S3: E7 Becoming Hyperadaptive with Melissa Reeve: Rewiring the Enterprise to Become AI-Native

[00:00:02] Hello and welcome to the Hope @Work. Everyone with me today is Melissa Reeve. And Melissa, I think we met, I think it was through LinkedIn. But then I got a chance to meet you in person in Vegas recently. Was that at Unleash or Transform? Was that Unleash? Unleash, that's right. Because I went to both of them. So those two weeks in Vegas start to roll together as you can imagine. So it was great to put a name with the face in person. So glad you're joining us today. I understand you have a background.

[00:00:31] Unleash, you're a background in organizational transformation. I know you're doing a lot of consulting work around things like AI integration. I know you've got a book coming up that of course we'll talk about, but let's start with just getting a sense for your background and what you're focused on today, if you don't mind. Melissa Reeve Sure. I'd like to say that my journey to today started on a factory floor in Tokyo, where I was studying the Toyota production system at Hino Motor Company.

[00:00:59] And that's where I got to see it up close, in person, people pulling on the end on cord. And that's where I really realized that somebody who is working on the front line can really make an impact on the entire system, if given the chance. Melissa Reeve And I didn't necessarily have all of the words to describe my thinking at that time. But now looking back, I realize I've always been a systems thinker.

[00:01:28] I have always loved to see how the pieces and parts of an organization combine. Melissa Reeve And so that was the through line through a career in executive leadership, mostly in mid to small companies, although oftentimes I was servicing the world's largest enterprises. And that culminated when I worked for a company called Scaled Agile.

[00:01:54] They were providers of what was called the Scaled Agile Framework, which is a way of scaling agility and technical teams to the world's largest enterprises. So I want you to think, your listeners to think about, even a company like Nike has 10,000 software developers. And so how do you coordinate that activity? Melissa Reeve But it gave me an exposure to leading thinkers in organizational design and experts like Peter Senge and John Cotter.

[00:02:21] And I've just integrated that into my thinking. And when AI hit, I realized that this was going to be another challenge for organizations and how they organize their systems. Yeah, no doubt. I like that you mentioned John Cotter, some old school change management folks. And that's a great place for us to start, I think, because everybody thinks that this AI transformation is so unique.

[00:02:51] And yet we as a species of human species have gone through many other ginormous transformations. And they take a similar shape and format and sequence and structure. And so we're repeating history to some degree, although there's elements that are unique. But I like that you acknowledge that. And so I know you talk about making the mistake of jumping ahead too quickly and that you've got to bring people along incrementally.

[00:03:19] It feels like most companies seem to believe that AI is more of a technology implementation challenge. But I think you tend to argue it's more of an organizational evolution problem. Maybe talk about that a little bit and what leaders are fundamentally misunderstanding about this big shift that we're taking right now. Yeah. In the beginning, it's funny, I'm talking about in the beginning, like two years ago, right?

[00:03:48] So when ChatGPT first launched in November of 2022, I ignored it for about six months and then I started leaning in. And it became evident to me pretty quickly that there was a technology side of AI, right? It's the LLMs. It's the models. It's the capabilities. But then there was also what I saw as the human side. And that is the people, the processes and the roles.

[00:04:17] And this is where my background in technology transformation starts to shine through. Because when I watched these large organizations try to implement things like the Scaled Agile Framework, what was hindering their progress wasn't the technology. It was things like alignment. It was getting a shared language. It was understanding where we're all going together.

[00:04:46] And so I was able to see through some of that early hype and early noise to really understand that what was going to happen is the organizations themselves needed to rewire. And, you know, there were a few other things driving this because I had taken a little time out in my career to study management theory.

[00:05:11] And so I had really understood this arc of where management theory started back with Taylorism. So there's John Winslow Taylor back in 1911. He was the first person to really treat management as a science and wrote the book, right, The Theory of Scientific Management.

[00:05:35] And then see it go through this arc through World War II, where we started to have globalization, where we started to have these functional silos in order to improve operations. And so I realized that the operating system that got us here, Taylorism, functional silos, was not going to get us there. And AI compressed both of those dimensions.

