Hyper Adaptive: How to Rewire Your Entire Organization for AI | Melissa Reeve

Unlocked Professional: AI and Future of Work: I like to say that AI is easy to use but not easy to learn. And I equate it to a piano. So anybody can walk up to a piano and start dinking away on the keys, but to actually learn to play a song, to actually be a concert pianist, takes a lot of learning and a lot of practice. And from my experience, some of the things that companies or enterprises and or companies in general don't necessarily spend enough on would be training for one. But also, as you mentioned, they're looking at AI and saying, like, this it's gonna s solve all these problems, but they're really not putting together a strong enough plan to implement these systems, ensure folks are trained on them, but leveraging them in the best way. When you do that and you're bringing in these new tools and systems, a lot of times have a staff that's not really getting the value out of them that they could. I sometimes reference the washing machine. So I don't know that anybody really regrets not having to wash clothes by hand. Like I I certainly don't. And when you when you think about the the job shift, it it wasn't like jobs around washing clothes went away, but they sure did shift. All of a sudden we're building the machines. We're we're having to maintain the machines. And that's not to say that everybody has the same aptitude or attitude needed to make that shift. But you know who's best qualified to build, monitor, and maintain those automations. Is the people who've been doing the work for 20 years. Right. You know, if you've got an automation around processing invoice, it's that person who's been doing it day in and day out that knows when it's going off the rails. Most companies right now are trying to win a race in a carb that's built over a hundred years ago. They have the newest, fastest engine that's AI, but with the wheels and the steering wheel, those are still from 1911. So what happens? Leaders buy the new shiny tools. Nothing actually changes because the Operating system of the office, the way that decisions get made, the way that people are managed, the way ideas move is still broken. You know who figured this out early is Blockbuster. They had the chance to buy Netflix for $50 million, but they passed. And we know how that story ends. Our guest today has a very special superpower. She rewrites companies from the inside out. She has spent 25 years helping over one million people learn how to work better together. Now she's on a mission to help leaders stop doing what she calls random acts of AI, just throwing tools at problems and hoping something sticks, and start building a real safety net for the future. She teaches leaders how to take the extra time that new tools create and turn it into a special advantage for the whole team. And she put everything she knows into a brand new book, hyper adaptive, rewiring to become an AI native enterprise, dropping. May twelfth. If you're a leader trying to figure out where to start, this is the book. Melissa, welcome to the show. Thanks so much for having me. It's a pleasure to be here. Great, Melissa. Yep. Really looking forward to what we're going to discuss here today. I'll kick it off with the first question. Sounds great. So you studied Japanese literature and language in college. What was it about that kind of communication that first made you curious about how teams and workplaces actually function? Yeah, I feel like the two are tangential. So I dove into Japanese in high school because quite honestly, we needed four semesters of a foreign language. I'd already burned through French and German and Spanish, and we started offering Japanese and it took. But while I was studying Japanese literature and language, I got exposed to many of the industrial engineering best practices that were happening in Japan at the time. This is 1990s, Japan Inc was a big thing. And particularly the Toyota production system. So I spent some time on the factory floor of Hino Motor Company and I got to see the Toyota production system in action. And what I really saw there was how somebody on the front line in an observation that happened there can trickle through the entire system to change the way an entire organization operated. And ⁓ so while I didn't stick with Japanese literature and language, I think what it was tugging on was my ability to translate concepts. And that in particular has served me well as I've gone throughout my career. I couldn't agree with you more. I think being able to figure out concepts, analyze concepts, and leverage those concepts is definitely the wave of the future. I talk about this a lot on the channel and the brand, the just being just a normal task. doer essentially a lot of that is being replaced by AI. So critical con thinking component that you mentioned agreed is very valuable for the future. So for start and I never know like I never know if you want me to add on to that or if you just want to keep going. Up to you up to you. If you feel yeah up to you. Yeah I couldn't agree with you more. In fact I was guest lecturing the other week and the level of anxiety in these college classrooms is so high. And I think what needs to really sink in is that we're going from doing the task to evaluating the output of the task. And so for all of those teachers that may be listening, there only might be one or two. But I would just invite you to invite AI into your classroom because it's not going away. And this notion that AI, using AI is cheating, I think is really