If that’s your firm, Hyperadaptive can do double duty. Internally, it gives your consultants and practice leads a shared framework, the five capabilities and five stages, so your point of view is consistent from one engagement to the next. Externally, it’s a credible artifact to put in a client’s hands, a way to align a client leadership team on the map before the real work begins.

I’ve had partner firms use it exactly this way, as a shared reference that travels from their team to their clients’ teams. It turns a good conversation into a repeatable one.

What I Am Offering

The offer applies to firms as much as to in-house teams. Order 10 or more copies of Hyperadaptive by August 30th, and I’ll join a 30-minute live Q&A, whether that’s with your consultants or alongside one of your client teams.

If you’re guiding other companies through this shift, let’s make sure your people are all working from the same blueprint. Send me a note and let’s talk.

hyperadaptive.solutions/book

Not Yet Ready for a Book Club, But Still Need Guidance? Consider becoming a paid subscriber.

Two years later, the same building reopened under a new name, NUMMI, a joint venture between GM and Toyota. Toyota rehired roughly 85 percent of that same workforce. The absentee list, the strike leaders, the people GM had written off as the problem, most of them came back. Within about a year, absenteeism fell to around 2 percent and quality climbed from the worst in GM to the best in GM. Same people. Same machines. Same town. As the Lean Enterprise Institute later put it, the only thing that changed was the system.

I have spent a good part of my career studying that system, going back to my time learning the Toyota Production System in Tokyo. So I’ll admit that I have a soft spot for TPS. But I am sharing this story for a different reason. I believe it has applicability for this AI moment. We have handed our people powerful tools. Many are using them. And, yet…the enterprise value we were promised has largely failed to show up. The Fremont floor answers why.

Everyone is adopting AI, and value still lags.

McKinsey published a global survey in July 2026 that put numbers to AI adoption. Across 750 employees and leaders, 70 percent said they felt personally ready to adopt and use AI. But…only 27 percent of leaders believed their organization was ready to make the shifts needed to actually change around it. On top of that, only 11 percent of organizations had reached what McKinsey calls reinvention (the point where work itself is redesigned rather than merely assisted). The majority of organizations, at every level of maturity, said AI had yet to deliver meaningful business value.

Then McKinsey found that organizational readiness, not personal readiness, explained the difference between the companies capturing value and the ones that weren’t.

Organizational readiness accounted for 48 percent of that difference. Personal readiness accounted for 25. In plain terms, whether your people can use the tools is real, but it is roughly half as important as whether the organization around them has changed to make those tools count.

This is the same gap that sat between old Fremont and NUMMI, without the hostility. GM had capable people the whole time. What it lacked was an organizational system that let capable people produce good work. When McKinsey says most organizations are stuck in adoption without value, they are describing a factory full of talented workers standing at the old workstations, wired to the old line, reporting up the old ladder, now holding a much better tool. The tool is not the constraint. The wiring around it is.

Building a system that supports AI is the hard part.

After NUMMI worked, everyone wanted to know how. GM had a front-row seat, a literal joint venture with its own workers inside a Toyota system, and it still took GM the better part of two decades to absorb what it was seeing. The tools were never the secret. The kanban cards, the andon cord that any worker could pull to stop the line, the standardized work sheets, all of it was visible and copyable. Companies copied them enthusiastically. Many got very little in return, which is exactly the story Womack, Jones, and Roos documented in The Machine That Changed the World in 1990. Western plants adopted the tools of lean and kept the system of mass production underneath, and the tools produced almost nothing on their own.

Think about this as you implement AI. The license to a frontier model is the visible tool, and it is genuinely powerful. But if you install it into an operating model built for a different era, you get activity without value. It’s also the reason I give away the Hyperadaptive Model in the book. The model holds value, for certain. But describing the model isn’t the same as implementing it.

McKinsey found that leaders were 5.3 times more likely to report enterprise value when they had actually redesigned workflows, 32 percent capturing value versus 6 percent when the workflow stayed the same. The redesign is the system. And system redesign is something that a procurement contract can’t solve.

Everything below is about the three things Toyota changed in the NUMMI experiment. I share them because they map almost perfectly onto the three places I see AI transformations stall today.

Trust is Actively & Continually Cultivated

McKinsey found that trust was the single most important readiness factor across all three horizons of maturity. Not in the early stage only. In every stage. Employees needed to believe the organization would support them through the change rather than simply expect them to absorb it. McKinsey advises leaders to communicate honestly, acknowledge what they don’t know, and follow through. All true, and so seemingly simple. And yet, when I ask leaders what their “AI North Star” is (what they hope to accomplish with AI), I am often met with uncertainty.

When NUMMI opened, Toyota made an explicit commitment that it would not lay people off to capture the gains from their own improvements. That single policy is what made the rest of the system function. Think about the andon cord for a moment. You are asking a worker to stop the entire production line when they spot a defect, which draws immediate attention and costs real money by the minute. A worker will only pull that cord if pulling it cannot be used against them. The same person who had sabotaged quality at old Fremont, because quality was management’s problem and management was the adversary, would now stop the line to protect quality, because the system had made a credible promise that improvement would not cost them their livelihood.

Trust was not a sentiment. It was a structural commitment that changed what was rational for a person to do.