[00:06:03] And so you talked about leaders wanting to jump to that finish line. And I've heard it over and over, these leaders who are like, you know, let's reinvent and let's, you know, let's put in the orchestrated agents. But if your operating model has not evolved, you're really going to struggle. And I like to say that you can't expect 21st century results with a 20th century operating model. And I think that's underneath your question.

[00:06:32] And what we know from other transformations, like business process reengineering or digital transformation, is that generally speaking, big bang approaches don't work. They're too disruptive. They require too much change.

[00:06:51] And quite frankly, if you are dealing with a larger organization, 5,000 people, 10,000 people, 100,000 people, 400,000 people, it's, you're turning an aircraft carrier around. And that just does not turn in the same way as a sales sailboat. And so that's why I took this more incremental and iterative approach. Okay.

[00:07:14] So when you talk about rewiring an organization, you talk about, I like the analogies that you use. We can't use that same old operating model. The whole Marshall Goldsmith won't got you there. What got you here won't get you there, right? That's right. You talk about the functional silos that were purposefully created, it sounds like, back in the days of Taylor and served us well for many years. But those are breaking down as well.

[00:07:41] What exactly does it mean to rewire an organization? What are some of the essential ways that you're starting to see organizations do just that? Yeah. In all, you know, I'd like to hone in on kind of the people, the processes, and the roles as three critical pillars that will rewire. But underneath the hyper-adaptive model, there's actually nine different dimensions. Okay. And they include things like rewiring your budgeting processes.

[00:08:11] Rewiring how you make decisions. Rewiring your organizational system. Rewiring the role of leadership. Like there's very little in how our existing structures operate that won't be touched. And I know that you're going to ask the question, or at least I anticipate the question of being AI native.

[00:08:33] But I want to jump ahead in that moment for just a second because I want you to think, and I want your audience to think about that company that is just getting started. And there was an article this past week in the New York Times that talked about the first solo billion dollar company. I don't know. I've seen that. Yeah. All of the big guys have like bets on when that will happen, right? Yeah. So they profiled this guy who supposedly was the first.

[00:09:02] You know, New York Times put some caveats on that article after they published it. But the gist of it is, is if you are an organization that's just starting out, you don't have the same way of operating. You are firing up agents that start to talk to each other. Your role is to monitor the agents.

[00:09:23] And so the question becomes, if you're a linear, if you're a traditionally wired organization, how do you get from here to there? Right. How do you start to address the people, the processes and the roles? And that's where we take you on in the hyper adaptive model, this five stage journey to gradually rewire that over time. Okay.

[00:09:49] So let's come back to the AI native question, because I'm hearing more organizations. In fact, when I was in Vegas with you and I was talking about the company that I work for now, Lytics, somebody asked me, are you AI native? And that was the first time anybody's asked me that. So how do you define that? Yeah, I've got a very specific definition, but the vision I have is of this one person billion dollar company.

[00:10:16] But the definition is one that can sense and respond in near real time and has the systems to do that. So if you think about the underpinnings of what AI will enable us to do, yes, automation.

[00:10:33] But it will also allow us to sense at a scale and by sense, like keep track of what's going on out in the universe at a scale we're not able to do today. And then it will allow us to respond to what it's sensing. Well, there's the shift in consumer sentiment. How are we going to respond to it? We have the systems in place to start pivoting, to start retooling.

[00:11:00] We've embedded integrated learning loops into every step along the way. We've reconstructed our systems in a way where we can take advantage of these opportunities in near real time. And to me, that's the hallmark of being AI native. It's not necessarily everything's automated. It's this ability to have the systems in place to do that sensing and responding and learning in near real time. Okay, interesting. I like that.

[00:11:29] That's helpful to hear your point of view and your definition of it. I think what I'm hearing a lot in the market, and we're seeing it in Wall Street, is traditional SaaS cloud companies taking a beating on their stock price because the perception is you're not AI native. You're trying to bolt AI onto some older kind of platforms and frameworks that may or may not benefit from that kind of architecture versus being AI driven and structured from the start.

