information processing in the systems of information processing in the business have to evolve to work with AI. business have to evolve to work with AI. business have to evolve to work with AI. First, you've got to determine your First, you've got to determine your First, you've got to determine your criteria of success. And this is where I criteria of success. And this is where I criteria of success. And this is where I will be really blunt with you. If your will be really blunt with you. If your will be really blunt with you. If your first AI rollout doesn't go well, most first AI rollout doesn't go well, most first AI rollout doesn't go well, most people either bluntly processed because people either bluntly processed because people either bluntly processed because they say it's AI, we just got to get it they say it's AI, we just got to get it they say it's AI, we just got to get it done, or done, or done, or they take the wrong lesson and walk away they take the wrong lesson and walk away they take the wrong lesson and walk away from it entirely and just take a break from it entirely and just take a break from it entirely and just take a break from AI, which I've also seen happen.

from AI, which I've also seen happen. from AI, which I've also seen happen. And instead what they should be doing is And instead what they should be doing is And instead what they should be doing is they should be looking at specific they should be looking at specific they should be looking at specific lessons learned. Was there not a real lessons learned. Was there not a real lessons learned. Was there not a real managerial commitment to AI there? Were managerial commitment to AI there? Were managerial commitment to AI there? Were team members not given the tools to team members not given the tools to team members not given the tools to actually understand how AI works, where actually understand how AI works, where actually understand how AI works, where the expected failure modes are, how to the expected failure modes are, how to the expected failure modes are, how to use AI successfully, use AI successfully, use AI successfully, and what success looks like in their and what success looks like in their and what success looks like in their role. Uh when you did the technical role. Uh when you did the technical role. Uh when you did the technical rollout, did the tool actually do a job rollout, did the tool actually do a job rollout, did the tool actually do a job that actually helped? I've seen so many that actually helped? I've seen so many that actually helped? I've seen so many cases where there's like co-pilot cases where there's like co-pilot cases where there's like co-pilot rollout, and people don't use it because rollout, and people don't use it because rollout, and people don't use it because it doesn't actually help. You need to it doesn't actually help. You need to it doesn't actually help. You need to get at the harsh ground truth reality of get at the harsh ground truth reality of get at the harsh ground truth reality of whether AI is adding value to the whether AI is adding value to the whether AI is adding value to the business. And so, first find the truth business. And so, first find the truth business. And so, first find the truth out. Second, once you know the truth, out. Second, once you know the truth, out. Second, once you know the truth, you either need to take learnings away you either need to take learnings away you either need to take learnings away and try somewhere else if the pilot and try somewhere else if the pilot and try somewhere else if the pilot wasn't successful. Which data, tool, and wasn't successful. Which data, tool, and wasn't successful. Which data, tool, and system implications there are for your system implications there are for your system implications there are for your specific tech stack if you expand what specific tech stack if you expand what specific tech stack if you expand what you're doing to other departments, if you're doing to other departments, if you're doing to other departments, if you expand what you're doing to other you expand what you're doing to other you expand what you're doing to other teams, teams, teams, ahead of time. And the reason why is ahead of time. And the reason why is ahead of time. And the reason why is that people either try and bite off the that people either try and bite off the that people either try and bite off the entire org at that point, which is entire org at that point, which is entire org at that point, which is disastrous because it's so expensive and disastrous because it's so expensive and disastrous because it's so expensive and it's so complicated, or they it's so complicated, or they it's so complicated, or they misunderstand what they have to actually misunderstand what they have to actually misunderstand what they have to actually address from a tech perspective. You address from a tech perspective. You address from a tech perspective. You really need to be willing to dive into really need to be willing to dive into really need to be willing to dive into the technical details on to be the technical details on to be the technical details on to be successful. And those technical details, successful. And those technical details, successful. And those technical details, as I've been sharing, turn into people as I've been sharing, turn into people as I've been sharing, turn into people details. Because people are then going details. Because people are then going details. Because people are then going to be like, "Okay, so suddenly all of to be like, "Okay, so suddenly all of to be like, "Okay, so suddenly all of our memory files are in markdown. What