Think about this through the lens of AI. Your people can see exactly what is coming. When AI can handle a million service tickets, they know what that means for the level-one role. When AI drafts the first version of the contract, the junior associate knows what that means for the apprenticeship that used to make them senior. You cannot build trust by promising nothing will change, because they will not believe you, and they shouldn’t. Some anxiety in this moment is honest and appropriate.

What you can do is make the NUMMI commitment in your own language. You can say, clearly and then in your actions, that the capacity AI frees up will be redirected and the people freed up will be redeployed, not discarded. Trust of that kind is the precondition for everyone else pulling the cord, which is to say, for everyone else telling you where the real defects and the real opportunities are. Without it, they will keep that knowledge to themselves, exactly as the old Fremont workers did.

AI needs a shared language

The second thing the Toyota Production System provides is a shared language. A Toyota floor runs on standardized work and visual management. Everyone, from the newest hire to the plant manager, could look at the same board and read the same state of the same system in the same terms. When a problem surfaced, they had common words for it, a common picture of where it lived, and a common method for improving it. That shared language is what lets improvement spread horizontally instead of dying inside one supervisor’s head.

Most organizations trying to adopt AI have nothing like it. In the book, Hyperadaptive, I outline a shared set of terms like “AI Activation Hubs, AI Impact Hubs, AI Knowledge Engine,” to standardize language and provide a shared mental model across the organization. Without this shared vocabulary, every function uses AI differently, names things differently, and measures success differently. You get a hundred local experiments and no way to compare, combine, or compound them. This is a large part of why McKinsey finds so much individual productivity that never becomes organizational performance. The wins can’t travel because the organization has no common way to describe them.

Building that shared language is unglamorous and it is foundational, which is why the first stage of the Hyperadaptive Model is about foundations before technology rather than tools first. It means a common vocabulary for what AI is doing in your workflows, a shared way to translate technical possibility into business terms, and enough baseline fluency across the organization that a redesign in one function can be read and reused in another. You are not standardizing to control people. You are standardizing so that a good idea in the claims team can be understood by the underwriting team without a translator.

Toyota Understands There Is No End State

McKinsey’s research outlines three stages, with the last being reinvention. This makes it feel like there is an end state. Toyota puts kaizen, continuous improvement, at the heart of its system. A nod that things will always be changing. NUMMI was never installed and finished. The standardized work sheet was not the final answer, it was the current best answer, posted so that the person doing the job could propose the next improvement to it. The system’s defining feature was that it never stopped changing itself. That is the actual destination, and it is not a rung on a ladder. It is a different operating state, one where sensing and adjusting is the normal condition rather than a project with an end date.

This is why I describe continuous adaptation as one of the core capabilities of a Hyperadaptive organization. It’s why I keep saying that reviews of how you work can’t be quarterly or annual anymore, they have to become continuous, with real-time adjustment replacing periodic inspection. If you treat your AI transformation as a program with a go-live date, you will fail. The pace of the technology will not sit still long enough for a finished state to hold.

What this means for AI Transformation Leaders

If you lead a function or an enterprise and you take one thing from Fremont, let it be the order of operations. GM’s mistake, for twenty years, was to skip the invisible system, assume trust would materialize on its own, and have no game plan for empowering front-line workers to reinvent their work. The same instinct is everywhere in AI right now, and it produces the same result, motion without gain.

So, where do you start? Start by redesigning one real workflow around what AI now makes possible, end to end, rather than sprinkling assistance across everything, because the redesign is where McKinsey’s 5.3 times difference lives. Make your social contract explicit and then honor it in a visible decision, so that people believe the freed-up capacity is going to be redirected and they will be redeployed. Until they believe it they will not tell you the truth about their own work. Invest early in a shared language, a common way to name and measure what AI is doing across functions, so that a win in one place can be read and reused in another. And design the whole thing to keep improving after it ships, because a transformation with a finish line is already out of date. This is the work of Stage 4 in the model, rewiring the organization, and it is worth every bit of the effort that the tool purchase was not.

None of this requires you to have all the authority in the building. It requires you to start where you sit. You become the person who notices that the operating model will not survive what is coming and begins to design what replaces it. You build the structure your organization is missing, you name what is happening out loud in the rooms where it isn’t being named, and you find the others who see it too.

Final Thoughts

NUMMI provides an interesting reference point because it is proof, not theory. It is documented, it is decades old, and it settled the question of whether the people or the system was the issue. The people were never the problem. They were the same people. What changed was a set of choices about trust, language, and continuous improvement that turned a workforce everyone had given up on into the best in the company inside a year.

That is not only a Toyota story. It is the root of a longer one. Serious people have been trying to redesign the operating model for the better part of seventy years, through lean and Six Sigma and business process redesign and Agile, and every one of those efforts did real good inside one part of the organization and then hit the same ceiling, because the industrial-age operating system underneath was strong enough to absorb the improvement and keep running. AI is the first force I have seen that the old system cannot simply absorb, because it changes the work itself, under everyone’s hands, all at once. That is what makes this moment different, and it is why the choice in front of us is real. The system is going to change either way. The question is whether it changes by accident, in whatever direction the next model release happens to push it, or on purpose, in a direction we choose.

