Hey everyone, welcome to another episode of the Leadership Growth Podcast. I'm your host, Daniel Stewart, along with my brother, Peter Stewart. And we have a fantastic guest today. Melissa Reeve, welcome to the Leadership Growth Podcast. Thanks so much for having me. I'm just so... I'm jazzed for our conversation today. I'm excited to be here. And jazzed is such a good word because the topic... I mean, I guess a part of us all might be... you know, sick of talking about AI. However... However... it ain't going anywhere, and figuring out how to embrace it, how to understand it, how to manage it, especially from an organizational level. How to help our organizations become more AI-native, as we transition into what this might mean, and to wade through the noise. So, Melissa's going to be here to help us wade through this noise, understanding how to help us do this. So, let me, uh, let me share a little bit of a background of Melissa, and then we're going to jump in to understanding more AI as it relates to our organizations. Melissa Reeve is the creator of the Hyperadaptive Model

Rewiring the Enterprise to Become AI-Native, and yes, we'll no doubt dive into what hyperadaptive means. So keep that and we'll dive into this. Prior to leaning into AI, Melissa spent 25 years as an executive and agile thought leader, which led to pioneering work in agile marketing, and her role as the first VP of marketing at Scaled Agile, and co-founding the Agile Marketing Alliance. She lives in Boulder, Colorado, lovely Boulder, Colorado, with her husband, dogs, and chickens. I don't know if we'll get into the chickens, but that might be a fun question later, where she also enjoys hiking and gardening, no doubt, in those beautiful Flatirons. So, Melissa, welcome again to Leadership Growth Podcast.-Thanks so much. Pleasure to be here.-So, let's start off with this question that many of us are feeling because there is such potential with AI and it's so, so frequently around us. We might be experiencing a bit of FOMO, the fear of missing out, the fear of being behind, perpetually behind. Give us a sense of what are you seeing in the marketplace. What are ways that people are kind of trying to reconcile these expectations around what AI could be doing, what we're seeing now, and let's use that as kind of a jumping off point for our conversation here.-Yeah, I appreciate the question, Daniel, and I'd start by saying, I think just about everybody has FOMO, including the people on the front lines. I remember being at a conference earlier or later last year, and there was somebody who was literally a programmer at OpenAI, and she felt like she couldn't keep up with AI. So it's not just executives. It's not just your average individual. It's even the people who are writing the code. And I don't know if it's because AI can generate the code so fast. We've got all these features coming in. And quite honestly, our systems and humans we're not equipped to absorb these changes so quickly. And even Dario Amodei, the CEO of Anthropic at another conference, was saying that, um, if we stopped releasing features today, it will take enterprises 10 years to integrate what we already have. And that was 6 months ago. And I know he's released a ton of stuff since then. So I think, I think it's just our normal state and to a certain, certain amount don't worry about, or a certain extent don't worry about it. And then I think the other thing is just acknowledging that before we see the returns on our AI investments, we have to allocate time for what I call sense-making. So we have to wrap our heads around what the capabilities are, how we can use them. And I feel like so many organizations are trying to rush to that ROI state, without really recognizing or acknowledging that sense-making, that sense-making period.-It's... you're bringing up a lot of good points to help kind of level set uh, leaders, organizations, because this wave, this flow, I mean, it is here, it is moving, you know, it feels more like we're, we're on that river. We're in the ocean. The current's taking us. And we just need to make sure we're steering in the right direction. Uh, so we're we're going with that.-Absolutely. So appreciate that, that grounding. So let's dig into this, this topic of AI a little bit more. And we may kind of come back to how we can alleviate the FOMO, by some of the things we're doing. So we may bookend this with tying that back. So what, let's let's kind of get the foundation. When we talk about an AI-native organization, like, what does that really mean? It's a term that's thrown around all the time. Like...-Yeah.-What's your definition of it?-Well, I appreciate the question because it's true. It's like agentic. What is agentic AI? You know, there's these words out there that people are tossing around and everybody's got a different definition. So my definition is that it's an organization that's able to use AI to sense and respond and react in real time through the power of AI. And so then that begs the question, how do we get there? If we aren't, if we aren't AI-native from the get go. How do we how do we transform there? And I think the biggest difference for me is existing organizations, they're what I call linear organizations. So you have strategy to execution. I'm like, with my hands drawing an X and Y axis, right? So you have, you have strategy to execution and you have concept to delivery. And you have handoffs and delays through both of those axes. And what I believe AI will do is compress both of those axes. You know, we can deliver things faster. We can get decision support so that we won't have to go through so many layers of hierarchy. When you think about that linear organization, that's an organizational structure that is from the 20th century. And I like to say you can't expect 21st century results with a 20th century operating model. And so, an AI-native operating model is one that's born out of today's reality, that... those, the compression of both of those dimensions, rather than, we're gonna, we're a brand new company and we're just from the get go, gonna spin up a, a sales silo and a finance silo. And I feel like that is such a key differentiation.