[00:11:57] But I want to come back to this idea of rewiring organizations. And again, you brought up functional silos. So I want to ask you a very pointed question because I'm very intrigued by how you're going to answer this. We created these functional silos because they served a purpose. Some of them are finance related. I grew up in the HR silo, right? So there's supply chain. There's IT.

[00:12:27] There's lots of different traditional silos. Now you've got AI. And I think there was like originally this assumption that AI fit in IT. And then people were like, but wait a minute. They're creating these agents that are actually doing the work. And so then you've got the modernas of the world saying we should combine HR and IT. And they're all just trying to do the same thing. Get the work done. I don't care if it's a bot or an agent or a human.

[00:12:56] And then there's others that are like, no, no, no, no. This isn't another silo to add to the mix. It's like the electricity running throughout the organization. How do you help us explain like where does the new world of AI fit? And I guess, do we have to get rid of all of those functional silos? Do we create new ones? What do we do? Well, I want to paint the picture for your listeners first about to answer your first question, which is how do we rewire?

[00:13:26] Yeah. And then we'll talk about where does AI fit? Because I think your answer is it fits everywhere. And that is the correct answer. And so what we start to do in the hyper-adaptive model in the first stage, the first stage of rewiring is starting to inject AI into existing processes.

[00:13:47] And so the goal here is to have people become intimately familiar with how their workflow is running because we know that that's going to reinvent itself for the foreseeable future as AI capabilities continue to increase. So we have people look at their workflows. We teach them how to inject AI into the workflow. We also fire up what I call AI activation hubs.

[00:14:17] And I want you to think of these AI activation hubs as, first of all, they're fractal. So they can grow and shrink depending on the size of the business unit or the size of the actual business. But think of them as a network of accelerators. And inside those accelerators, you've got somebody who's maybe exceptionally good at AI. You've got people who are capturing the best practices. You've got people who are atomizing the learning.

[00:14:46] Oh, Claude 4.6 just came out. Here's what it means to our little group, our little section. And they're keeping track of the metrics. What impact is this having on the business? And I want you to think of this network of AI activation hubs interacting with AI leads that interact with the AI practitioners. So in your Moderna example, you said, oh, let's mesh IT and HR together.

[00:15:14] So if they had an activation hub, and they probably have a couple of them for HR because HR isn't a monolith. All of a sudden, we start to pair these AI experts with the subject matter experts. And what you start to do is you start to see that AI skill embed itself within all these different parts of the organization. Okay, so let's cut of stages one and two. By stage three, what we start to see happen is we start to see automations take hold.

[00:15:43] And when I say automations, I mean we're not just injecting AI into parts of a workflow. AI is doing entire sections of the workflow. We know from previous automation events that when this happens, whether it's factory automation or something called DevOps, which is the automation of the software delivery pipeline,

[00:16:04] that the jobs shift from doing the work to building, maintaining and monitoring the agents that do the work. Right. Now that's where roles significantly start to rewire. And this is the disruption that the World Economic Forum is talking about. Yes. When they say that 78 million jobs will go away, but 92 million jobs will be recreated. That's an incredible displacement.

[00:16:35] And so that's what I call stage three, which is like, let's do that on a small scale. Let's start automating on a small scale, really understanding what are the skills people we need to have? How do we upskill people into these new roles of building, monitoring, maintaining? Now, the good news for your listeners is that according to the World Economic Forum,

[00:16:58] if you took 100 employees, we would be able to migrate around 89 of those employees into these new roles. So they're very bullish and very optimistic. You might have 11 employees out of 100 that maybe don't have the right aptitudes or the right attitude to make that cut. So we spin up what I call the AI impact hubs, again, a network of hubs across the organization

[00:17:25] that is looking at the actual people saying, who can we move where with what? Before we start to scale this effort. Mm-hmm. And you can see now we start to rewire because the other thing we're doing as these automations take hold is what I suggest we do is we start organizing around value streams.