The tools in our hands today are far more powerful than an andon cord, which means the value on the other side of the redesign is far larger, and the cost of skipping it is too. The efficiency will follow the rewiring. It always has. Our job is to build the system worth wiring the tools into, and to keep building it, together, all the way to AI-native.

How to Build the Hyperadaptive System

Building the system is what we cover in the Running Hyperadaptive Organizations class, where we bring Hyperadaptive concepts to life. You can find more information at hyperadaptive.solutions/class.

And if you want the full picture of the Hyperadaptive Model, that’s what the book is for. Hyperadaptive: Rewiring the Enterprise to Become AI-Native

Sources & Further Reading

• “How NUMMI Changed Its Culture,” Lean Enterprise Institute

• “How to Change a Culture: Lessons From NUMMI,” MIT Sloan Management Review

• Womack, Jones & Roos, The Machine That Changed the World (1990)

• “From adoption to impact: Three horizons of AI transformation,” McKinsey & Company, July 2026

• Robert Solow, “You can see the computer age everywhere but in the productivity statistics,” and the productivity paradox

Here’s why. When only one leader has the framework, they spend the next three months translating it for everyone else, and a lot gets lost in the translation. When the whole group has read the same pages, they skip straight to the real conversation: which stage are we on, what does the next step look like, and who owns it?

What I Am Offering

That’s the thinking behind the offer I’m making to teams this summer. Order 10 or more copies of Hyperadaptive for your group by August 30th, and I’ll join one of your sessions for a 30-minute live Q&A. You bring the questions your organization is actually wrestling with, and we turn the framework into your first Monday morning together.

If that’s useful for your team, send me a note. I’d love to be in the room.

hyperadaptive.solutions/book

Not Yet Ready for a Book Club, But Still Need Guidance? Consider becoming a paid subscriber.

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The idea was lovely. Once a week, anyone doing something interesting with AI would demo it to the group, trumpet the win, and inspire the next person to go try something of their own. It would fill the pipeline with ideas and let the best ones rise, a little like a startup competition. Everyone was behind it. And it has met exactly once, because on the day they gathered there was only one thing to demo. Now it gets quietly canceled most weeks.

Most organizations I chat intuitively move in the right directions. They start small. They run pilots. They name champions. They fire up slack channels. Then they wait for it to spread on its own. It rarely does.

That’s because there is another layer needed. A layer to catch the win and turns it into the next one. That layer is mostly human, and it is missing from most AI implementations.

First Look for AI Impact, Not Scale

I have a saying that shopping is the easy part. Meaning, it is easy to buy AI licenses… or a platform to track token usage. But shopping for licenses won’t teach a person to use AI, nor will it teach a programmer how to get more out of the model for fewer tokens. No more than buying tile gets you a new shower. You still have the same problem: implementation is the hard part.

Here is the pattern I’d rather see.

Somewhere in that company are two or three people who already have their token usage dialed in, who get excellent results without burning the budget. Find them. Make them your first AI leads. Not champions, leads.

People who lead through influence rather than authority. People who know how to transfer knowledge. People who have a remit to show their peers how they get great results. Now you have something you can count. You started with three efficient users, you activated them to teach, and a month later you can point to the number that moved and the dollars that came back.

That countable result is the seed. It is small on purpose, because a small, measurable win is the only thing that reliably frees up the budget and the patience for the next, larger one.

When your team lands its next win with AI, who is responsible for catching it and getting it in front of the next person? If the answer is ‘no one,’ you have found your gap.

Build the Pockets that Ripple AI Out

Another leader I spoke with last week runs global development inside a professional-services firm of roughly a quarter-million people across a hundred and fifty-plus member firms. She is a systems thinker, and she has arrived, on her own, at something I wish more leaders understood in their bones. She told me you cannot do anything at the global level if you have not built your pockets first, because those pockets go on to activate everyone else.

The spreading is what needs infrastructure. A network of AI Activation Hubs are where the win gets caught. A hub keeps current on what the tools can do, atomizes that into something a busy person can actually use, tracks where AI is creating value, and codifies the good patterns so the automation someone built in one corner can show up in another. Without it, every win stays local, and you are back to demos that stay local.

You Have the People You Need; They Just Have Different Titles

Underneath everything I’ve described, the AI Leads and the Activation Hubs, is a suggestion that your organization needs some roles it does not have yet. That can feel radical, and I understand why. Most leaders I talk with already feel fully staffed. They look at the org chart, see capable people in every seat, and, if anything, feel pressured to shrink headcount. So why would this be the moment to talk about new work and new roles?

Because this is exactly how jobs change. A job is a bundle of tasks and decisions and handoffs, and when a technology this big arrives, that bundle gets taken apart and put back together in a new shape. Some of that new shape is the connective work I keep pointing at, the people who help their peers get results and the hubs that catch what those people learn. It is not more headcount stacked on top of the old structure. It is a few of your existing people picking up genuinely new work, because the old bundle no longer fits.

Which is why you start small. We have watched big-bang transformations disappoint for thirty years, from the ERP rollouts to the digital reinventions that reorganized everything at once and delivered very little. Radical reinvention rarely survives a simple declaration from the top. They have to earn their place in your context, in front of people who can see the result. You start small, you get the win you can count, you show that a lightly-appointed AI Lead and a small hub actually moved something, and then you have the standing to build on it.