our memory files are in markdown. What our memory files are in markdown. What does this mean for me?" Right? Like, "Do does this mean for me?" Right? Like, "Do does this mean for me?" Right? Like, "Do do do I write in the wiki?" You have to do do I write in the wiki?" You have to do do I write in the wiki?" You have to think about what is the long-term think about what is the long-term think about what is the long-term capability that I am evolving into the capability that I am evolving into the capability that I am evolving into the heart of the business, and how do I make heart of the business, and how do I make heart of the business, and how do I make sure that I do it in a way sure that I do it in a way sure that I do it in a way that allows both humans and AI agents to that allows both humans and AI agents to that allows both humans and AI agents to work together. And and that sentence in work together. And and that sentence in work together. And and that sentence in and of itself is actually a really and of itself is actually a really and of itself is actually a really compelling sentence to share with compelling sentence to share with compelling sentence to share with everyone. Major studies are coming out everyone. Major studies are coming out everyone. Major studies are coming out showing that one of the biggest impacts showing that one of the biggest impacts showing that one of the biggest impacts of AI, and it is on jobs, is not of AI, and it is on jobs, is not of AI, and it is on jobs, is not necessarily AI killing jobs, although necessarily AI killing jobs, although necessarily AI killing jobs, although that does happen in places and I'm not that does happen in places and I'm not that does happen in places and I'm not denying, it's that AI is blurring denying, it's that AI is blurring denying, it's that AI is blurring boundaries between jobs. boundaries between jobs. boundaries between jobs. And that leads to a lot of ambiguity, at And that leads to a lot of ambiguity, at And that leads to a lot of ambiguity, at least a lot of confusion, at least to a least a lot of confusion, at least to a least a lot of confusion, at least to a lot of people trying to figure out where lot of people trying to figure out where lot of people trying to figure out where their careers are. We either we need their careers are. We either we need their careers are. We either we need them, which is also true, because them, which is also true, because them, which is also true, because everyone else is adding them, and so you everyone else is adding them, and so you everyone else is adding them, and so you need them to be competitive, and so the need them to be competitive, and so the need them to be competitive, and so the business won't work without them, or business won't work without them, or business won't work without them, or because we have a wider vision, which is because we have a wider vision, which is because we have a wider vision, which is always more preferable. People like always more preferable. People like always more preferable. People like vision. People buy vision. People want vision. People buy vision. People want vision. People buy vision. People want to see the wider horizon. And to see the wider horizon. And to see the wider horizon. And in that world, this is how we're going in that world, this is how we're going in that world, this is how we're going to get that done. And you need to tell to get that done. And you need to tell to get that done. And you need to tell the story in a way that that supersedes the story in a way that that supersedes the story in a way that that supersedes the technical details. And so, when it the technical details. And so, when it the technical details. And so, when it comes time to move from the pilot into a comes time to move from the pilot into a comes time to move from the pilot into a larger scale, and you've talked through larger scale, and you've talked through larger scale, and you've talked through the technical details of what you need the technical details of what you need the technical details of what you need to change, when you share that message to change, when you share that message to change, when you share that message more widely, talk about the impact to more widely, talk about the impact to more widely, talk about the impact to the customer and the benefit to the the customer and the benefit to the the customer and the benefit to the business that you experienced in the business that you experienced in the business that you experienced in the pilot. Then, talk through at a high pilot. Then, talk through at a high pilot. Then, talk through at a high level why AI matters to the business as level why AI matters to the business as level why AI matters to the business as a whole, and why you are adding AI a whole, and why you are adding AI a whole, and why you are adding AI agents into the mix. Then, talk through

agents into the mix. Then, talk through agents into the mix. Then, talk through the long-term vision that you have for the long-term vision that you have for the long-term vision that you have for AI agents and how information flows, AI agents and how information flows, AI agents and how information flows, without diving too far into technical without diving too far into technical without diving too far into technical detail, especially in the world after detail, especially in the world after detail, especially in the world after the hugging face attack, where everyone the hugging face attack, where everyone the hugging face attack, where everyone is asking themselves, "What are we doing is asking themselves, "What are we doing is asking themselves, "What are we doing here? here? here? What are we doing here? Are we just What are we doing here? Are we just What are we doing here? Are we just adding AI agents into the mix, and adding AI agents into the mix, and