-And you're building on so many themes as we think of the more traditional, often militaristic hierarchical based structure of decision making rights at the top. They decide what needs to happen. It flows down. It's in this lovely pyramid shaped thing, and each level has certain responsibilities, and at the end of each month, or each quarter, or each milestone they produce X amount of something, and then it's sent out. It's this very nice linear understanding. It's easy to conceptualize. It's easy. However, over the past 100 years, that model has consistently been challenged. And with AI, it seems to be like one of the greatest challenges to this hierarchical structure. And it really reinforces this participative, this democratic, this more empowering kind of notion, even as we structure the organization. Because everybody has access to so much knowledge.-That's right. Yeah. With the internet, we started to democratize knowledge, right? Anybody could access knowledge on the internet. And I feel like AI starts to democratize skills. And it really changes who can do what in the organization. And so even this thought of separating the organizations into functions starts to blur. You know, we have an HR person who might be able to develop their own recruiting video. We have a traditional marketing person who can code analytics and apps for the website. And so in the book, I start to advocate for a reorganization around value streams, and the value that we're delivering in the organization. And that's, that's a pretty different... I know there's organizations out there who certainly are doing it and have been chipping away at that for a while. But to wholesale reorganize in that way, it's going to take us a hot minute. And so that, that's really the basis of the model is, is how do we get from these linear structures into a hyperadaptive organization, and that organizational model is orchestrated value streams powered by AI.-This key point that you're really illustrating well that I think is a powerful take home for listeners... It's recognizing AI-native. This terminology is not about the adoption of a new technology, in and of itself. It is about a mindset shift in how we do business, in how we interact with each other, in how we organize our organizations, businesses, agencies, whatever you might want to call it. And just right there, unpacking that is a powerful statement for leaders to sit and think about. We're not just integrating a new technology. We are completely altering the fundamentals of how we are interacting.-That's right. We're rewiring the organization. And what I start to cover in the book is all the layers of that. So I like to say money follows, culture follows the money. And so that implies that we need to rewire our funding structures as well. And in AI-native organizations, I advocate for three. I call it the Hyperadaptive Funding Model, but it essentially has three layers, right? So you have an innovation layer. So that's your company's internal VC firm, because good ideas can come from anywhere.-Mm hmm.-We know this. Let's enable it. Then let's have our value streams, which can form and they're fully funded value streams. That becomes the mainstay of the organization, not functional areas. And then we have our stable foundation. And that's keeping the lights on. That's making sure we have the infrastructure that we need. And when you think about how those three layers start to get funded, you fund, you fund ventures and innovation very differently than you fund your stable layer. And I believe that we haven't been able to suss that out in organizations because it's too much overhead. It would be too much overhead to fund our our organizations that way, although again, I know there's certain organizations that are leaders and have this down. But I do believe that AI can start to change that equation in terms of how organizations are funded, how the money flows, and then we'll finally be able to change the culture and change the way we operate.-As we imagine that as a future scenario, as a future end state, in whatever form that might shape, for each different industry or organization, what are the phases or steps to get there? Where does, you know, what's that kind of maturity model, those, those process steps? Where does an organization start? What does that look like?-Yeah, I appreciate the question because, you know, for a listener, they're like, hey, Melissa, that sounds great. Like, what do I do tomorrow?-Exactly.(laughing) And, and so that, I really took that to heart because, because I do have this background in lean and agile and transformation, I really purposely built the model to iteratively and incrementally start rewiring the organization toward that hyperadaptive, hyperadaptive future. And, um, and how I did it was I, this is a research backed model. This wasn't just Melissa sitting in a vacuum and, you know, pontificating. So I used AI. I used deep research to start surfacing the patterns of those organizations. Unilever. There's a great organization out of China called Ping An Insurance. They started their AI journey in 2008. And there are some really leading organizations. And I used DeepAI, or deep research, not just to surface them, but to help synthesize those patterns, to say, how do we get from here into that future. And so the hyperadaptive model is a five stage journey. And of course journeys, like all of our journeys, they're not necessarily linear, right? Sometimes we go off the path and different people are moving at different speeds. But you have to start with your foundation and get some foundational structures in... in place before you start to augment your tasks with AI, before you then move into agentic AI where the roles really start to change. And in the model, I feel like the piece that most organizations are missing is what I call the layer of support structures. So this is not only your AI councils, I advocate for a network of dynamic AI councils, but support systems for your AI leads. Do you have a network of what I call AI Activation Hubs, uh, which are the ones that will continually update your organization as AI changes. When you think back to the 1990s and the rollout of the PC, we didn't just like hand people a PC and say, well, good luck with that.