So, Where Do You Find Your Meaningful Wins?

Look in three places.

First, find a problem the business already cares about and can measure, the way my engineer has runaway token costs staring him in the face. A win only counts if someone was already keeping score.

Second, find the people quietly getting great results without being told to, because those are sometiems your AI Leads before they have the title, and they are usually easy to spot once you ask.

Third, find the pocket with appetite, the team leaning in rather than the one you would have to drag, so your first win arrives with pull instead of push.

None of this requires a reorganization or a grand announcement. It takes a countable win and some small piece of human infrastructure to catch the win and spur on ,the next one. That is how a change this large earns its right to spread. It is how you get, one proven pocket at a time, to become AI-native.

If this is the kind of thinking you want more of, consider reading the book Hyperadaptive: Rewiring the Enterprise to Become AI-Native or joining a Running Hyeperadaptive Organizations cohort.

Sources & Further Reading

• The Hyperadaptive Model — AI Leads, AI Activation Hubs, and the flywheel

Then I asked what felt like a simple question. Had anyone else in the company picked it up? She thought about it and said she wasn’t sure. As it turned out, another group two floors away had spent the previous quarter solving nearly the same problem from scratch. Neither team knew the other existed. The win was real, but orphaned.

I’ve come to believe this is the most expensive pattern in enterprise AI right now, but it remains hidden. Companies are generating real wins. The wins just have nowhere to go.

We Keep Expecting AI to Install Itself

Here’s the part that blows my mind.

We seem to have decided that AI, the most powerful technology most of us will adopt in our careers, is somehow going to install itself. Spread itself. Get learned on people’s own time, in the margins of their real jobs. That we can reshape the organization while asking for almost no new human infrastructure in return.

It won’t. AI doesn’t install itself.

We actually know this, because we’ve done it before and done it better. Think back to the 1990s, when we put a PC on every desk. We didn’t just hand people a beige box and tell them to go play. We stood up IT departments. We built help desks. We created whole roles whose entire purpose was to help humans absorb a new capability into how they worked. The technology was the easy part. The human infrastructure around it was the actual project.

With AI, a lot of organizations have skipped that step. We’ve done the equivalent of dropping the PCs on the desks and walking away, and then we’re surprised when the value stays trapped in the few teams motivated enough to figure it out alone.

The Layer Nobody Drew on the Org Chart

If you sketch how most AI transformations are actually built, you’ll usually find two layers. At the top there’s strategy, the North Star, the executive sponsor, the budget. At the bottom there are the teams doing the real work, running pilots, finding what actually helps. Both of those layers get a lot of attention.

What’s almost always missing is the layer in between, the one that runs sideways across the organization and connects those teams to each other. Without it, every win is vertical. It goes up to leadership as a nice slide, and it stays down in the team that built it. It never travels across to the six other teams who could use it.

Toyota has a word for the thing that’s missing here. They call it yokoten, which translates roughly as “across everywhere.” It’s the deliberate, horizontal, peer-to-peer spread of an improvement from the team that discovered it to every other team that could use it. And the part I love most is that yokoten doesn’t just copy the result. It copies the thinking that produced the result, because the reasoning is what lets the next team adapt it instead of blindly pasting it in. That distinction is the whole game.

When did your organization last take a genuine AI win from one team and deliberately walk it across to another?

The One Piece of Infrastructure I’d Put In First

So let me let you in on one concrete example of the human infrastructure that has to be built, because this is the piece I’d put in first. It’s what I call an AI Activation Hub.

And the reason it matters so much right now is that individual AI talent has raced way out ahead of organizational capacity to use it. One recent survey found that AI super-users are delivering roughly 5x the productivity of their peers, and yet only 29% of organizations report significant ROI from generative AI.

The talent is already in the building. The results are real and enormous at the individual level. What’s missing is any mechanism to spread them. A separate reading from Microsoft’s 2026 Work Trend Index tells the same story from the other side: only about one in ten workers say AI has actually changed how work gets done, while three times as many executives are convinced they’ve already achieved enterprise-wide impact. The wins are staying local, and the people at the top can’t see it.

An Activation Hub is one answer to that. It doesn’t replace the rest of the support system, and it doesn’t work as a governance gate that every project has to pass through for approval. What it does is act as the connective tissue that lets a good idea compound instead of dying where it was born.

Where This Goes

The shift is this: stop treating AI as a tool you install and start treating it as a capability you have to help the organization absorb. That means building the human infrastructure on purpose, and the coordination layer is the piece almost everyone skips. Get it in place and your wins stop being lucky one-offs and start becoming how the whole company learns. That’s a real part of the road from where you are now to genuinely AI-native.

That coordination layer is exactly what we build together inside the Running Hyperadaptive Organizations class, where we bring Hyperadaptive concepts to life. You can find more information at hyperadaptive.solutions/class.

And if you want the full picture of how the pieces fit, that’s what the book is for. Hyperadaptive: Rewiring the Enterprise to Become AI-Native is available now at hyperadaptive.solutions/book.