cheating your students out of pulling that critical thinking forward. In really learning how to evaluate this critical new technology that's arrived on the scene. Definitely. For several years, you were part of a local sailing fleet. How does time out on the water help you stay sharp and creative when you're working with a slow-moving corporate office? Yeah, we can't rush nature, right? And we can't control nature. So I think there's so many lessons to be learned out on the water, including you might get dunked. It was a very competitive environment. So you had to stay really attuned to what is around you. And I tell you what, there are times when there's no wind and you're literally bobbing on the lake. So you I think if you're in an organization, you've got to prepare be prepared for it all. You've got to be prepared to bob on the lake. You've got to be when prepared when the wind is howling and how to adjust your sails so that you can take advantage of the wind. And right now, I gotta tell you, the wind is howling with AI, right? Stuff is coming at us so fast we can hardly hardly absorb it. And in those cases, you almost have to bleed off the wind and not take it all in. Otherwise you are gonna flip over. S something I connect with on that, and I agree with you completely, is and I was actually just talking to my wife about this recently, is like a lot of times at the end of the day. ⁓ these days my brain hurts and I'm wondering why that is. And I have a number of different projects that I'm working on, different businesses and podcasts, all this fun stuff. And it's just it's and I and I saw an article about this recently too that I thought was super interesting. But ultimately what I feel is happening is that since we are moving away from more task to more creative thought and design and planning. You're leveraging different components and or parts of your brain now, right? That you maybe not weren't having to maximize. Th this article I read recently was talking about how jobs used to be 80% of task and 20% thought, right? And or depending upon your role or function. But a lot of it was like, okay, let's figure out what where we're trying to go today. And then 80% of our job is getting there. Where that's flipping now. Now 80% of your job is really figuring out the design, what's gonna happen. And then you have AI leveraging this tool, or you're then you're leveraging this tool to do a lot of that heavy function task. So ultimately, like I said, my brain hurts at the end of the day. I think that's a positive thing, but couldn't agree with you more that I think leveraging that in school and in the classroom is important because it's a different, it's not it's a different way of thinking, but it's also a different way of leveraging your your work andor thought. Yeah, and there's so much to unpack there. There was a far a recent Harvard Business School study that took a look at this and the they came to the same conclusion you did naturally, which is work is becoming more intense with AI. And it's because people are expanding into what I call adjacent competencies. So before you wouldn't have even a tr tried to maybe create that video because you didn't have the skills, but now you might be using AI to generate a video. Or you might not have done the analysis because you didn't have those skills. But now with AI you can. And so that intensity, I think, is something we need to observe and be mindful of because it can get too intense. And then the other thing you all alluded to was what I call continuous partial execution. And we're starting up so many prompts that it it takes us a minute to figure out which of these 200 tabs are open, which ones we're s we needed to come back to. As it was running in the background. Yeah. So I think you're right in the same boat as everybody else in terms of that intensity and just how much stuff we have going on. So you talk about the nineteen eleven operating system, which is the old rigid way that most companies still run people. When did you know that this old system was not just slowing teams down, but blocking them from using the new tech altogether? Yeah, and I think most people intuitively sense that something is broken in the enterprise. Anybody who has worked in a company and felt frustrated by the amount of time it takes to get an expense report submitted and processed and reimbursed. Anybody who's had a great idea and have it stalled out for six weeks, six months, six years while people ⁓ while the forces that be try and make a decision, it just feels broken. And I got a taste of that when I started to interact with people on the digital transformation front. And those are the people who were trying to embed what we called at that time digital native ways of working into organizations. The same disruption that we saw, you mentioned Blockbuster at the top of the show, Blockbuster and Netflix and Nokia are always the well-cited examples. And so that was really my clue that the system is broken. I saw it firsthand there and I saw people trying to change it. And so when AI came on the scene, it just dropped in right away that this was going to be yet another forcing function that was going to shine a light on these outdated operating models that we haven't been able to shed yet. And w I still hold out hope that we're going to implement. better ways of working in our organizations. What's your prediction? How long is it going to take before you think we see that dramatic shift? You think we're five, ten or more or less? I don't know that you can put a time frame on it because