(laughing) You know, we built IT help desks. We built these support systems to help people learn this new technology, this powerful new technology. And I feel like organizations haven't quite clicked into that yet. They're handing out the licenses. They're providing access to video libraries, and they're saying, “good luck with that.” And then they're disappointed when they're not having orchestrated agentic AI. So, you know, we'll get to the one takeaway, but uh, it may be related to those support structures.-Yeah, those support structures are key. I think, to help with that adoption and to look at it. So I'm, this may be a bit of a tangent. But here's where we get the human side of these reactions to great ideas, this, you know, complete paradigm shift of how we're moving. But when you start to shatter, the functional structure, our human nature is, “You're taking away my role. You're taking away my my value. This is my area. What am I supposed to do? Like, how, how, wait a minute! So HR team is producing videos based on, you know, producing comms, but yet the comms department isn't involved. Like, what, what, no, this isn't supposed to happen!” Like, what advice do you give to those leaders who are feeling like this, you're moving my cheese, you're taking away my responsibility, like, there's no order anymore.-Yeah. So the good news is, is we have precedent, right? And that was really the impetus for the book, is we have precedent in terms of factory automation. We have precedent in terms of something called DevOps, which is the automation of the software delivery pipeline.-Mm hmm. And what we saw... one of the first things I did was I reread this book called The DevOps Handbook. And I said, what lessons can we learn from that automation that we can now apply as other parts of the organization start to automate their activities. And to summarize it generally, what we see is we see people shift from doing the task to building, monitoring and maintaining the thing that does the task. And so what I like to say is that jobs are made up of tasks, processes, decisions and human interactions. And our real work is going to be deconstructing roles and figuring out... because we know AI isn't going to be able to take over a job. I mean, we're treating them like monoliths.(laughing) And we know this from factory automation, where only 1% of the world's factories are fully automated, and 34% of factory activities are non-automatable. And so I, when I hear this, like all jobs are going away, you know, it kind of rubs me the wrong way. And I can see both of you are like, yeah, that's not quite right. Um, so that's part of the rewiring too, is how are the jobs shifting, and how do we go from doing the thing to building, monitoring, and maintaining? And in stage three of the model, we do that on a pretty small scale. Like we inject agentic AI into really select roles so that we can monitor. I spin up something called the AI Impact Hub, so that we can see what does upskilling look like? What does reskilling look like? We know we hired good people, and we want to retain that institutional knowledge. So let's do that on a small scale, kind of figure out what we're dealing with before we start rolling out agentic AI to the whole organization. And in that way, we can we can really scale this because scale is is a thing. You know, how do you do this at scale?-As we keep imagining different scenarios through this phasing process, we're, we're talk— our listeners right now are going to come from a 100 different industries and perspectives and sizes. And those that may be in a high tech or a technology focused organization might be viewing this in one way. And then others who are attorneys or run janitorial services or run restaurants or run manufacturing. I mean, there's also... or insurance organizations or healthcare. All of them might be, well, well, would be coming from very different perspectives wondering, how does this fit for us?-Mm hmm.-Because in some ways, I'll say easier, conceptually, to then imagine if we're talking about a technology group, in terms of even coding, that might be a more straightforward approach because it follows some of the, even the language that has been used. What can we do to help apply this to non-tech related entities and companies?