Sources & Further Reading

• Yokoten: Capturing and Sharing Best Practices — Lean Enterprise Institute

• Yokoten: knowledge sharing in Toyota — Knoco

• Enterprise AI adoption in 2026: super-user productivity and ROI — Writer

• 2026 Work Trend Index: Agents, human agency, and opportunity — Microsoft

• Rising AI Adoption Spurs Workforce Changes — Gallup

• AI Center of Excellence: Why Most Become Bottlenecks — Agility at Scale

He’s past the first cautious steps with AI. Most people have licenses. But, the organization is splitting in two. His most enthusiastic people are racing ahead while the broad middle of the company stays exactly where it was.

That gap has a name. In the Hyperadaptive model, I call it ‘AI Bifurcation’, but the plain-English version is more useful here, so let me use that. You are becoming a two-speed company. A small group of power users pulls ahead fast, the large middle stalls, and the distance between them widens every month. Most leaders see that as few AI super-stars and some stragglers who will catch up eventually.

I want to make the case that it is not a long tail, is a split, it is predictable, and it is something your system is producing on purpose, whether you meant it to or not.

The numbers around this are getting hard to ignore. IBM’s 2026 research found a 61-point gap between the share of employees who can use AI at work, about 85 percent, and the share who actually do, about 25 percent. So in most organizations, three out of four people have the door open and are still standing in the hallway. Meanwhile the people who did walk through are compounding. By some estimates AI super-users are several times more productive than their peers and meaningfully more likely to get promoted. The gap is not cosmetic. It is turning into a difference in who advances.

Is the AI Split Happening to You?

Before we talk about why this happens, it helps to know whether it is happening to you. Here are four signs you have quietly become a two-speed company.

• The AI wins wear the same faces. Every AI success story in your all-hands traces back to the same three or four names. The demos are real and impressive. They are also a very small sample.

• The AI usage dashboard looks fine until you segment it. Overall adoption looks healthy. Then you split it by team, by level, or by tenure, and it falls off a cliff somewhere below the early adopters.

• The quiet around AI means two different things. Your power users went quiet because they no longer needed help. Your middle went quiet too, but because they have privately decided this is not for them and stopped asking. Same silence, opposite causes.

• The curious are using nights and weekends to learn. Your power users invest in themselves at night, during the weekends, and working late. Those who are falling behind often carry the weight of the organizational burden, have heavy commitments outside of work, and fitting in ‘one-more-thing’ breaks the camel’s back.

If two or three of those are true, you may have a two-speed organization developing.

This Isn’t an AI Motivation Story

Most leaders in my ecosystem reach for a motivation story. The champions are hungry and curious, the middle is comfortable or resistant, and the job is to light a fire under the laggards. There is a sliver of truth in it. People do differ in appetite for new tools, and some genuinely are dragging their feet.

But look back at that fourth sign, because it gives the game away. The people racing ahead most often are investing in themselves. They got access to good tools. They got time (or made time) that was protected enough to actually experiment instead of squeezing it between two deadlines. And they got permission to fumble in public without it counting against them. The middle got a license and a shrug. We told them AI was available and important, and then we left them to figure out the rest on top of a full workload, with no on-ramp and no air cover.

When you frame it that way, the gap stops looking like a character flaw in your people and starts looking like exactly what the system was built to produce: more of the same.

This matters because the diagnosis decides the cure. If the gap is a motivation problem, you respond with pep talks, mandates, and pressure, and you spend a year wondering why the needle will not move. If the gap is a system problem, you can actually fix it, because systems are the thing you have the power to redesign.

Three Ways to Close the Gap, From a Systems Point of View

So here is what I would do, in order. None of this is exotic. The hard part is choosing to build the structure instead of running another campaign.

First, build the on-ramp your power users never needed. Your champions got where they are by following their natural curiosity, so it is tempting to assume everyone can. The middle needs more support than a license and a hand-wave. They need the next steps to be obvious:

• Clear, low-stakes paths with the most relevant use cases for their actual role, taught in the flow of the work rather than in a generic course they will forget in a week.

• Dedicated capacity to learn and experiment with others. The unglamorous truth is that adoption spreads socially, peer to peer, far better than it spreads from the top, so design for that. Make it safe to be a beginner in a room full of beginners.

• Guided experiences that create always-on learning arenas that move people from ambivalent to empowered.

Second, turn your power users into infrastructure, not heroes. Right now your best people have a moat around their knowledge with a drawbridge that lowers only in sharing forums. The move is to capture what they know and route it to the people who need it, so their advantage becomes shared plumbing rather than personal legend. This is exactly the work an AI Leads network and an activation hub are built to do, taking the scattered brilliance of your early adopters and turning it into something the whole organization can draw on. A champion who teaches ten people is worth far more than a champion who simply outperforms them.

Third, manage the gap, not the average. Stop reporting AI adoption as a single company-wide figure, because that number is engineered to make you feel fine. Report the spread. Track the distance between your most and least adopted teams and make closing it the actual goal. What you measure is what you will manage, and a company that measures the average will keep optimizing for the people who were never the problem.

Why the Gap is So Important to Mind

The reason I care about this is that, left alone, the fast group gets faster, the middle disengages further, and what started as an adoption gap becomes a culture gap, then a retention gap, then a two-tier company where half the people believe the future does not include them. You cannot pep-talk your way out of that. You can only build your way out, one on-ramp and one metric at a time.