I think we're dealing with a bell curve. Somebody was talking about on a call the credit union out in rural Wisconsin. And I don't know that credit union is going to feel the pressure of AI for maybe five years, eight years, ten years. I don't know. But then on the other hand, you've got these AI native companies, these companies that are right now putting the pressure. Even if you take a look at OpenAI or Anthropic putting the pressure on the Microsofts, on the Googles, on the legacy organizations, they have a much smaller window and you see them reacting. I just read that Google made like a $40 billion investment in Anthropic. That is them. Saying, hey, we are in some serious wind here, and we gotta have some ballast to keep our ship upright if we're gonna sail through the AI storm. Did I hear that right? You said Google made an investment in Anthropic or is purchase. Yeah, their competing product. I know as a user of multiple tools, I there is a significant difference between Cloud, Anthropic, and Gemini, Chat GBT. I don't use it as much. They're somewhere in The competitive space, but it's pretty amazing actually, you know, how Anthropic and Cloud has been able to develop their tools to align to both enterprises, but also also individuals and give you like a taste of like agentic capabilities within cowork. And obviously, if you're looking to take it little bit further, Cloud Code and yeah, just again, me nerding out and trying all these different tools. I can tell you my experience with Google anti-gravity, the competition. competitor or two Cloud Code is I not I'm not as satisfied with that experience, I guess you could say, as somebody that's not extremely technical. It's just the learning curve on it andor the capabilities that I experienced just seemed yeah. Seems like they definitely are making a good decision by making that investment. For sure it feels very defensive against Microsoft because Microsoft has already invested in Anthropic and Claude is embedded into all those Microsoft products. I don't know if you ever use those plugins. And so it only makes sense that Google would want them in your in their products as well. Definitely makes sense. You built the focus framework. Can you walk us through how a leader uses it to pick the right work to focus on? Sure. So one of the headlines that keeps coming forward is 80% of AI initiatives fail. And do feel like there's a lot of reasons why that can happen. But one is random acts of AI, which is the way we've rolled AI out is what I call the hand wave. So it it looks something like go play with AI and just assuming that the right things will happen. But the problem with AI is that there are so many use cases that you don't know if you're using the right ones. So what the focus framework does is it's just a simple mnemonic. And it helps you to make sure that you're getting value out of where you're putting your efforts with AI. So the F starts with fit. Is it, do you have the organizational fit around? Does it fit you with your strategy? The O is organizational pull, meaning if you build this thing that fits with the strategy, is the organization actually going to use it? And the C is capabilities. So do you have the capabilities to build this AI solution. A lot, there's a lot of people out there who are shooting for the moon and then they get halfway through and they realize they're over their head. That's part of that 80%. The U is underlying data. So that's great. You can build it. It might fit with your strategy. People want it, but if your data is all screwed up, the results are going to be screwed up. And the S stands for success metrics, which is can you measure the needle that got moved? And it's so important because there's so many solutions out there that are released and then people don't know if they're making an impact or not. And so I think just that simple mnemonic and putting it through those filters really helps you identify those high impact use cases. Something that I take away from that, if you think that you're going to build this robust system with AI right off the box that's gonna solve out of the gates, that's gonna solve all of your problems, you're sadly mistaken. It definitely doesn't work right that it's you can solve for pieces of the puzzle and you have to re refine those different pieces over the course of time. But I've noticed the same thing even with small businesses that I talk to that are looking for different types of solutions. A lot of times they give me their laundry list of A through Z and I say, Hey, that's an exciting idea, but let's it's a lot to tackle, right? Let's break it down piece by piece and make sure each function works. It's I as an example here, just in mind, I'm thinking about the way a car is built. And you do have to figure out how the tires are built, how the steering wheel, all those components and pieces of it before you can and make sure that they all work and function properly before you put it all together and throw it out on the road. And so I think AI, AI and that journey that enterprises are in and companies are in, you do need to have components and breakdowns and pieces that you're using. It's just it's not logical to be able to go A through Z and build some extreme tool right off the bat. That's going solve all your problems. Yeah. In fact, I talked to a mid-sized organization and this is what not to do, but they really wanted to build a sophisticated orchestration. They took about six months to do it. They invested quite a bit of money to do this. And of course, everybody had high hopes