S04E18 Building Communities for AI Learning | Melissa M. Reeve | Wired for Wonder

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Speaker B: hello and welcome to Wired for Wonder where we speak to the architects of the future of work leaders using AI to dismantle the old grind and rebuild work around what actually drives results. I'm Lori Kirkland and today I've got someone who will tell you straight out, you cannot plug AI into a 20th century organization and expect 21st century results. The org has to rewire and she's built a blueprint for exactly how that happens. Melissa Reeve is the founder of Hyper Adaptive Solutions and the author of the forthcoming book Rewiring to become an AI native. Her model draws on 25 years of hands on organizational transformation work and 18 months of dedicated research pulling from the DevOps handbooks, Peter Senge's Learning Organization principles and Clayton Christensen's work on disruption than extending it all for the age of AI. The core argument is direct linear strategy to execution. Organizations will find that AI compresses those dimensions fast and the companies that survive will be the ones that learn to rewire iteratively People, processes and roles together. Before founding Hyper Adaptive, Melissa uh, spent six years as VP of Marketing at Scaled Agile where she watched the safe framework scale from 60,000 to over a million training trained practitioners worldwide. She knows what it looks like when organizations try to adapt a new model at scale and she knows exactly where it breaks down. What she brings to this conversation is a framework that starts where most AI strategies stall. Not with the technology, but with a question leaders are afraid to answer out loud. What is your AI North Star? And what do you actually believe about what AI is here to do? Welcome to the show.

Speaker C: Melissa, what a wonderful introduction. Thank you so much. It's a pleasure to be here.

Speaker B: Well, good. Well we're really excited to one learn a little bit about your book that's going to be coming out. But as we get started, when you think about what you're seeing in the marketplace and you talk about Your past and how leaders, um, are needing an AI North Star. In your experience, what does it tell you about an organization when they don't have one yet?

Speaker C: Well, it's understandable. I mean, I think we're all in what I call AI sense making. So it's this notion that AI is so big and so amorphous that it's actually hard to wrap your, your arms around it. And you know, are, is, does AI mean the technology? What are the implications for the people in my organization? What is the implication for the organization itself? And I think any, any leader worth his or her salt really has to recognize that this AI sense making is a phase to go through and, and not be freaked out by it. Not be freaked out that we don't have all the answers yet. And I do feel like, you know, if you're an organization and you don't have that AI North Star yet, it's okay. It's part of the process.

Speaker B: Um, AI sense making. I have not heard that term before, but man, does that like really describe what is going on where this is not as leaders. I think about examples where I've worked with companies that are like, oh, my team is doing AI over there. And you're like, no, no, no, no, no, no. Every one of us is going through a change right now. Um, is that kind of what you're talking about? Of like, hey, we're all. Or give me some more details about what you mean by AI sense making?

Speaker C: Yeah, I mean it actually, it came out when I was listening to two founders go through their AI journey and it was aligned to our local women in AI group. And these two particular founders were telling their story of how they entered into AI and how AI ignited into their business. And as they were telling their story, one thing that came just, just hit me hard was this notion that before they were able to get hands on with the tools and really, really understand what AI meant to them as individuals and as their companies, they actually had to find a trusted community to separate the hype from reality. And it really resonated in what I'd been seeing in executives and in what I had been seeing, uh, on the front line as well, that there's so much that we're being told about AI, all the jobs are going away and uh, it's going to change everything. And we know at a visceral level that some of that is true and some of it may or may not be true. And so part of the AI sense making is, is parsing, doing that activity with a Trusted community to separate hype from reality. And I don't think we've called it out yet.

Speaker B: No. And that is so powerful because it's interesting. I see the word collective coming out a lot, which I think is the same thing you're saying is all of a sudden where you may have gone to get information three years ago with AI, uh, you actually, it actually helps you to look and become part of different communities. So I've seen a lot of different communities and collectives pop up, but I love that you just really put some true value into what is happening. Right. We're all trying to look at things differently. And so by working with other people who may not be, you know, in your direct world, every day you start to open up your, your lens or your frame for, for a bigger picture, which helps you to be more open to possibilities. Um, is that, is that really what you're saying when you talk about community? Is that, that it is?