Going Further

If you want help building those on-ramps and turning your champions into a system rather than a handful of heroes, that is the work we do inside the AI Lead Accelerator and Applied AI Workshops. And if you want the full map of the journey, including the stage this piece is really about, the book Hyperadaptive: Rewiring the Enterprise to Become AI-Native is available now at hyperadaptive.solutions/book.

Someone I have known for the better part of a decade is one of the most interesting people I get to talk to. He is a working musician, the kind who still plays gigs on weekends and hears arrangements in his head while the rest of us hear noise. He also holds a computer science degree. And for the last several years he has made his living as an account executive, carrying a quota, running a pipeline, closing deals.

When he was starting out, he had to choose. Not because he lacked the range to do more than one thing, but because the world he was entering was built to reward one thing at a time. You pick the lane that pays, you get good at it, and the other parts of you become hobbies. He chose sales because sales was the lane with a clear price on it. The music and the code went into the evenings and weekends.

I think about him a lot lately, because the organizational structure that made him choose a lane is the same structure that will buckle under AI.

In a world where the lanes are dissolving, what was the point of the lanes in the first place?

Why We Drew the Lanes at All

To answer that, it helps to go back to where the lanes were first drawn so deliberately that someone wrote them down.

In 1776, Adam Smith opened The Wealth of Nations by walking through a pin factory. He counted ten men. On their own, each one fumbling through the whole job from wire to finished pin, he reckoned a single worker might make one pin a day, and certainly not twenty. Divided into about eighteen distinct operations, one man drawing the wire, another straightening it, a third cutting, a fourth pointing, those same ten men produced 48,000 pins in a day. Specialization did not make the workers smarter. It made the system enormously more productive.

For a long time we told the story as Smith told it, as a story about output. But a generation later, a British mathematician named Charles Babbage noticed something Smith had walked right past. In 1832, in On the Economy of Machinery and Manufactures, Babbage pointed out that dividing the work did not just raise the volume of pins. It changed what you had to pay for them. If one skilled artisan made the whole pin, you paid that artisan’s full wage for every step, including the easy ones a child could do. Break the job apart, and you could buy exactly the amount of skill each step required, and no more. Cheap labor for the simple steps, expensive labor only where the work was genuinely hard.

The division of labor was never only about productivity. It was about the economics of skill. We split jobs into pieces because it let us match the price of the person to the difficulty of the task.

But, here’s the conundrum. On the one hand, it is more economical to ‘keep people in their lanes’ and ‘match the price of the person to the difficulty of the task.’ But, on the other hand, people can almost always do more than their lane allows. The lane is a financial decision dressed up as a job description. Frederick Taylor took that decision and made it a science, timing every motion, separating the people who think from the people who do. By the time the assembly line was humming, lanes defined the architecture.

So the Lanes Got Narrow, Right?

The trouble with an architecture is that it outlives the reason it was built.

We are long past pin factories, and yet most organizations are still drawn as a grid of lanes. Marketing here, legal there, sales down the hall, engineering on another floor entirely. We hire people into a box, we evaluate them on how well they stay in it, and then complain about the ‘lack of innovation’ in our organizations. The account executive who can also read a data model, or sketch the product, or hear the music in a brand, has nowhere on the org chart to put those other selves. There is no column for the intersection.

For over a hundred years, that trade was worth it. The economics held. You genuinely could not afford to let everyone do everything, so you accepted the narrowing as the price of scale. The question becomes, when both AI and humans get adjacent competencies, what happens to the lanes?

And AI Doesn’t Live in a Lane

Watch what AI actually does inside a real team, and you see the lane constraint lifting from two directions at once.

From one direction, it lifts people into competencies that used to take years to earn. The clearest evidence I have seen is a study by Erik Brynjolfsson, Danielle Li, and Lindsey Raymond, who looked at more than 5,000 customer support agents using a generative AI assistant. On average, productivity rose about 14 percent. But the average hides the real story. For the newest, least experienced agents, the gain was 34 percent, while the most experienced agents barely moved. The tool worked as a kind of knowledge equalizer, taking what the best people knew and quietly handing it to everyone else. The novice could suddenly operate in the expert’s lane.

From the other direction, AI moves into adjacent work itself. The account executive does not just get better at selling. He uses AI to build the rough version of the dashboard he used to wait two weeks for, to draft the contract language he would have routed to legal, to mock up the campaign he would have briefed to marketing. We have seen a version of this movie before, with DevOps, which did not erase the work of shipping software so much as it changed who was allowed to do which parts of it. Toyota and Mercedes-Benz have been cross-pollinating factory-floor workers with data science skills for exactly this reason. The lines around who-can-do-what stop being walls and start behaving like membranes.

Which brings me back to Babbage, and to the question I do not think any of us can answer cleanly yet. Babbage’s whole insight was that you divide the work so you can pay less for the easy parts. But AI is now doing the easy parts. So when the cheap, simple steps are absorbed by a machine that costs roughly the same whether it drafts one email or ten thousand, what exactly are we still dividing, and why? Is the goal still to break the work into ever-finer pieces and assign each to the cheapest qualified hands? Or is the goal to stop organizing around tasks at all, and organize around the outcome, the value, the thing the customer actually wanted?