for it. And then ultimately they got about 80% of the way there because the technology wasn't quite there yet. And so they burned through six months and political capital and goodwill, and people got cynical around what AI could do by shooting ahead of where they were. And just for your listeners, I think much better guidance is to start small and start chipping away. And what you start to do there is you start to build a foundation that you can build on that's much more stable. In terms of AI literacy, in terms of your processes, in terms of moving the organization forward rather than biting off more than you can chew? Absolutely. I had a thought. I'll probably get back to that later, but we'll go on to the next one. Great, Melissa. So you also developed the AI learning flywheel, a system that keeps a team growing on its own without a trainer in the room every day. What does it take to get that wheel moving the very first time? Yeah, and before we start talking about the flywheel, I just want to set the stage for it, which is I like to say that AI is easy to use, but not easy to learn. And I equate it to a piano. So anybody can walk up to a piano and start dinking away on the keys, but to actually learn to play a song, to actually be a concert pianist, takes a lot of learning and a lot of practice. And I feel again, what's happening in a lot of organizations. Is that it's left to happenchance. Left to happen chance. It's left to happenstance, or it's we spun up a video library, or people are just trying to learn very casually on their own time. Instead of doing that, I think organizations need to be very deliberate in terms of putting the structures in place where continuous learning can happen. And so that's where the AI learning flywheel comes in. Is the start of the flywheel is the spark. This is where people really have their aha moments with AI and figure out what they can do. And that happens in in my context, what I call an applied AI workshop, but you want to create a so conditions for social learning to happen and those sparks to fly. Then they start to spread through what I call your AI leads. These are typically your AI champions, your power users, the ones who are really leaning into AI. And what they do is they support the other people in the organization. Many people have identified these AI leads, but they're not supporting them programmatically. So they're not taking them and saying, here's how you can continue to upgrade your knowledge so that you can spread it in the organization. Then we want to scale it. And we scale it through what I call an AI activation hub. And if you think about it and you think about the speed at which AI is moving forward, if you have an organization of 200, 500, 5000, 500,000 people, you don't want all of those people having to keep track of the advancements. And so you spin up this AI activation hub. And it's fractal, right? So you can have one, you can have 500 depending on the size of your organization, but it's their job to monitor what's going on with AI. Let's just say 4.7 got released. They contextualize it for their area of the organization. They spread it down to the AI leads, who then get it into the hands of the practitioners. And in this way, you create this flywheel of learning that keeps the organization updating itself. So the hub that you mentioned, is that what are you saying build a team? That's their core function, or are you taking are you saying take representatives of specific business units and turn them into kind of a committee, if you will? Yeah. So I think the organizations will have to figure out if these are FTEs or if you're gonna beg, borrow, and steal. But I think it's dedicating, it's understanding that you need that capability and then staffing for that. It's a funny thing. When we rolled out PCs in the 1990s, we understood we were gonna have to have help desks. We understood that we were gonna have to spin up IT departments. And yet somehow with AI, we feel like it's just gonna magically happen on its own. This distribution of knowledge and is not gonna have to create any new people. So in my ideal world, yes, this is a fully staffed COE center of excellence. That has people who are dedicated to integrating AI knowledge into the organization. They're measuring it, they're sharing best practices, they're doing all the goodness that's quite missing from many organizations today. And from my experience, some of the things that companies or enterprises andor companies in general don't necessarily spend enough on would be training for one, but also when they're As you mentioned, they're looking at AI and saying, like, this it's gonna s solve all these problems, but they're really not putting together a strong enough plan to implement these systems, ensure folks are trained on them, but leveraging them them in the best way. And so when you when you when you do that and you're bringing in these new tools and systems, you a lot of times have a staff that's not really getting the value out of them that they could. And yeah, I think that's definitely something that I've seen based upon my just general working experience within enterprises and supporting a number of enterprises. I hope this is a lesson learned, right? That it I don't think I'm on the same page with you. I don't think AI is just going to train itself. And I don't think it's enough just to initiate a SME within a specific organization to say, spend an hour each week talking about what this tool can do. I think having it within a enterprise at least. Having a committee, you could