Speaker C: That's right, yeah. These collectives and I think there's two, two levels there. One is AI is moving so fast that it's hard to get it codified in a way that we can all keep up with. So one of the ways we're keeping up with it is, is through other people and through these trusted communities. It's, it's through. Even if you read about the new, uh, capabilities of Claude 4.7, it's one thing to read about them, it's another thing to be in community and hear what people are doing with it and seeing what people are doing with it, because the use cases are endless. And I like to say it's like drawing a cat. You can tell somebody what the capabilities are, which is the equivalent of saying, draw a cat. But if you have 30 people in a room, you're going to have 30 completely different cats. And so that's where the community comes in, where we can see how everybody's drawn their cat with these new capabilities. So I think that's like one level and then the second level for leaders is understanding that not everybody's in the same place when it comes to this sense making. And it's a very individual activity. So if you're a leader and you're like wanting to have your organization ahead of where they're at. Just recognize that everybody has to go through this phase and we're not going through it at the same rate or the same time.

Speaker B: That lands so well for me. Right. Um, being obsessed with kind of the human technology intersection and how each of us have the things that Motivate us or the way. The style that we learn and which is deeply personal, but it is part of the bigger collective when we give ourselves that opportunity to say, hey, I'm going to learn in the way I want in this community is providing, um, a safe place for me to explore, to start to understand the use cases so I can connect where I'm at today and the way I want to learn to what things are possible. And it can make sense. And it's such a, that's such a deeply human way to learn right through storytelling, through, you know, we're, we're wired to look at the survival through the community. But it's different than what you see on social media, which you hear about echo chambers. It's actually the opposite of that. Where it's, what you're talking about, I think is, no, let's actually learn to see what's possible so I can make more sense of it in my head rather than just validating the hype that's out there or one point of view. It's actually the opposite. We're pulling away from that and saying, wait, let's look at all the possibilities so I can make sense to it for myself.

Speaker C: That's right. And it's one of the reasons why what I call static curriculum isn't really working, why you can't really train your way to an AI literate workforce because there are so many use cases. You have to create what I call a learning arena rather than just this one way communication of this is what AI is and this is how you do it. Because we all, I like to say AI learning is social learning. And so we're all learning from each other. And I think that's why this has felt more difficult than just a simple technology rollout. I mean, obviously the speed is also factoring into that, but it's also the number of use cases that it's uh, it's not deterministic. So the outputs look completely different. Your prompt versus my prompt. And I think all of that, uh, together creates a totally different experience for people on the front line and challenges for leadership.

Speaker B: I love that. And you're, you're putting into words so much of what is really going out, going on out here, right, is, is that it's like we're moving from fixed leadership. That was, here's the directive, here's the plan, here's the outcome. It's very logical and it moves. But the speed of which AI is creating, you can't rely on that nice predictability from this Step to this step to this step. Because 200 things, the circumstances have changed so much. Right. So instead what we're seeing is a shift to more fluid thinking, which is, okay, I have to know that things are always going to be changing. So I just need to look at things with different, different human skills than I've had before. Um, and what I hear you saying is like, right, there's this opportunity to lend to the things that are actually naturally very human. I mean, we are moving through life every day with the circumstances always changing. We just haven't had to do that in work because we've had a pretty predictable environment. But now we're seeing how we get through our everyday lives. Hyper adaptive. Right. Which is what humans are great at. We're seeing that those same skills have to be applied at work. Is that, does that feel like that's a legitimate connection for you? I'm trying to make sense of it in my mind. And so I'm wondering if, if that is something you're seeing as well or if that, that is making the same connections.

