Author of Hyperadaptive: Rewiring the Enterprise to Become AI-Native (Part-1)

Tom Stoneman:

Hi. I’m Tom Stoneman, and this is The Intelligent Enterprise, where every two weeks we take a break from the chaos of enterprise life and get inside a big idea by getting outside of it. Each episode, we meet an industry expert who helps cut through the noise from all the updates and rollouts while exploring one of their favorite break-time activities. It might be over a coffee, a bike ride, or even an afternoon on the water, something that gives them some headspace when they’re deep in a problem.

Today, I’m joined by Melissa M. Reeve, author of the new book, Hyperadaptive: Rewiring to Become an AI-Native Organization. Melissa has spent her career studying how enterprises evolve, and she’s known as one of the sharpest thinkers on how organizations actually thrive in an AI-driven world. Her new book makes the case that companies who win won’t just be the ones who adopt AI fastest, but the ones who fundamentally rethink how they operate.

Melissa M. Reeve:

When you had a steam-powered factory, you can’t just switch over and have an electric-native factory. We have to do it gradually over time, and I think that’s what many organizations are missing.

Tom Stoneman:

Her book is out now, but I got an early look. And honestly, it changed the way I think about some of the challenges I face every day. In part one of my conversation with Melissa, we discuss why so many AI investments still fall flat and how organizations can shift their thinking from optimization to adaptation. Let’s get inside the future of enterprises by stepping outside of them.

One of the things that really stuck out… We all know that enterprises are investing a lot in AI. Some of them are doing it because the Joneses next door are doing it, and they just feel they have to. Others are doing it because they see opportunities. But in a lot of cases, it’s just being layered on top of legacy systems that really weren’t designed to handle this. Because it’s treated as an add-on right now, I think a lot of customer value, and internal value for that matter, is siloed. And I want to ask you from your perspective, why is that still such a common pattern? And where are things actually breaking down right now with companies that aren’t getting it?

Melissa M. Reeve:

Well, it’s a natural reaction, I think for a couple of reasons. One is it takes time to figure out what these new patterns look like. And I actually think the mistake that many companies are making is that they see the future and they know they want something like orchestrated agents, but they don’t know how to get there. And so most companies, most organizations don’t have the luxury of building a, quote, unquote, “factory” from scratch.

So if we just use the electricity example, when you had a steam-powered factory, now electricity comes on the scene, you can’t just switch over immediately and have an electric-native factory. We have to do it gradually over time. And I think that’s what many organizations are missing. You have to do it incrementally and iteratively over time. And what the hyperadaptive model does is it starts to show how that might look.

And I think when organizations click into it, they get it. It’s like, “Oh, we have to do this deliberately, and we have to spin up some new systems to get us from where we are today into this more AI-native state of the future.”

Tom Stoneman:

I know you mentioned in here that most organizations, their operating systems, the legacy ones at least, weren’t designed for AI. Can you talk a little bit about that and maybe those comparisons? I know you had in the book there was a comparison of competition, how a younger organization that’s very AI native competed with just a few people rather than up against a larger organization.

Melissa M. Reeve:

Yeah. If you think about the legacy of most enterprise organizations, they’re what I call linear organizations. So you have strategy to execution. You have concept to delivery. What we know for sure is that AI’s going to compress both of those dimensions, that the organizational model based in Taylorism… So you think about Taylorism, 1911, what was going on at that time, it was manufacturing. And with manufacturing, you had the managing class and the laboring class, and the managing class’s role was to find the one best way of doing things and impart that onto the laboring class.

And then we move forward into right after World War II and we have globalization and we have the rise of the functional silos, and that helped us create standardization within the functions. But with AI, those functional boundaries really start to blur, and we start to get what we call adjacent competencies. And so it really does require a new way of operating.

In the book, I highlight the company of Tomorrow.io. And so this organization tracks, you know, everything from movie studios… You know, they’re shooting a lot of times outdoors. They need to know the weather. There’s a lot of actors that could be sitting around if it was raining all day, unless they wanted the rain. Railroads. Are their trains going to get derailed because of high winds?