have those individuals that are part of the conversation. I would still do that, but I would definitely have kind of an overlying body that is responsible andor aggregating a lot of that, those lessons learned, best practices, ensuring that the organization is trained and is leveraging those tools effectively. Yeah. In fact, these when you invest in this infrastructure, so you have named AI leads that are supported, when you have a named AI activation hub. When you have an AI impact hub, which we'll talk about, I'm sure later. But when you have this, you your flywheel not goes bidirectionally. So we talked about the one direction, which is somebody atomizing the learning, getting it into the hands of the leads and putting that into the hands of the practitioners. But it goes the other way too. Somebody has an in a frontline insight. Hey, I created this great automation. It saved me hundreds of hours. They send that up to the AI lead. It gets codified by this AI activation hub and shared with the other activation hubs. And so in that way, now you have these success patterns that are populating throughout the organization. And now we're starting to talk about Peter Senge and learning organizations and integrated learning loops. And that is the ultimate goal that we'll need to, or ultimate capability that we'll need to build in an a much more AI native organization. So just and I'm thinking this problem through that enterprises and companies are ⁓ confronted with right now, which is we have new AI solutions that are coming in. We d maybe the enterprise maybe does or doesn't know how to leverage the tools. We've got to get them integrated into our environment. So there's a number of challenges, but let's just assume the tools are at the point in which those tools they've been selected and they're integrated, and you have people that are actually leveraging those tools. What guidance and I I guess, yeah, my thought is you have the tools selected and now what are you going to do with them ultimately? You pick the tools. Now what do you do with them? And so what would you say to that? Yeah. So in this is where the gradual migration and and getting deeper into the water, tiptoe on the sh shallow end first. Don't just dive into the deep end makes all the difference. So you've bought the tools, you're going to end up with A handful of power users. You're gonna have those curious people who who figure it out. They're gonna be on YouTube, they're gonna be watching all the YouTube videos. And so the question becomes, and yes, you need a baseline literacy, lots of baseline AI tutorials out there. You're gonna need to figure out your guardrails. So what can people do or not do? And so that that implies governance. I like to say it's got to be dynamic governance rather than static governance. Can you put your governance documents into a GPT and have people query, hey, is it allowed for me to do this? What is what am I allowed to do? What am I not allowed to do? And then what you want to start to do is you want to start taking a look at your processes. And I'm a big advocate for saying everybody in the organization needs to be a process expert. Because we're going to have to reinvent these processes over and over again over the next six months, two years, six years. And so the goal is to get people very familiar with the end-to-end processes. In the beginning, start injecting AI into those processes. This is where the focus framework comes in. Figure out what those high-value use cases are. Start to Then start to share those best practices, share those success patterns, and create what I call learning arenas. So I like to cite Price Waterhouse Coopers, and they have something called prompting parties. Now, doesn't that sound fun? Sounds like it's a Friday afternoon, there's pizza involved, and we're sharing what's going on with each other. And when I say a learning arena, that's what I'm talking about. And we're getting together with people who understand our area of the business, our work, so that we can exchange notes. Because I also like to say that AI learning is social learning. And so if we create these arenas for that social learning to happen, all of a sudden it goes from overwhelming, I don't know where to start, or even random acts of AI to something that feels much more structured. Because what would you say to a leader that's hesitant to integrate new tools because of the innovation that's happening and they feel like those tools might be at a completely different level in months from now? If why make the investment now if the tools are just gonna advance, we're not really sure where they're heading. What would you say to a leader that, you know, has that concern in mind? Yeah, it doesn't make any sense to me. It you're just gonna get farther and farther behind. is really the way I see it. And I I get the logic, which is, hey, why would I teach them how to use bad AI when good AI is just around the corner? But the reality is that if you're learning to play the piano, the learning comes through the reps. You know, that you're pr like there's something as piano practice. And I'm not a piano player, but I've known enough people who are. You don't just go to the lesson and then don't play in between lessons. Like you gotta get your reps in. And so if you're a leader and you are keeping your teams from having the reps, it's really hard to start making that deficit, no matter how big or how great the tools are. To to that point, I agree. I think it's these are all building blocks, whether they're the tools are at version one