Speaker C: Yeah. And I think the shift. I'm going to riff on the navigation metaphor that, that you were putting forth, which is before I feel like leadership was we're going to get to the top of the mountain and they might even say we kind of know what the route looks like. And so we're going to go up to base camp one and then we're going to go up to base camp two, and then three and four. And I feel like the shift for leadership is you can still say go to the top of the mountain and that can be your an or star. But the difference is that you don't know the route because AI is paving new ways of getting to the top of the mountain. And so what you've got to do is you've got to empower everybody who's climbing the mountain with you to be able to sense and respond and use these tools to see what's in front of them and determine their next path, best steps. And when you think about that shift, it's a decentralization of decision making. It's an empowerment of frontline, uh, experimentation. And it's embedding learning loops into the route as you're going up the mountain. Because somebody might discover that there's a crevasse and they need to not only signal that to everybody else in the organization, but everybody else in the organization needs to register that, but also register that that crevasse might shift. It may or may not be there. The Next time somebody crosses over that spot. And so I feel like from a leadership perspective this is a really different way of moving people toward a goal.

Speaker B: Mhm. But I love that because that image of the mountain is so powerful of the AI North Star. Like you are still directing, but it is a different type. You don't want to limit people anymore to say, go to base camp one, go to base camp two, because what if you run into a uh, big hole and in the meantime somebody's invented something to fly over the hole so you don't actually have to go around it anymore. You can't shut off possibilities. Like it's almost not safe to do that anymore. Whereas it used to be the very safe way in business to get there because you might be able to. I mean that's the fun thing about having so many possibilities is you might be able to shortchange that, that, you know, that climb to your outcome, um, much easier, uh, than you ever were before. I mean, and that is so exciting when you start thinking about it. Like, I can get to the top of the mountain and no, I don't know how, but I can probably get there faster.

Speaker C: Yeah. And uh, you know, in our pre conversation we talked about Moderna and I love pointing to Moderna and their AI North Star because they put us, you know, their flag on top of the mountain is let's release 15 new drugs in five years with the help of AI. And if you're familiar with that pharmaceutical space, you know that just getting one drug out in 10 years would be a monumental accomplishment. And so like, even as I'm talking, I'm imagining deploying multiple teams to try and get to the top of that mountain. And yet it helps bring clarity to the decision making because we know what the goal is, we know what we're trying to do with this brand new tool. And it becomes less about using the new tool. Whether it's a new set of ropes to climb the mountain or uh, some powerful ladders or new camping gear, the tool isn't important. It's what we're trying to do with the tool that keeps us all aligned and reaching towards the same goal. Wow.

Speaker B: You understand this at such a depth and such a, um, I think such a great point of view for leaders. This is really, really incredible stuff. Now when you talk to leaders, right. That is different to just say, hey, it's right. Like we all want productivity, revenue and efficiency. Right. Those are just the antis to even you gotta have, you know, hiked a little bit before, before you go to the type, the top of the mountain. And those things set you up.

Speaker C: Mhm.

Speaker B: But when you think about the future and where leaders are going, tell us a little bit about hyper adaptive and how that plays into that mountain scenario.

Speaker C: Sure. Like you, you introduced it so well in the, the beginning as you talked about this notion of a linear organization. And when I think of most leaders today, they have this linear organization. So this is strategy to execution, concept to delivery, lots of handoffs and delays through both of those dimensions. And when you think about A.I. uh, compressing both of those dimensions, I think most leaders get that like, okay, we'll have more streamlined decision making and we'll be able to get stuff out faster. Faster. The question becomes how do you get from your current operating model into that more AI native operating model of the future? And so what. Which is the hyper adaptivity, the ability to sense and respond in, in near real time. So the question becomes how do you iteratively and incrementally start rewiring the people, the processes and the roles in order to uh, keep rewiring while not disrupting your business as usual? And that's the key. And in the book I start talking about these support structures you need to spin up in order to do that. And I feel like if there's one thing that leaders could dial into, it's this notion that you do need to spin up support structures. And I like to say in the 1990s when we put a powerful new technology on people's desks called the PC, we didn't just say like, go have fun, go play with it. We spun up IT Help desks, we spun up entire IT departments in order to support the humans in integrating this powerful new technology into their daily lives.