[00:17:48] And so now we're taking everybody who is needed to deliver something from concept to value, we're taking those people from the functional areas into these value streams. And why that's so critical is because AI unlocks what we call adjacent competencies, right? So you maybe never were big on creating video, but now you can be with AI. Yeah. Right?

[00:18:16] You know, maybe you weren't big on writing, but now you can be with AI. Maybe you've never coded in the, but now you can with AI. And so we're unlocking these adjacent competencies and it becomes less about your functional area and more about the value that we're delivering to the customer. I know that I've just laid a ton of stuff out there, but I want to take a breath. But, you know, does that start to answer your question of like, how do we start to rewire? It does. I love it.

[00:18:45] There's so much to unpack there. I think we need six more hours to finish this podcast. First of all, this term adjacent competencies, I'm 100% stealing that because I love it. It's a great term. And I think it's really an accurate reflection of the opportunity that will happen as a result of our access to these new tools. But I want to come back to something that is center stage right now in the media, which is fear, right?

[00:19:11] And some of the statistics that you provided, I think from the, was it the World Economic Forum or the one that said, okay, 78 million jobs will go away. 92 million jobs will get created. And then you see other people that are like much more pessimistic about, you know, what's going to happen. And I'm just curious, like what your point of view is on these prognosticators that some

[00:19:40] of which are maybe Pollyanna. Some others are just, you know, depressingly cynical. And what's the reality? I imagine it's somewhere in the middle in terms of how will this actually impact human beings and their ability to work if they want to, because you use the term aptitude and attitude. And I like that too. Yeah.

[00:20:05] So I think if you look at the number of jobs going away and the number of new jobs created, both are true, right? So when you say jobs are going away, yeah, a heck of a lot of jobs are going to go away. You know, as I was driving home, I had a doctor's appointment earlier and I heard a radio commercial for somebody saying like, hey, have you ever been a radiologist? You might consider, you know, doing X, Y, Z.

[00:20:29] And I thought, those are the commercials that are going to be playing five years from now. Hey, do you have a background in financial analysis? Have I got a job for you? You're going to monitor our automations that do the financial analysis. And you're going to make sure that the intent is there, that the model isn't drifting. And I feel like those are the roles that are going to be created.

[00:20:57] I think the other thing I can point to is a company out of China called Ping An Insurance. Okay. And in 2008, Ping An Insurance made a quiet little decision to get their data house in order and make that the center of everything. Then they focused on their customer. And they said, today, that customer wants us to underwrite their insurance.

[00:21:24] What could we add into that equation tomorrow that would be synergistic? Fast forward 17 years. Now that same company has an ecosystem that includes finance, insurance, and health care. And because they got their data house in order, they're able to do things like, oh, you just lost your job. Here's some mental health support. Oh, you just bought a used car.

[00:21:54] Here's a lot of mechanics that are in our ecosystem that we highly recommend. Oh, you're conservative in your financial investing. What does that do to your insurance ecosystem? And you can see there that even if you are one of these software companies that is getting beat up right now because you're not AI native,

[00:22:19] if you start to imagine what your value is that you're delivering to the customer, you might be able to expand your pie rather than just contract it and harvest those gains and just put them straight to the bottom line. If you've ever listened to a business podcast and thought, this sounds good, but no one actually talks like this behind closed doors, you're not wrong. On the Leaderbook AI podcast, we talk about the conversations leaders actually have.

[00:22:48] After the board meeting, after the AI rollout that doesn't quite land. I'm Felicia Shakuba, and I interview CEOs, investors, and operators about what really drives performance. If you want that level of honesty, subscribe to the Leaderbook AI podcast. Yeah, I like that. I'll use your term back at you. So we talked about adjacent competencies. You're talking about adjacent opportunities for those institutions. So that's great.