right now and you're hesitant about bringing it in and they're gonna be much more advanced in the future, just even through my experience of leveraging these tools is You are these are building blocks. You're learning new things, new processes and how they work together. And I really don't necessarily foresee, at least within the next couple of years, this full agentic tool that's going to be plug and play. You push a button, it goes and does everything you want it to do, without having to definitely give it some level of guidance and instruction. And so if you have those building blocks in place, it's gonna make that process that much more easier in the future. I think it's S similar to you, it's very much worth that investment right now to get your organization and company and the people that are working in it familiar with these tools. Yeah. And what we know from past automations, when I started writing the book, I really dove into factory automation. I dove into something called DevOps, which is the automation of the software delivery pipeline. And what we've seen is that the jobs go from doing the task to building, monitoring, and maintaining the automations that do the task. And so I I sometimes reference the washing machine. So I don't know that anybody really regrets not having to wash clothes by hand. Like I I certainly don't. And when you think about the job shift, it wasn't like jobs around washing clothes went away, but they sure did shift. All of a sudden we're building the machines, we're having to maintain the machines. And that's not to say that everybody has the same aptitude or attitude needed to make that shift. Who's best qualified? To build, monitor, and maintain those automations is the people who've been doing the work for 20 years. Right. If you've got an automation around processing invoice, it's that person who's been doing it day in and day out that knows when it's going off the rails. And like when we were talking about job cuts and layoffs, that there's a lot of noise around the reason why behind it. But the ultimate result is there are a lot of enterprises and companies that I would say are probably cutting. A little bit too heavy, it's it's arguable either way. But ultimately, I when you make those cuts heavy, then you are definitely losing a lot of those experienced people that have that domain knowledge that you will need in order to train those tools and ensure that they're functioning better. So my my advice again to those enterprises that are making those cuts andor question, and maybe you could actually lend a voice to this based upon your experience. Do you think a lot of companies are being a little bit too aggressive with? Those cut from your experience or even with the companies you're working with, are they being a little bit too aggressive with those cuts too soon? Yeah, there's a lot there. So the World Economic Forum ⁓ anticipates, I think it's something like 72 million jobs will be lost because of AI. And they anticipate 98 million jobs will be born out of AI. So that's a lot of displacement, right? Like the net is new. And so the question really becomes who Who bears the responsibility for that upskilling and for the redeployment of people? Do you just lay everybody off and then expect them to redeploy themselves? ⁓ One most organizations, one of their biggest expenses is around training and hiring people. Right. So my question to those who are thinking about laying off people is hey, you're probably gonna have to hire back a bunch more people. And the hire the people you hire back may or may not have the institutional knowledge of the people that you just laid off. And so they really have a choice to make, which is all right, we know that we're gonna get gains from AI. Like you see it in your life, I see it in my life. The question becomes do you harvest those gains and just take the headcount away? Do you harvest some of those gains and invest in RD or innovation? Or do you reimagine your business into something that is it doesn't even exist today? And you grow the pie. And I would just challenge the leaders that are listening today, what is the true value of your business to your customer? What are you offering that nobody else can offer? And how do you grow that pie? Because AI is going to make it possible. Yeah, and but I the hiring component interests me. Heavily in the audience and back the work that I did heavily related to recruiting and hiring, and especially within IT services and technology. And from my experience, even through the downturns and the economy that we experienced before back in the 2008 timeframes and all these different bubbles we've experienced, what I see a lot of is I see companies do make cuts, make decisions, and they always just think. ⁓ that's fine. We'll cut now. We'll cut a little cost and then we'll go back and we'll just secure that qualified talent later on. And I was on the side that was securing that qualified talent. And that's great in theory, but like I can tell you definitely within the technology sector, the unemployment generally is much lower than the general, like the typical tip the average of other roles and curve. Basically, yeah. There's a much lower unemployment rate within technology roles than ⁓ as opposed to, let's say, more functional types of rules. And so if your game plan is to go and secure this new qualified, experienced AI talent that for one, like you mentioned, has your enterprise institutional domain knowledge, and number two, can act can deliver on these tools in the way that you're looking for. My