Speaker B: Oh, I love that. I love the way your, your language is so powerful here and really giving very real clarity into what's going on within organizations right now. Um, and to tie back that those support systems to what we were saying before about the speed and the dynamic, the dynamicism. Right. That we are all facing. Right. It's much faster. That makes so much sense that organizations now have to be thinking. We were talking about the navigating um, analogy. Right. If you're on a boat, you need to make sure that your crew knows where a spot of safety is. Right. Like if you're seeing a big tidal wave coming, you want to make sure they have a place to anchor or. Right. They know how to turn away from the storm. They, you know, know how to do that. And that's what I, when You're talking about those safety, uh, mechanisms. I feel that too. Like, that's a really good analogy of you have to look at for your own organization. How do I make this change safer? And it's interesting because every organization will have a. Like, there's not a one answer, right? It's. It's what your living culture is. Will, will determine how do you make this psychologically safe? How do you make this, um, you know, so that it's. The. The business isn't being interrupted or disrupted while you're disrupting the organization. Right. There's a lot of things to think about.

Speaker C: You think about that AI North Star and setting that. That stake in the ground that says, this is why we're doing AI all of a sudden it shifts the conversation from we're doing it to reduce headcount or we're doing it to just, uh, get more productivity to now there's. This is the reason. It's. It's a better customer experience. It's getting to market faster. It's, uh, it's inventing. Achieving that big, hairy, audacious goal that we've never been able to do in the simple act of articulating that starts to create the psychological safety. And once you have the psych. Psychological safety, then you can start putting these other pieces of what I call support structures in place. And I'll just give the audience kind of one small example. So I know many people have, um, appointed their AI leads, and that's a great first start. I'm going to go back to our mountain analogy. You probably have your Sherpas that know the mountain well. Uh, they've gone up at a couple of times. You know, they're more familiar with the AI but the question is, how are you supporting them programmatically? Because for every AI lead, at least the executives I'm talking to, you know, let's say there's one AI lead, there's 80 or eight other people, you know, so 10% are AI leads, 80% are just kind of middling with AI or resistant to AI and so how do you take that knowledge that the AI lead has and then start to in, like, spread it and create what I call social contagion? You know, spread it throughout the organization so that now everybody starts to know how to climb up the mountain and

Speaker B: love that social contagion. M. Tell me a little bit about that.

Speaker C: Yeah, and I like to point to an example from, um, PricewaterhouseCoopers. So they had something called prompting parties. And it sounds great, right? It sounds like Friday afternoon there's going to be pizza and we're going to all learn together. And that's exactly what it is, is let's take people who have like, types of work, we'll put them in a room together and let's experiment and play and create capacity for this learning so that the power users can share what they're doing with everybody else. And in that way, we're all starting to move up to the mountain together rather than just having what I call the bifurcation, just a handful of power users and everybody else.

Speaker B: Yeah. And you know, I'll share with you my experience with, with working with, you know, cross industry, with many organizations is, is that those people who are racing up the hill, like they want to feel the support of the whole, of the whole group because it's not easy. Right? You're, you're going in and exploring into unknowns. But there is a problem where those people, if you just keep relying on them, they get too far ahead and then they get discouraged because the organization is way too slow.

Speaker C: Right. That's a great point.

Speaker B: And what you're talking about is like, no, let's have, you know. Yeah, let's make that. I'm Gen X, I'm always about a party, but you're talking about like, let's make this all of a party and use the energy of the whole organization to propel this forward. Like, it's not you, it's not me, it's us and naturally, humans. When energy puts together, if you're focused on, you know, if you're a really fearful company, you've got fear that can, you know, passes around contagiously. If you're a really innovative company, you've got that. But most companies are somewhere in the middle, middle and they can actually use the, the social contagion to, to create and communicate in positive ways of, hey, we don't actually know where we're, we know where the destination is, but we don't know how we're going to get there. And you are part of the contribution. So now when we go back to that mountain person, you're thinking, this guy's all the way up, but instead of when he looks back, everyone's still at the bottom. Um, they're all at base camp or, you know, there are people scattered. So he's not, they're not feeling so alone. Right. Like, I think that, um. Brilliant. Just a brilliant, brilliant analogy.