-Yeah. So I profile so many interesting use cases in the book, and Unilever comes to mind. They're a soap company. Uh, McDonald's comes to mind, they're fast food. And one of the use cases I profile is the stress of working in the store. And they with their AI, they're talking about their employees and how stressful it is when a rush hits. And they, um, they're trying to manage the drive-through. They're trying to manage the front counter. They're trying to deal with any customer service issues. And so McDonald's is asking themselves, how do we inject AI into this flow so that we can alleviate the stress of our frontline workers and our managers. And there's so many interesting angles here. One is, of course, mundane things like scheduling and keeping track of just some in-store logistics. Imagine if you're on the hottest day of the year and your McFlurry machine breaks. That was happening at a regular basis in McDonald's all over the world. What they were able to do is they were able to use AI to inject predictive maintenance into their McFlurry machines so that they had more reliable service on those hot days. And I wouldn't be surprised, because other organizations are doing this, if they also are monitoring the weather so that they can more proactively deliver more McFlurry mix uh to the stores that need it ahead of that heat wave. So those are just some of the innovative ways that non-technology companies are using AI and integrating it into their organizations.-That's a great example. Because all these examples, they give, well they give me and I assume they're giving our listeners just different ways to look at the adoption and the integration of the way AI is. So there's a premise I've heard you repeat a few times now of it's the shift from just doing to the kind of building and monitoring and maintaining idea. And it sounds... okay, that's where we wanted to go. So if I'm a listener and I'm saying, okay, great. I'm in a fairly traditionally structured organization. AI is coming in, whether it's Claude or ChatGPT or, you know, AI incubators that are in-house, whatever it might be. We want to become AI-native.-Yeah.-What, what are appropriate kind of horizon one, horizon two, horizon three benchmarks to try and get to?-Yeah. I mean, I, I think the first thing is, is again, this recognition of, of support, and I, it's a totally different way of learning. I like to say that AI learning is social learning. And part of this is because the use cases for AI are endless. And I think that's part of the other, the other part of the churn, is that people are, they're, they're “using AI,” but not in a way that really moves the ball forward for organizations. And so, um, the question becomes is how do you activate AI in a meaningful way? And I'll give you the example of Moderna. I love Moderna for so many reasons. One is, and I know we're going to talk more about AI North Stars, but they have a great one. And theirs is to deliver 15 drugs to market in five years with the help of AI. And if you know anything about pharmaceutical, you know that it typically takes 10 years to deliver one drug. So that is like, Daniel's shaking his head, like, that's amazing.-That's shooting high. Yes.-Yeah.-It's pretty high.-Yeah, that's the moonshot. Definitely.-Right. But all of a sudden what you get is you get focus. And when people are thinking about all... of all the things I could be doing AI, is this going to move the needle on our North Star? And we start to align around business outcomes rather than what I call random acts of AI. So that's my clarion call to leaders, figure out why you're using AI, and figure out what your philosophical stance is. Are you really using it to reduce headcount? Are you, are you looking to grow? Are you looking to improve customer service? Like, what is, what is that? And then communicate that out to your organization. Because that starts to bring the temperature down. Like, like people understand why they're using AI, what it's for, what we're trying to do with it, then start activating your leads. So Moderna had a prompting contest where they wanted to surface their best prompters in their organization, and they identified the 100 best prompters, which is a great start, but then you need to activate those people so that they can become your frontline change leaders in the organization. Because what's happening is we're seeing this bifurcation in organizations between the power users and everybody else. And we're not unlocking the value of those power users and creating learning contagion and social contagion. So then the other compounding issue... You got me talking here, Peter, so I'm going to keep going.-Well, let me pause you, Melissa, before you dive in. I just want to make sure when you said “prompters,” I just want to make sure everybody's following, I assume, you're saying prompters is those who are drafting and engineering the best prompts to enter into AI to then get the outcomes, correct?-That's right.-OK.-Yeah. Like, what cool thing did you do with AI?-Yeah.-Yeah. And then you've got to create...-And that's half the battle.-Yeah, it's half the battle. And then the other, the other two parts of the battle are one, how do you share that? And then two, well, those are the two part, two additional parts. Two is how do you keep up with that? Because AI is changing so quickly. And so that's where this layer of layers of the organization are so critical. You have the AI Activation Hub and their role is to really be monitoring those changes. What, what's new that's dropped this year, or this year, gosh, this week? And then how do I, how do I send that through the systems we've built to the leads to the practitioners that's just in time, just enough, just for me? And it shifts education from this, like, let me go to this 2-day course to I understand, I'm going to have to learn 15 to 20 minutes every single day, in order to keep on top of this. But we need to invest in these, these parts and pieces of the organization that can empower this fly— what I call the AI learning flywheel to happen.-It's almost like it's reminding me of different centers of excellence or expertise that have been built up over the years where whether it's in change management or talent acquisition or specific DevOps or a specific methodology, there is, there is this group of kind of expertise that they can then be utilized for higher level education training. And then there's like the more help desk version of it, or the self-service approach even. But all of this needs to be applied toward an AI mindset versus what I think is happening more often today, which is simply, hey, AI itself should be inherently easy, or just go and figure it out because nobody else has expertise enough to then guide and direct. And yet the challenges, there's only about a billion different combinations and options in which you can then utilize AI for. So...