I do not have a tidy answer, and I have made my peace with that. I think the honest position right now is that the answer is in flux, and that the leaders who do best will be the ones comfortable saying so. When did your organization last ask whether a given role needed to be a single lane at all, rather than just asking how to make that lane more efficient?

The most useful reframe I have seen on this comes from Satya Nadella, who has been arguing that every firm now runs on two kinds of capital. There is the ‘human capital’ of its people, their judgment and relationships and pattern recognition, and there is the ‘token capital’ of the AI capability the firm builds and owns. His claim is that human capital does not get less valuable as the token capital grows, it gets more valuable, because without human direction you just have compute running in circles. So the thing that actually matters is not the task and not the model, it is the loop between people and AI that keeps compounding what the firm knows. You can offload a task, or even a whole job, but you can never offload your learning. And a loop that keeps compounding is not a machine you finish building. It is something that grows.

We’ve Been Building Machines, and Machines Age Badly

Faced with all this, a lot of leaders are reaching for a new org chart. Flatten the layers, cut the middle, redraw the boxes around AI, and declare a new operating model. And I want to be fair to that instinct, because some restructuring is genuinely overdue, and 82 percent of executives in IBM’s latest research say their functional silos are actively blocking value.

But redrawing the grid is still grid thinking. The most extreme version of this is the company that cuts half its workforce on a spreadsheet logic of profit-per-employee, the way Block has, treating the people who walked out the door as cost rather than as the living memory of how the place actually works. Even setting aside what I think of that as a way to treat human beings, it is shortsighted on its own terms. You cannot download institutional knowledge back into a building after you have shown it the parking lot.

The deeper problem is that machines, including org-chart machines, are built to do a fixed thing well, and the moment the environment changes they become precisely wrong. We have a hundred years of evidence for this. Arie de Geus, who ran planning at Shell and wrote The Living Company, found that the average corporation dies well before its twentieth birthday, even though there is no natural reason a company could not live for two or three centuries. The tenure of companies in the S&P 500 has fallen from thirty-five years in the late 1970s toward fifteen. The machines we build to be efficient keep dying young, because efficiency and adaptability are not the same thing, and we kept designing for the first one.

Émile Durkheim saw the better half of this back in 1893. He described societies moving from what he called mechanical solidarity, where people cohere because they are all the same and interchangeable, to organic solidarity, where they cohere precisely because they are different and depend on one another. He reached, deliberately, for the language of a living body, parts that are unlike each other but bound together. It was the right metaphor. We just stopped at the organ and never got to the organism that grows.

Better to Grow Like a Forest

Walk into a forest and you are looking at the most successful organizational structure on the planet, one that has run continuously for hundreds of millions of years. A tree does not draft a new operating model every spring. It sheds the leaves that have stopped working, holds the structure that still serves, and grows into the light that is actually available this year. It renews itself without ever ceasing to be itself.

That is not a poetic flourish, it is a body of science. John Holland and his colleagues at the Santa Fe Institute spent decades studying what they called complex adaptive systems, the family that includes ecosystems, immune systems, cities, and markets. What those systems share is instructive for anyone running a company right now. Their boundaries are semi-permeable rather than rigid, more like the edge of a niche than a wall. They are made of many agents adapting locally. And their order is not designed once from the top and frozen, it emerges and re-emerges as conditions change. They are built to keep becoming, not to keep still.

So the goal, I have come to believe, is not to arrive at a new operating model and plant a flag. A new fixed structure is just tomorrow’s legacy system. The goal is to build an organization that can keep re-forming its own boundaries, the way a forest keeps re-sorting which species thrive in which clearing. The structure becomes a verb. You are not designing the final shape. You are designing the capacity to keep finding the next shape.

This is also where Nadella’s framing gets concrete and practical. He describes the real test of a firm’s sovereignty in this era as the ability to swap out a generalist model the way you would replace a worn part, without losing the ‘company veteran’ expertise your people and your systems have accumulated. The tree drops the leaf and keeps being the tree. The learning stays even as the components turn over. That is what separates a structure that is alive from one that is merely assembled, and it is why the firms that start building that compounding loop early will be very hard for a swapped-in competitor to catch.

If that sounds abstract, notice that it is exactly what my friend the musician does instinctively, and what AI now lets a whole organization do. Given tools that hand him adjacent competencies, he stops being an account executive who happens to play music and code on the side, and starts being a person who brings all of himself to whichever problem is in front of him this quarter. The lane dissolves into a living set of capabilities he can recombine. That is the real change AI makes possible, scaled from one person to ten thousand.

So Where Does a Leader Actually Start?

The honest catch is that you cannot become a living system by reorganizing into one on a Tuesday. That would be the machine move again, one more big redesign imposed from the top. Renewal is grown, not installed.

So I would start small and on purpose. Pick one seam in your organization where the wall between two functions is clearly costing you, the place where work bounces back and forth and value leaks out at the handoff, and make that boundary deliberately permeable for ninety days. Give a few people the tools and the explicit permission to work across it. Watch what they make at the intersection. Then do it again somewhere else. This is the logic behind the five-stage Hyperadaptive model I wrote about in the book, where the stages themselves are becoming well understood and the real secret is in the support structures that let change spread and stick. Slow down to speed up. You are not trying to reach the destination this quarter. You are trying to build the thing that can keep reaching new destinations on its own.