Speaker C: Well, and I'd love to say that, you know, I am brilliant and you know, I can create this kind of stuff, but it's really rooted in very proven historical patterns. So I mean this is John Cotter in leading change 101, right? Let's get our early wins, let's make it safe for other people to lean in. Let's use those early wins to pull other people along. And you know, I feel like what I did was I took. I saw how the puzzle pieces fit together. I saw that this would be an extension of John Kotter and Peter Senge and that AI might finally be that forcing function to help us create the learning organizations we've always wanted to. And I integrated what we've learned along the way because we have a lot of failed transformation initiatives where we know what doesn't work. We know big bang doesn't work. We know that um, M. Um, just from the outside in doesn't necessarily work. That was business process re engineering. You can't just bring in a bunch of consultants and force the culture to change. It's gotta be inside out, it's gotta be middle out, it's gotta be incremental. And putting all of those together, start to increase your chances of success for any sort of technological adoption.

Speaker B: Well, and what you're talking about is so human focused, so people based on like. Wait a minute, for years, because of like kind of the manufacturing mindset, we've been able to almost take humans out of it. But we've got this whole system of people that we actually, we've always all wanted to be part of it, right? But it. The kind of operating system didn't happen. But now we have this like ability of like, well, hey, we've got the technology, we actually can get way further, faster, safer and with a lot more fun. If we really do think about how do we use the energy of the people, how do we actually take the value of our people in our organization and have somebody who's great at wayfinding shoot up the mountain faster, right? Somebody who's great at risk analysis. Be like, I'm going to do the water station at the bottom, you know, whatever it might B. You're really talking about tapping into the people system to create and build. Yes, we all want the outcomes, we all want the revenue. But like you could actually have a really great, you know, hike up the mountain getting to your income or to your outcomes. Whereas that was not always the case in the workplace the last decade, you know, or so people are. People have been burned out and overworked and not feeling like they could contribute. So you're really tapping into something. Um, I Love how you related it back to the, the great change thinkers too, of our time as well. So that is super powerful.

Speaker C: Yeah, I mean, I felt like the work environment's been broken for a while, and I think most people understand that. And we just keep trying to change it, whether it's business process re engineering or whether it's digital transformation, whatever it is. We've all felt that frustration of spending an hour and a half to submit, uh, an expense report. And because the processes are, they're just broken in so many ways and we feel powerless to fix them. So I really wrote the book because I think there's better ways of working and I think that AI is the forcing function to finally change the operating model. And it's not going to happen overnight. So I, uh, you know, like, I remain, I'm such an optimist and I remain so optimistic about our futures and what, what we can do. And together, if we can just see a new way of working and work

Speaker B: to implement it, well, please hold onto that optimism. You are such a light to talk to and I am in full alignment with you. We have some really cool things we can do if we're willing to look at AI as the excuse. We've all been waiting for something that was really hard and painful for you before. It doesn't have to be anymore. So let's use AI to get rid of that and then we get to get into the juicy, you know, stuff that makes us all really enjoy work, which is how do I contribute to solve something greater than me? Right. Like, that's a very, very human way, um, of operating. And to be honest. Right. We all operate best when we're, when we feel like we're contributing to something greater than us.

Speaker C: Yeah. I'd like to pull on a couple of things from the book. One is in stage five, I profile ping on insurance. And this is an insurance company out of China. They started their AI journey in 2008. So think about what can be done from 2008 to 2026. They got their data house in order. They figured out with this additional data that they could grow beyond their roots as an insurance underwriter. And they extended into healthcare, they extended into finance. So you see that this is a company that's not just harvesting the gains from their AI initiatives. They're not just saying, hey, we got so much more efficient, so let's cut headcount. They said, hey, we're getting so much better and so much more efficient. What else can we be doing?