-That's right.-Back to this point around this North Star idea. I like this idea of this rallying cry or something to be able to help connect business issues and a business problem, to utilize AI. And we're using AI because that's the current technology vernacular. But otherwise, it's process improvement. It's value stream improvement. It's being able to add increased value to the client. And right now we're utilizing AI as the placeholder to describe that. So this, this intersection though between the value for the business problem and the value add for AI, what are good ways that leaders can go about crafting that North Star for their organization, without that becoming demoralizing in and of itself? There's kind of that, that happy medium there.-Yeah, I think so. I mean, I think you're absolutely right that, that the leaders need, the leaders also need support. And what I see happening in many ways is, is just kind of a handoff. The board puts pressure on the C-suite to “do something with AI,” the C-suite then goes to their lieutenants and says,“Hey, we got to do something with AI.” I mean, it's so abstract. And then, you know, and then, and then you've got the messy middle which is like, “Okay, I get I'm supposed to do something. Now I'm the one who's supposed to translate that into, into frontline reality.” And so I actually think one of the best things that leaders can do is really focus their efforts on the capabilities of AI. Not necessarily... the, um, the detail on how to build an agent, but really have a few trusted sources where they can turn to, to understand not only its capabilities, but it's limitations in a much more nuanced way. So, for example, I uh... I talked to an executive, he was a CEO. It was a much smaller organization. But he went straight to orchestration, and he wanted to orchestrate some very complex metrics for his organization. But honestly, the organization wasn't ready. And so he brought in some outside help to help with this orchestration because they wanted to do something with AI. They burned a lot of human capital, a lot of dollars. They, they got the thing working to about 70%. Not good enough. You know, it wasn't good enough for the clients. It wasn't good enough internally. And he now had a staff that was a little bit disillusioned with AI's capability. He felt like he'd spent a lot of time and money on this thing that, that didn't work. And there you go. There's, you know, there's another pilot in the AI graveyard. And so, regarding that North Star, really, if you, if you understand the limitations of AI and capabilities, and then understand it's a journey, you can set a North Star that makes sense for your organization.-I think that will also then as circle back to the FOMO. As you set that plan, set that North Star, it's sure, we may not be doing absolutely everything as we look to our right and our left, that others might be, but we have clear focus. We know where we're heading. And we can feel each day like we're making progress towards somewhere as opposed to just floundering out in the middle of the ocean without, uh, you know, without that clear direction.-There's so much low hanging fruit. That's my other thing. It's like, focus on those 15 minutes that you can save a 1000 times over. And that starts moving the needle. Like if you don't have your North Star in place and if you don't, you know, you're just getting started... start there. And you'll start to, you'll start to see progress.-Yeah. Well, this has been tremendous to hear these thoughts and to get us thinking about so many ways. So just to remind our listeners, your most recent book, Hyperadaptive, where can people get a hold of that?-Yeah, so it's out on Amazon. It's available for pre-order. It'll be coming later this spring. And so there are, there are select few individuals who are getting advanced copies, but I would encourage you to get your pre-order in because you will get, uh, some bonus materials that'll, that'll guide you in the right direction until you can have the physical book in your hand.-Yeah, that's fantastic. And we can, we can mention this in our show notes as well. So listeners, please go and check that out. And so as we wrap this up, Melissa, what's the one thing that leaders can keep in mind to be able to help shift their organization into a more native AI organization?-Well, I think the awareness that the rewiring is available, that it's doable, and that it's going to require investment in the people, the processes, the support structure beyond technology. And AI isn't a point in time. It's in constant motion. So this is a line item that will be on your, on your balance sheets and on your budgets for years to come.-Yeah. Well put. Well put. Melissa Reeve, thank you so much for being a guest on the Leadership Growth Podcast today.-Such a great conversation. Thanks for having me.-And to all of our listeners, thank you for joining us. Please like and subscribe. And we look forward to having you join us in future episodes. All the best along your leadership journey. Take care everyone. If you like this episode, please share it with a friend or colleague. Or better yet, leave a review to help other listeners find our show. And remember to subscribe so you never miss an episode. For more great content, or to learn more about how Stewart Leadership can help you grow your ability to lead effectively, please visit stewartleadership.com.