adding AI agents into the mix, and they're going to cyber attack other they're going to cyber attack other they're going to cyber attack other other companies? Are they going to other companies? Are they going to other companies? Are they going to experience cyber attacks themselves? experience cyber attacks themselves? experience cyber attacks themselves? You need to be willing to say, "These You need to be willing to say, "These You need to be willing to say, "These are the safeguards we're putting in are the safeguards we're putting in are the safeguards we're putting in place to protect you, to protect your place to protect you, to protect your place to protect you, to protect your families, to protect the companies that families, to protect the companies that families, to protect the companies that we work with, to protect ourselves, and we work with, to protect ourselves, and we work with, to protect ourselves, and ensure that we're not getting hit by by ensure that we're not getting hit by by ensure that we're not getting hit by by cyber attacks or malicious actors from cyber attacks or malicious actors from cyber attacks or malicious actors from AI agents. This is how we are evaluating AI agents. This is how we are evaluating AI agents. This is how we are evaluating AI agent output. These are the the AI agent output. These are the the AI agent output. These are the the things that AI agents cannot touch, and things that AI agents cannot touch, and things that AI agents cannot touch, and how we're protecting them, at least at a how we're protecting them, at least at a how we're protecting them, at least at a high level." And then, at that point, high level." And then, at that point, high level." And then, at that point, you need to say, "And this is what is you need to say, "And this is what is you need to say, "And this is what is sacred, right? This is what we are sacred, right? This is what we are sacred, right? This is what we are committing to maintaining a human edge committing to maintaining a human edge committing to maintaining a human edge and advantage for." And that last part and advantage for." And that last part and advantage for." And that last part is really important, cuz people will is really important, cuz people will is really important, cuz people will will jump through a lot if they know will jump through a lot if they know will jump through a lot if they know where you see the vision for humans in where you see the vision for humans in where you see the vision for humans in their business.

their business. their business. And that's what I want to focus on for And that's what I want to focus on for And that's what I want to focus on for the last part of this video. the last part of this video. the last part of this video. We know at this point where we're seeing We know at this point where we're seeing We know at this point where we're seeing a lot of value in AI. We're seeing it in a lot of value in AI. We're seeing it in a lot of value in AI. We're seeing it in coding tasks for sure. coding tasks for sure. coding tasks for sure. We are seeing it in the ability of AI to We are seeing it in the ability of AI to We are seeing it in the ability of AI to accelerate iteration across the accelerate iteration across the accelerate iteration across the business, whether it's in coding, business, whether it's in coding, business, whether it's in coding, whether it's in writing, uh whether it's whether it's in writing, uh whether it's whether it's in writing, uh whether it's in mathematics. in mathematics. in mathematics. And we're seeing it in deep deep And we're seeing it in deep deep And we're seeing it in deep deep analysis. So, can you build me a 20 a analysis. So, can you build me a 20 a analysis. So, can you build me a 20 a 20-tab spreadsheet that analyzes the 20-tab spreadsheet that analyzes the 20-tab spreadsheet that analyzes the stock position is something that AI is stock position is something that AI is stock position is something that AI is very very good at. It's because what we very very good at. It's because what we very very good at. It's because what we find really consistently, if you want find really consistently, if you want find really consistently, if you want something professional, you have to have something professional, you have to have something professional, you have to have a human go through and take out the a human go through and take out the a human go through and take out the LLM-isms. You have to have a human go LLM-isms. You have to have a human go LLM-isms. You have to have a human go through and make sure actually aligns to through and make sure actually aligns to through and make sure actually aligns to the larger strategy, etc. Talk about the the larger strategy, etc. Talk about the the larger strategy, etc. Talk about the things that the human is doing. And and things that the human is doing. And and things that the human is doing. And and I will tell you, there are people in San I will tell you, there are people in San I will tell you, there are people in San Francisco who will say, "No, you should Francisco who will say, "No, you should Francisco who will say, "No, you should not do this. No, you should not do this not do this. No, you should not do this not do this. No, you should not do this because agents are just going to keep because agents are just going to keep because agents are just going to keep magically getting better forever. And if magically getting better forever. And if magically getting better forever. And if they get that magically get better they get that magically get better they get that magically get better forever, then there will be nothing for forever, then there will be nothing for forever, then there will be nothing for the humans to do."