question to those leaders, again, and and kind of a thought provoking ideas. What do you think the cost of finding those resources are gonna be? Though those are those purple squirrel types of resources that we talk about. Is it gonna take you months, years to find those people? And if that short if that that volume of talent is so small, how will you actually be able to fulfill the needs that your organization has if A, they don't exist and B, you can't afford them? And what kind of reputation has your organization gotten? Like Jack Dorsey and Block with his layoff of four thousand people. So if you're a purple squirrel now and you really want to be part of a leading organization, are you gonna go to somebody who has this reputation of laying off half their workforce? Or do you wanna go to someone who is growing the pie and has supported their workers in in transitioning and continues to support the humans in your organization? And to that, I think that's a valuable topic as well is The way companies are positioning themselves and what messages they're sending during this phase is going to set up how they're perceived in the future. And so the decisions that you make right now will definitely play out. It may not, in fact, it may have a net positive effect on that executive's bonus today, but ultimately that organization, it that reputation damage is something that is going to cost those stockholders for the years to come. Okay. So When a team starts finishing work faster, they end up with extra hours they didn't have before. What do you think is the biggest mistake that leaders make the moment that gap opens up? And I think we talked about that a little bit before, but maybe you can shed a little bit more light on that. Yeah. I think, and I talk about this in the book a little bit, but I think the first thing leaders need to understand is that there's a J curve, is that the J curve actually has two things. The first is it's a learning curve. We talked about the need to get in the reps. So to really understand this technology and understand how it can can amplify your work, just because it can create a blog post in five minutes, the bulk of the work shifts from creating the blog post to really evaluating the output. And we've already touched on that. But I think the other thing is when I look at the quality of work and my own work included, I realize how many shortcuts I have had to take. Either in the research side, in how my PowerPoints look, in the graphics I create, in the analysis that I've done, like how much I've been shortchanging my own work, the decision-making process, because I haven't had the time. And so all of a sudden, when we get these tools and we can do the research that truly should have been done. From the start, when we can do the analysis that we would have done if we would have had the talent on board, if we would have had the time. I think that we're filling in a lot of quality gaps right now, which is why we're not seeing these huge, huge leaps forward in output. And so I would just encourage leaders to be paid one, be patient, and then two, really think about what you want to get out of it. AI, is it truly more productivity? Or is it better quality? Is it faster time to market? Is it improved customer experiences? What is it that you're going for with AI and not just look at the productivity gains? Because that feels it one, it feels a little short-sighted. And I I ⁓ anybody who's heard me on a podcast has probably heard this, but I like to say that Moderna get got it right. And they like to sorry, that Moderna got it right and that their AI North Star is to release 15 new drugs in five years with the help of AI. It's a really strong AI AI North Star is what I call it. And it aligns everybody's work to a common goal. And if you have extra hours, hey, let's see if we can release those 15 new drugs in four years. And I think it's important that we all recognize that we're in that adaptation phase. Everybody's still learning how these tools work, what their capabilities are. And I feel like there were there's a level up that will come once everybody feels more comfortable and competent with the tools that they're leveraging right now. So we're just in that gray area. But yeah, as we talked about earlier in the show, the fact that I'm my brain is hurting oftentimes, it's that I'm in that adaptation phase. But then I noticed that maybe a week down the road, I I'm solving for a different type of problem, but I'm leveraging this knowledge that I learned by utilizing these tools that that I was working with weeks ago. And in that moment, my brain hurts less. So I think we're we're heading towards that, right? Where it's that when once we get past that uncomfortable point of figuring it out, then we're gonna see some pretty significant productivity gains. It may not be here in the short term right now. A lot of that still has to be worked out. So going back to your book, The Hyper Adaptive lays out a five-stage journey for rewiring an entire organization to work with AI. Most leaders reading it are somewhere in the middle, not at zero, but not also at the finish line. What is the one thing that they need to stop doing right now to move on to that next stage? Well, I think we've been dancing around it pretty much the whole show, which is treating AI like it'll install itself. And And really funding the people, the processes, understanding that's a journey. I feel like I I have this saying, shopping is the easy part. You know, like it's easy to go out and plunk down twenty dollars, or if you're an