[00:23:19] So I want to go down that same path when we talk about exploring these opportunities. So I'll tell you a quick story. Some folks have heard me tell it, but on the way to Vegas, I took a Tesla to the airport, right? And it was a self-driving Tesla, and it was brand new, 2026. And the guy, it was a 45-minute drive, Melissa, and the guy didn't touch the steering wheel the entire time. I couldn't believe it. It was so cool.

[00:23:43] And he was telling me soon, when all of the regulations come out and he's allowed to do it, he's going to put his car into the taxi kind of queue, and it will be self-driving all the time without him in it. And he'll be at home, and he's like, then I'm going to buy three more, and I'm going to have four of these driving around. And it comes back to your third stage, which is build, maintain, and monitor. That's what he'll be doing. He won't be driving.

[00:24:12] And so, again, what I like about what you described about this insurance company out of China is they were like, okay, instead of focusing on – it's like the Henry Ford model. If I gave them what they wanted, it would have been faster horses. Stop trying to make the driver's experience better and actually recreate the whole experience and say it's now self-driving. And that's what the insurance company did is let's stop focusing on insurance. Let's focus on some other ways we can add value. Yeah, sure. So does that make sense to you? Is that kind of similar kind of –

[00:24:41] That's exactly the mindset. And it's funny. So Dani from Red Thread Research, she and I are having a little conversation online right now. And I said, you know, if it was about innovation, we would all be driving flying cars right now. But it's about the systems. And, you know, what do you do with all the roads? What do you do with all the gas stations?

[00:25:05] And in order to really leverage AI, we need to think about the systems in which we operate. And the systems in which we operate are optimized for functional silos. And so the question becomes, what new systems do we have to put in place? What new infrastructure do we have to put in place to facilitate that AI-native sensing and learning?

[00:25:34] And I like to posit that it is more of an information infrastructure. And by information infrastructure, I don't mean more pipelines like email and network. Humans talking to each other in new ways to make this happen. Is this what you call kind of that messy middle where we have these old traditional structures colliding with these contemporary capabilities and new norms?

[00:26:01] How do we navigate that without like losing momentum that we're trying to create? Yeah. So this is a perfect segue. So we stopped the hyper-adaptive model in stage three, where this is the messy middle, where the old system is starting to morph into the new system. And we start to take baby steps. We start to experiment with one, now two, now 10, now 20 value streams in the organization.

[00:26:30] And we start to study what the impact of that is in the organization. Is the flow improving? Is our outcomes improving? What effort is required to make that transition? Then in stage four, we start to scale not only our agentic systems, but our orientation around value. So we start to get more and more value streams, and maybe those value streams start to interact with each other.

[00:26:57] And with that, we start to spin up telemetry networks because we have more sensing and responding and measuring to do as we integrate our value streams. By the time we're in stage five, we have a pretty different organization. And I know your question is about the messy middle, but I'm going to go to stage five and then come back to the middle.

[00:27:24] So in stage five, I want you to think about the organization in the future as having three levels. The top level, think about it as your organization's venture capital arm. These have your innovation circles. Anybody can pitch an idea, say, hey, this is what I'm seeing. Here's my business case to back it up. Will you guys fund this? The middle level are these orchestrated value streams. These are your long-lived value. I like to use the example of a bank.

[00:27:53] A bank services a lot of different types of customers. One might be high net worth individuals. One might be new grads. One might be married with kids and family. And you bring everybody who's related to that value stream into that to deliver the value. Your bottom layer is your stable layer. This is your buildings, your infrastructure, keeping the lights on. And these are your three layers.

[00:28:20] And they start to have different talent models, different funding models in this new type of an organization, this new operating model for these AI native organizations. Now, we're not going to get there overnight, right? Because, you know, the other piece of this is there is the skeleton of the functional hierarchy that remains. But those folks do overarching strategy.

[00:28:47] And you might have just, like, almost high-level snipers that come into these value streams. And they're experts at their function. And they can inject a little bit of that into a specific value stream. But it's a very different type of an organization. And so my guidance to enterprises that are going down this journey is do it slow. You know, you have the deep bench.