the humans to do." the humans to do." I don't believe in that world. I don't believe in that world. I don't believe in that world. I don't see it. I actually see a lot of I don't see it. I actually see a lot of I don't see it. I actually see a lot of changing and evolving roles, but a ton changing and evolving roles, but a ton changing and evolving roles, but a ton of work for humans. The people I know of work for humans. The people I know of work for humans. The people I know who are the busiest are the people who who are the busiest are the people who who are the busiest are the people who are in AI and around AI. Not because are in AI and around AI. Not because are in AI and around AI. Not because they're necessarily training models, but they're necessarily training models, but they're necessarily training models, but because figuring out how to work with because figuring out how to work with because figuring out how to work with models successfully is the hardest models successfully is the hardest models successfully is the hardest challenge that I think we've had to face challenge that I think we've had to face challenge that I think we've had to face in 500 years of the corporation. It's in 500 years of the corporation. It's in 500 years of the corporation. It's really really hard. And so, we need to really really hard. And so, we need to really really hard. And so, we need to be honest about how engineers are moving be honest about how engineers are moving be honest about how engineers are moving toward being system designers. They're toward being system designers. They're toward being system designers. They're moving toward being writing evals and moving toward being writing evals and moving toward being writing evals and actually writing how evals deliver code actually writing how evals deliver code actually writing how evals deliver code in a loop so that you can actually push in a loop so that you can actually push in a loop so that you can actually push the agent against a standard and deliver the agent against a standard and deliver the agent against a standard and deliver working code over time. But it takes working code over time. But it takes working code over time. But it takes work, it takes patience, and it takes work, it takes patience, and it takes work, it takes patience, and it takes talking about the human impacts of talking about the human impacts of talking about the human impacts of what's going on. So, there you go. Those what's going on. So, there you go. Those what's going on. So, there you go. Those are my three big principles that I I are my three big principles that I I are my three big principles that I I people through as I start to talk about people through as I start to talk about people through as I start to talk about AI transformation. And And that is how I AI transformation. And And that is how I AI transformation. And And that is how I address the question that I get. What do address the question that I get. What do address the question that I get. What do you do when people hate AI? What do you you do when people hate AI? What do you you do when people hate AI? What do you do when people in the organization do when people in the organization do when people in the organization aren't committed to AI? How do you walk aren't committed to AI? How do you walk aren't committed to AI? How do you walk through that process with the business?

through that process with the business? through that process with the business? I hope this has been helpful. If you've I hope this has been helpful. If you've I hope this has been helpful. If you've had a terrible rollout experience, put had a terrible rollout experience, put had a terrible rollout experience, put that in the comments. And if you've had that in the comments. And if you've had that in the comments. And if you've had a great one, or if you have things that a great one, or if you have things that a great one, or if you have things that you'd add to this process, put that in you'd add to this process, put that in you'd add to this process, put that in the comments, too. I'll see you next the comments, too. I'll see you next the comments, too. I'll see you next time.