And Tomorrow.io was a brand new organization. When I profiled them, I think they were about $80 million. They’ve grown since then. But at $80 million, they had, I think, three or four people in their marketing department. And that was handling all of the events, all of the social media, all of the email marketing. And I tell the story of a proposal or a pitch they did to a major Hollywood studio, and they were really hitting above their weight. There’s other weather forecasting organizations that were two, three, four, 10 times their size. But what they were able to do is they were able to take this movie studio’s… all the movies that they could get their hands on that the studio had ever made, ingest them into AI, and then create their pitch, their actual weather pitch in the style, in the cinematic style using real video clips from this movie studio.

And it got them… I think it was into the second or third round of evaluations where they might not have been given the time of day before AI. And that’s an example of an AI-native organization that is able to go up against the giants. And I do like to say that I anticipate giants will fall with AI transformation.

Tom Stoneman:

Most people working inside large organizations know the feeling when a good idea stalls and a simple decision takes weeks. Momentum dies somewhere in the handoff between the people who have the idea and the people who have to approve it. Melissa has a framework for fixing that.

Melissa M. Reeve:

So when you think about the decision-making structures, when I think about the hierarchy and I think about how traditional decision making works, I think there’s a lot of, one, coordination cost and then, two, friction. And friction is really, in my mind, a lot of the handoffs and delays.

So you talked a little bit about approval, and let’s just say that we want to make a decision. I remember talking to somebody… A lot of our examples here in marketing because we have those shared marketing roots. And this particular person worked for a large enterprise organization and was in charge of the pay-per-click budget, which is like your Google Ads. And they saw that an ad was underperforming, an ad groups underperforming, and they really wanted to switch the budget. It took them six weeks to get approval to switch $10,000 in spend. And this is for a multi-billion dollar organization.

And so when I talk about friction, that’s what I’m talking about. And when you think about what’s underneath that type of a friction, it’s really that we don’t have a lot of certainty around the decisions that we’re making. And so the question that I pose for organizations is, how do you use AI to really improve the quality of your decision making? Because now we can ingest massive quantities of data, do massive amounts of analysis in order to make better decisions.

And so I think if we allow our systems to do that, so if we change the way decisions are made, then we’ll end up having better decision making that’s faster, that’s smoother, that’s more informed, but it’s a different muscle. We have to give up this level of control that says we have to go through three layers in order to make sure that the spend is spend that we really want to do. And I feel like that kind of encapsulates the spirit of AI-augmented decision making that is one of the five capabilities of AI-native organizations.

Tom Stoneman:

So, Melissa, I want to talk about some of the friction that happens when AI gets layered into traditional enterprise infrastructures. Quite often, I’m pretty creative. I’ll come up with some ideas. Great campaign. Looks really wonderful. Send it up the chain. We present it to everybody. It gets all the way up. Okay, great. And then it starts coming back down for implementation.

And I use the analogy that we created a wonderful dinner plate, and it always comes back down as vanilla pudding. I mean, not always, but it happens. And a lot of that is… You used in your book the analogy of the phone game where it just gets a little bit changed and a little bit changed and a little bit more. And by the time it gets done, all the edges are rounded off, and it’s just a big… It’s a ball instead of a square. Can you talk about that a little bit about how… Because I know you’ve got a lot of examples in your book because it is a major friction.

Melissa M. Reeve:

Yeah. And in the book, I talk about both friction and fidelity loss. So in my mind, friction is really the delay. So you send your idea up the chain, and it probably takes days, weeks, sometimes even months for that wonderful five-course dinner to turn into vanilla pudding. So there’s that type of friction which keeps organizations from getting to market as fast as they want. It causes delays in the system. These initiatives die on the vine.

And then there’s the fidelity loss. And the fidelity loss… It’s interesting that you described it as you’re sending these ideas upward because a lot of times fidelity loss also happens from the top down where leadership has this vision of what they want to accomplish, but by the time it gets translated through the layers, it doesn’t look anything like the original vision intended. And a lot of that is that game of telephone.

I think there’s also this disconnect between frontline reality and vision. And we actually are seeing a lot of this disconnect right now when it comes to AI. You have organizations that really want to do AI, and it’s not enough for people just to say, “Go do AI.” And that’s an example of friction because you’ve got to take that concept of implement AI and start to translate it. And it’s in that translation that a lot of the friction appears.