[00:29:16] You know, Google was, quote, unquote, late to the AI game. But they were quietly doing stuff in the background. And they had a lot of capital that they could reinvest into this AI game. And they still do. They're still a big player. And so if you are one of these enterprises trying to figure this out, take a breath. Don't buy into all the hype. Be deliberate.

[00:29:40] And deliberately start to rewire yourself and account for that messy middle where things get a little bumpy with the knowledge that they should smooth out in the future. I like that, too. And that's somewhat different than what I've been hearing from a lot of organizations, of course, that are trying to sell this stuff. Is hurry up. Don't get left behind. Don't go slow, Melissa.

[00:30:08] Because you're going to, you know, you'll never catch up. And I think you're giving much better advice, which is, hey, acknowledge and recognize this messy middle. And make your way through it at an appropriate pace by bringing people along. I want to talk about the people piece for a minute more because, again, that's kind of my orientation. My people had stitched to my head. I can't take it off.

[00:30:30] When we talked about, again, 78 million jobs going away, 92 million jobs being created, different jobs than the ones that got left behind, that means the people that were doing that 78 million jobs have a choice. They can learn to do some of these new ones or some other ones that previously existed.

[00:30:53] So my question to you is, how do we make that transition as smooth as possible for the individuals and the organization? So this is where, again, I admittedly have a bit of a skin in the game because we do strategic workforce planning at Litics. We're like, hey, you guys need to start using data to determine what are you going to need in the future? What skills do you have today? What are the gaps? How many of those gaps will actually be taken up by agents and other technology?

[00:31:21] But my big thing is, from a people perspective, we've got to give them the ability to learn AI, to embrace it, to practice, to play with it. And yet we're not necessarily giving them the time and the bandwidth to do that. Like, should we at some point, Melissa, say, you know what, Fridays are just about learning now.

[00:31:48] You've got to build it into the daily flow of work or it just doesn't happen. So how do we accommodate this need for constant learning and growth for the employee population? That was a meandering question. I apologize. Well, there's a couple of layers there, and I'll try and tease them apart. One is, how do we keep the organization up to date as AI evolves is one layer that I heard.

[00:32:17] That was in the capacity management question. Yes. And how do we create this space? And I like to say that if you put the structures in place, you'll get a bidirectional AI learning flywheel. And if you give me a minute, I'll describe it for you. Take it. So imagine if you will, Claude 4.6 just jumps out. And we've actually kind of described this level of it.

[00:32:45] 4.6 comes out. You've got your HR activation hub. They ingest what that means for the people on the front lines in HR. They atomize the learning. So what does that imply? That implies you might have an L&D professional or 2 or 3 or 10 in this activation hub to atomize the learning. Short video, you know, little demo.

[00:33:08] And that goes in the hands of your AI leads, who then puts it in the hands of your AI practitioners. And so you've got a nice little flow that keeps itself up to date on the one direction of the flywheel. The other direction of the flywheel is you have an individual practitioner who says, oh, man, I just created the most awesome automation with AI. And they send that up to their AI lead, who gets it in the hands of the AI activation hub.

[00:33:36] And again, we have a network of these activation hubs. So now you have this horizontal knowledge sharing. Now, this is an interesting concept from Lean called Yoko 10, you know, horizontal knowledge sharing throughout the organization. Everybody starts to get updated with this new cool agent.

[00:33:55] And you start to see that it's more about knowledge sharing and keeping the organization up to date than it is necessarily about, you know, yes, it's about capacity. But it's also about creating these systems. So now your second part that I heard was how do we reskill and upskill people as these roles are changing? Yes.

[00:34:22] So when you I'm thinking about the stages of the model, too. So you were absolutely right. If you give them capacity, people capacity to learn how to inject AI into their role, that's stage two. Then those people get oriented to AI. And now when we move into stage three, which starts to automate entire workflows, we're not starting from zero. We've already leveled up individuals.