enterprise, even let's go through our procurement process. We don't like that's a known. We know how to go through a pro procurement c process. We know how to install software in our organization. But AI is more than a technical installation, right? We've talked about how you have to revamp your processes, how communication flows look different, how AI changes every other week, and how that learning needs to go throughout the organization. And I think organizations and leaders need to understand that you need to install new pipes in your organization, new support infrastructure through which AI learning can flow. To other parts of the organization, through which the capabilities can flow, through which you can build integrated learning loops and these new habits and practices. And that's a whole nother thing that needs investment and time and intention. And this may be what you just really highlighted, but I'm curious, I'll throw this question out at you and see if we can dig up something more interesting on the topic. But how would you compare? How would you compare a digital transformation to an AI transformation? So digital transformation, and a lot of that was anchored in agile software development. And that was the ability for these software teams to move much more quickly. So get the software out the door more quickly. It was about improving the quality of software, and it was about aligning with customer needs. And when you think about digital transformation through that lens and you think about everything we had to do and put in place in order to change those ways of working, you know, everything from agile coaches to scrum masters to product owners. And even though a lot of that has fallen out of favor right now and it's not the hot topic de jour, those patterns are the underpinnings of those patterns are how do you move an organization from its Current way of working into a future way of working. And that's why when people read the book Hyperadaptive, I've gotten a lot of great feedback that says, Melissa, the patterns are solid. We need to evolve them for the age of AI. And that's what I've done in the book, is I've said, okay, what did we learn? What went wrong with digital transformation that we need to evolve? And some of that is about being more iterative and incremental with the rollout. And getting the communications aligned and getting the incentives aligned, all of which I cover in the book. But I feel like the speed is pretty different. And that that makes that's a big part of it. I think we need to really reinvent the org structure and the organizational model. And I don't know that digital transformation put enough pressure on the organizations to say we need to reinvent the whole thing. And so I think the speed and the organization. organizational model are the two big things. So at a high level you're saying a very significant transformation to the organization in comparison to what a typical digital transformation process was like. We're talking with this whole AI times 10 or some something to that degree. Yep. Yeah. Yeah. I think it'll require an entirely different operating model. And that's some of what I outline in stage five, that hyper adaptivity in the book. Sounds like you're in a good business then too, Melissa, right? This is good. We'll see you. Yeah. Okay. So I appreciate all your insight here today, Melissa. We like to always wrap it up with one question in the f looking at the future. So we talk a lot on this show about being an architect and not just a worker, someone who builds the future on the purpose instead of just reacting to it. So looking at where things are headed, what do you think the future of work is going to look like in the next three, five, or even ten years down the road? Mm-hmm. Again, I feel like the operating model will will radically change. In the book, just to to tease it a little bit, I say the operating model of the future has three levels. One is what I call innovation circles. This is your internal organization's RD. This is where new ideas are coming in, where they're being sensed and vetted. Then you have your value streams, cross-functional, cross-functional groups of people who are delivering value to the customer. And then you have your stable layer. And that is the people who are keeping the lights on. If you think about that, all three of those require different types of funding. And I feel like the other big shift is that we go from a career ladder to a career portfolio, a portfolio of experiences where maybe we spend some time in the innovation circles and then we bounce back into our value stream. And we're not necessarily Trying to c climb up a functional hierarchy. And what the time frame for that is, I don't know. But that's the way I envision the future. Very unique. I appreciate that too. I think that's I'm excited to see what the future looks like. So where we talked about your book, but where can also people where can people go to follow your work and stay connected with you and what you're building? Well, thanks for the opportunity. So I'm out on Substack. So Melissa Reeve, find me there. Intel.hyperadaptive.solutions is the Substack. Find me on LinkedIn. I'm always up for a chat. And yeah, and then hyperadaptive solutions, you can find the book and more information there as well. Great. Everything we talked about today is also going to be in the show notes and the links, the frameworks, all of that information. And Melissa, thanks for helping us stay unlocked and don't be a stranger. Thanks so much for having me. It's been a pleasure.