And we were just talking about the story of a person who wanted to shift budget in their marketing organization. And when you think about the amount of misspend that happened in that six weeks, it was greater than $10,000. And so that’s really what we’re trying to improve when we are talking about becoming AI native. And I think that’s what we’ll unlock with AI, is better, speedier, faster decisions. And I like to say that AI-augmented decision making is one of the five core capabilities of AI-native organizations.

Tom Stoneman:

Yeah. It used to be you could have fast, cheap, good, pick two, right? And these days now, it has to be all three. And being in demand generation, by the time things get to me, they’re already out there for the most part, or it’s a launch getting ready to happen. And so there used to be sort of a luxury of some time. You could make everybody hold off a little, come up with a good campaign, get it out there. Those days, I think, are long gone.

And all I hear is, “You know what? We’ve got to get this out. It’s got to be fast. We got AI tools now. Why isn’t it done yet? And it needs to be as good as the last one or better, and it has to cost less.” You know? So I think a lot of these things we’ve experienced over the years, but it’s accelerated to the point of almost absurdity until this smooths out.

When the pace tips into absurdity, even the people who write the playbook on adaptation need a way to step back from all of it. For Melissa, that means getting as far from a screen as possible and enjoying the water.

Melissa M. Reeve:

So when things are getting to be a little bit much, I like to say that I just want to sit on the beach and drool, which really just means sit on the beach and stare off into space. But I live in Colorado, and there aren’t any beaches, much less a lot of water. But we do have a few reservoirs. And so my husband and I will take our kayaks, and we’ll drive to one of these reservoirs. And we’ll just paddle around and commune with the ducks. Sometimes we’ll bring the dogs.

I like to call them lake days. We’ll pack a little cooler. We’ll make a whole day out of it. Because for me, kayaking, it’s a little meditative. I try and use that time to really stay present. And in this world where we… I don’t know about you, but I wake up with a screen in my hand. It feels like I go to bed with a screen in my hand. I sit in front of a screen all day during work, and then I might even be sitting in front of a screen watching TV at some point. Or even now if I’m reading a book, it oftentimes is in front of a screen.

And so to just unplug from a screen and be out on the lake and trying to smell the breeze and hear the sound of the water, to really see the scenery around me, watch the clouds as they’re going by, I feel like it feels very grounding to me and just a feast for the senses. I find when I’m done with a kayaking session, it just feels like the world is a more sane place, and I’m ready to face whatever it was that was clouding my mind in the first place.

Tom Stoneman:

Sounds awesome. Let’s move on to how we sort of frame up what’s going on in organizations and how it affects everything down to the customers. So I know in your book you talk a little bit about a lot of organizations trying… It’s like building a Mars rover on a Model T assembly line. And I love that because it’s just so true. The way things are going now, that’s not going to work. So you highlight a lot the shift from functional silos to value streams. Why is that such a critical change in the AI-driven world now?

Melissa M. Reeve:

We touched on it lately in this notion of adjacent competencies. So you identified as a demand generation specialist. And I’m sure you’ve got… Obviously, you’ve got some background in recording and audio production. You might have some other more latent skills or capabilities. And I feel like AI actually further unlocks capabilities within individuals within organizations. And so I think that this notion that you will only be a marketer or you will only be a salesperson will eventually become quite limiting.

And we’re seeing the most forward-looking organizations. Unilever is one. MetLife is another one. They have something called MyPath. Unilever has something called U Work. And what it does is it starts to break jobs down into puzzle pieces. So what are the skills that you bring to the table? So it breaks jobs down into these puzzle pieces, and it breaks people down into all the different skills that they bring to the table.

And then it says, “What is your purpose? What really brings you joy? What is rewarding for you? Is it seeing the thing finally made? Is it the conceptualizing?” You get a sense of that. And what they’re trying to do is they’re actually trying to map people with the help of AI to the internal opportunities. And when you are operating in a dynamic system like that where rather than a career as a ladder, you’ve got a career as a portfolio and you’re dynamically moving from opportunity to opportunity, it doesn’t make sense to organize by functional silos.

And so what value streams start to do is they start to work from the customer backward from concept to cash. And I like to use the example of a bank, because most of us have interacted with banks. And they might have a different product line for retirees. They might have a different product line for new grads. They might have a product line for ultra-high net worth individuals. And all of those are individual value streams. And the goal is to staff those value streams with all of the talent that you need and organize in that way.