Speaker B: Uh, I love that.

Speaker C: Yeah. So they extend into healthcare, they extend into finance. And now I want you to imagine being extremely customer centric with that point of view. If you know that somebody's been laid off, maybe now you offer them mental health support, maybe now you refactor their loans to get them through this time. And you have this amazing synergy between these products that's grounded and centered on the customer because you have the data to do it. And I feel like that's just a hint of what the leading organizations will start to do when they set their vision bigger and they think about what AI can enable instead of what AI will cut back.

Speaker B: M. You are talking about the way nature operates, right? Nature doesn't say if a tree is here, the other tree loses. Right? No, it's a win win for everybody. And that example is such a beautiful example of, um, this company wins and they now get to help other potential people win. Or you're, you're. That, that. I love that analogy of, you know, a loan company refactoring like they would get new products longer, you know, like potentially longer customers and at the same time they're helping somebody at the time they need help. Right. Like, what a beautiful thing to be like, wait a minute. Everybody wins in that scenario. Which is, which is pretty cool.

Speaker C: Yeah.

Speaker B: Um, I would love to ask you from your book because you're so knowledgeable and you were, if you were to think about to tell our audience like three human skills that they should be adopting right now in this time of change, what would you advise, you know, leaders and really everybody, what would you advise them? What would be the three human skills that you would say? These are what you should be focusing on right now. Um, because these are what are going to help you grow and move into the future.

Speaker C: For sure. I think it's this, this critical thinking and you know, we, we are going from doing the task to evaluating the output. And so you need to be able to independent of AI. Like sure, you can ask AI, like what should I be thinking about? But if you independently think about where could this model be wrong? What else should I be asking this model? Uh, what might the model be getting right or wrong? You're already leaps and bounds ahead of most people who just take the output and they digest it. I think the notion of creativity and I believe that creativity can be taught and I think we hear that a lot. Well, the AI, ah, can't be creative. Well, it can be creative to an extent, but I think the uniquely human part of creativity is taking two unrelated things and combining them into something that hasn't been thought of before. And I think what AI starts to unleash are what I call adjacent competencies. So, you know, you're a musician and now you also might become a software engineer.

Speaker B: Ah.

Speaker C: And in fact, the Claude Code hackathon, the top five winners were a cardiologist, a musician, a lawyer, a civil engineer, and one software developer. So those first four are people who didn't have that software development background and they spread out into a new competency and they won.

Speaker B: Right.

Speaker C: That's human creativity.

Speaker B: I mean, that is a beautiful example. I love that.

Speaker C: And I think the third one is the growth mindset. And I know that that's kind of become one of these overused phrases, but I think it's really about leaning into the change. And we know that change is going from being episodic to almost always on. And if you don't have that ability to accept that, and I know we've all got different tolerance for change and, and that's okay. But I think the winners in this next X number of years will be the people who can really say, okay, things have changed. Whether it's your incentives, whether it's your roles, whether it's the career ladder melting into a portfolio, it's those people that I think will really succeed in the age of AI. Wow.

Speaker B: Well, Melissa, you are just a great thinker, um, clearly seeing such a clear vision of the future of work. And I really want to just thank you for being on today and giving, sharing some of your insight for our listeners today. If, um, people wanted to get in touch with you, where would be a great place for us to send them?

Speaker C: Thanks so much. So, two ways. Uh, one, I'm on LinkedIn, Melissa M. Reeve. And then second, Hyperadaptive Solutions, uh, is my website, and that'll give you a nice overview there too. Ah, awesome.

Speaker B: Well, thank you for imparting your knowledge and wisdom to the audience today and thanks for participating on the show. Thank you everyone for listening.

Speaker C: Thanks for having me.