[00:34:50] They've got a certain level of literacy. They've been kept up to date through the AI learning flywheel. Now when we say, hey, do you want to go build, monitor, or maintain? Maybe we have a little aptitude test that says, hey, this person's really interested in the guardrails. Let's have that person monitor the AI. This person really seems to be leading into building. Hey, maybe they're the ones that should be on the front lines creating innovative new solutions with AI.

[00:35:18] This person really does a great job when the AI breaks. And they have that, you know, that fortitude to go chase down what's wrong with the system. And I feel like that's the teasing out that we'll do in terms of aptitudes. And here too, I just say the goal isn't necessarily to name all the new positions.

[00:35:41] The goal is to create the infrastructure where when a new position is appearing, we know how to create it and fill it on an ongoing basis. Yeah, yeah. So that ability for ongoing mobility, really important without a whole lot of friction in that process.

[00:36:01] And again, when you talk about these folks and identifying those adjacent competencies, what you're really getting at is the transferable skills that will allow them to be successful in a variety of areas of the organization, depending on how those things evolve over time. So that makes sense to me. What in the world did you study in school? Japanese. Literature and language. Okay. All right.

[00:36:29] And again, the time you spent in Tokyo with this organization sounds like it was really influential. This has been fascinating. I want to ask you one last question. Again, I could no doubt talk to you all day and I can't wait for the book to come out. But again, we've already talked about the narrative of fear and disruption and uncertainty.

[00:36:49] So when you look at the future through the lens of hope, since this is the Hope @Work podcast, where do you see genuine hope for people in this big transformation? I think there's a set of what I call durable skills and that those durable skills start to look like creativity, experimentation, growth mindset. Yep.

[00:37:15] You know, I'm sure there's a lot more critical thinking and curiosity. Yes. Right. And so, you know, and before we go, I'll just talk briefly about what I call the bifurcation problem, which is there's 10% of the people leaning in naturally to AI. There's like this other percentage, maybe it's 60%, maybe it's 70%. That's just kind of using it for email.

[00:37:43] And then there's this group at the bottom that's maybe actively resisting. And so the goal in the organization, you can't just rely on your 10% to be curious, to lean in, to teach themselves. You've got to create the support structures, the capacity to help spread that learning throughout the organization. You know, I like to say that AI learning is social learning. And so how are you unlocking that social learning in your organization?

[00:38:14] Okay. Super helpful. So the book, tell me again, it's the Hyper Adaptive book about rewiring the enterprise to become AI native. Is that right? You've got it. Yeah. Okay. And when does it come out? So Hyper Adaptive comes out May 12th and at a retailer near you, both in-store and on Amazon. And for those of you, if you listen to it before May 12th, there's some pre-order bonuses. After May 12th, then I'm sure we'll see each other online.

[00:38:43] Good. Is there anything that we didn't get to that you want to at least mention relative to, I know you mentioned there's these, you know, different stages and these different levels and that you think is relevant that you want to make sure that you at least mention before we land today? Well, I appreciate the question. I think one, but when we were talking before, you were talking about different shapes of organization. Should it be a pyramid shape? Should it be an hourglass shape? Meaning what happens to the middle?

[00:39:12] Is it a diamond shape? You know, does the, does entry level go away? And I, I thought about that more and I thought, you know, I think the shape of the organization in the future is that of a network. Okay. And the question becomes, how do you set yourself up to become and operate more like a network than any given shape?

[00:39:37] And that's a pretty big shift from a square shaped organization or a pyramid shaped organization to a network. Sure is. Yeah. Wow. I like that though. That is a different way of answering that question. I appreciate that. Well, Melissa, it has been an absolute pleasure having you on the Hope at Work podcast. I can't wait for the book to come out. We'll make sure that everybody understands how to grab a copy of it.

[00:40:03] And then I'd love to stay in touch as you go out and talk to other organizations. I know you were just outside of the country doing some consulting work with some large institutions. Let's stay connected and see how all of this evolves over the next couple of months and years. Sound good? That sounds great. I really appreciate you having me on the show. You bet.