This isn’t a new concept, right? It’s been around from lean, and it’s been written about for many, many years. But what happens when you do that is you start to reduce the friction, because you don’t have all of the handoffs and delays that you might have if you were dealing from function to function. And so I really feel like that type of an organization makes much more sense for the AI-native organizations of the future.

And so again, when you think about a Tomorrow.io, they’re able to organize that way from the get – go. And so the question for the enterprises is if you are a linear organization and you’re organized by functional silos, how the heck do you start to rewire yourself into this value stream-oriented, hyperadaptive organization of the future? And that’s really what the hyperadaptive model starts to break apart and starts to say, “Here’s how you can, again, iteratively and incrementally start moving your organization in that direction.”

Tom Stoneman:

You’re right. That’s exactly where it… Well, it spoke to me all the way through, but that really opened my eyes because I’ve been doing this a while. And I go back to when we started doing A/B testing and that was like, “Ah, this is great. Now I can see which one…” But it took kind of a long time. We had to wait until one played out, the other one played out. Okay, the winner is this.

And then early on we would just forget about it and say, “Okay. We’ve done our job and things are great. It’s going to be this great…” But it wasn’t always. And you in your book mentioned it’s no longer just A/B testing. It’s A to infinity, which is just mind-blowing, right? Because it’s this 360 radar thing where you can just see things all the time and adapt on the fly at speeds that just nobody even though that was something you needed to do, let alone be possible, right? And I love that.

You described a difference between optimized organizations and hyperadaptive or even adaptive ones. I believe optimized was more… Like I was saying, I’ve done A/B testing. We’ve optimized. We’re doing great. Everything looks really good. We’re ready to go. As opposed to hyperadaptive where, yeah, we’re great, but we need to be looking at things constantly and using our employees as sensors to see what’s happening and… Tell us a little bit about that, because I just love that description.

Melissa M. Reeve:

When I hear that word optimized, my mind immediately goes back to the studies in the 1930s and 1940s where we’re looking to optimize work. And what they found… I forget the exact study, but it was like, how many widgets can you make in an hour? And essentially, the people were just gaming the system. They knew that they were being tested and they were being observed, and so they just kind of gamed the system. And so to me, that speaks to this notion of optimization. Let’s get efficiency at any cost.

And it brings to mind the story from a meetup I was at last night talking about AI. And there was an executive assistant there, and she was talking about how her boss just said, “We want to 10X your EA capabilities.” And that’s this optimization. That’s saying like, “We’re just going to try and squeeze every ounce of productivity out of human beings or out of our workforce or just out of the organization, and we’re just going to harvest those gains, and we’re going to put them in our pocket.” And that’s certainly one approach.

But I think what an adaptive organization says is it says, “We live in an environment that constantly changes, and we’re going to continually monitor what’s going on. We’re going to set up the infrastructure so that we can monitor what’s going on. We’re going to have these communication mechanisms so that we can communicate what’s happening, whether it’s internal mechanisms or external factors.”

And we’re set up to operate within the flow of change. And I feel like I’ve been using that phrase lately, this flow of change, and it feels so different to me than change management, which implies that you have a before state, that you have the state of change, and then you have an after state. And I hope I’m starting to paint this picture of optimization as really aligned to productivity and adaptation, which is much more like a complex adaptive ecosystem.

Tom Stoneman:

Optimization asks how much you can squeeze out of a system. Adaptation asks how well the system can survive what comes next. That distinction, simple as it sounds, turns out to change everything about how an organization is built, staffed, and run.

You’ve been listening to part one of my conversation with Melissa M. Reeve, author of Hyperadaptive: Rewiring to Become an AI-native Organization. In part two, Melissa gets specific about how enterprises actually make the transition to become hyperadaptive.

Melissa M. Reeve:

How do you teach people to really look at their processes through this lens of AI? Because what we know for sure is that these processes will have to be reinvented over and over and over again.

Tom Stoneman:

We’ll discuss the stages enterprises need to understand in order to get there and how they can begin to shape markets rather than responding to them. Join us in two weeks for part two with Melissa M. Reeve.

Thank you for listening to The Intelligent Enterprise, a podcast where we get inside big ideas by getting outside of them. I’ve been your host, Tom Stoneman. Please remember to follow the podcast and leave a comment or review wherever you get our shows. See you next time.