Because that, in the end, is what I think AI-native actually means. Not a company that has installed the most tools, and not a company that has cut the most heads to flatter a ratio. A company that has gotten good at renewing itself, that treats its own structure as something living and tends it accordingly. The pin factory taught us to divide a person into the cheapest useful pieces. The forest can teach us how to let people, and the organizations they make, grow back whole. I think the companies that learn the second lesson are the ones still standing in thirty years, and I would very much like yours to be one of them.

Taking the Next Step

If you are leading AI initiatives and wanting to explore how to become AI-Native in a structured way, consider attending the Running Hyperadaptive Organizations class.

And if you want the full map of the journey, the book Hyperadaptive: Rewiring the Enterprise to Become AI-Native is now out.

There is AI activity everywhere…a pilot in marketing…a clever workflow someone in finance built over a weekend…a team using Copilot that swears it has changed their week. Real energy, real effort, real wins. And yet, when we explore the impact all of this has on the business, they smile knowingly, and the call goes quiet for a second.

This is the thing I named Random Acts of AI in the book, and was highlighted in Fast Company this week. It represents AI that isn’t tied to strategy, and therefore…isn’t tied to results. We’re at the point where we’re trying to turn a hundred scattered acts of AI to coordinated activity that produces something the business can feel.

Why Is it So Hard to Scale AI?

Here’s how it usually goes (at least according to the Hyperadaptive Journey Map). A company gets excited, hands out licenses, has a few solid wins. The early numbers look good. Then they try to connect the work across functions, and the progress stalls. The marketing team is moving fast, legal is still on its heels, and the handoff between them is exactly as slow as it was a year ago. Ethan Mollick calls this the jagged edge, and it’s the natural result of moving one part of a system while the rest stays put.

I spent a lot of years in the transformation world before this one. The Toyota Production System, Agile, DevOps. Every single one taught the same lesson, which is that progress stalls when you move one dimension and leave the others behind. AI is no different, except the window to figure it out is a lot shorter. With digital transformation, companies had roughly a decade. With AI, it will be a few short years.

So when a leader tells me their AI adoption has plateaued, I’ve learned that they most likely have a coordination problem. The wins fail to scale because nothing connects them to each other or to a goal worth reaching.

Random Acts of AI is a Coordination Problem, Not a Tool Problem

Look at how the companies that are actually getting results set things up. When Schneider Electric wanted to take administrative drudgery off its global sales teams, they didn’t just push Microsoft Copilot for Sales out the door and hope. Audrey Hazak, their SVP of Digital Customer Relationship Management, deployed it on top of an AI hub and a governance framework that were already in place. Because the scaffolding existed, the same tool that would have been a random act somewhere else became a targeted, scalable solution. The pilot grew to five thousand users, and her team called it a game changer, not because the software was magic but because it solved a real problem inside a system built to spread the win.

That’s the part most efforts skip. We treat AI as a tool initiative, so we get tool results: isolated, local, and fragile the moment anyone tries to coordinate across functions.

It’s like handing everyone a drill and telling people to go make some holes. That isn’t going to get the house built.

The work that compounds sits underneath the tool, in the things almost nobody touches. Three of them come up again and again:

• Strategic Direction. If your organization lacks an AI-north star - the goals you are trying to achieve with AI - you won’t know how to organize around it.

• Incentives. If your reward systems still pay people for being right instead of for learning fast, the experimentation you need will quietly dry up, because nobody wants to be the one whose pilot didn’t pan out.

• Decision rights. AI collapses hierarchies. A junior analyst with the right model can now make a call that used to need three layers of sign-off, and if you haven’t rewired who decides what, you leave most of the speed on the table.

• How you organize. Most companies are still arranged around the work as it existed twenty or forty years ago. Coordinated AI means organizing around the work as it exists now.

When did your organization last look at its incentives and ask whether they reward learning or just reward being right? That one question tends to explain a lot about why AI isn’t scaling.

How Do You Coordinate AI Across Teams and Functions?

The good news is you don’t have to move everything at once, and you shouldn’t try. The point isn’t a big-bang reorganization. It’s that the dimensions have to move together as a system, even if they move a few at a time. You pick what’s ready, you move it in concert with the pieces it touches, and you keep the connective tissue intact as you go.

The companies doing this build mechanisms, not just enthusiasm. They stand up small cross-functional pods that run experiments and capture what works, then carry that learning to the front lines so the next team doesn’t start from zero. Motor Oil Group runs something they call an AI Garage, where their champions do weekly show-and-tell sessions and hands-on training, so a discovery in one corner of the company actually reaches the rest of it. That’s the difference between a hundred people each learning the same lesson in isolation and an organization that learns once and spreads it everywhere. A North Star gets you pointed in one direction, but it’s the connective tissue that lets the value compound.

Where This Goes

The organizations that win the next few years won’t be the ones with the most pilots or the flashiest tool. They’ll be the ones that turn scattered effort into a system that learns and coordinates faster than the technology underneath it changes. That’s the whole move, from random acts of AI to coordinated activity that actually shows up in the results. It’s slower to start and it’s far more durable, and it’s the work I’d put my money on every time.

If you want help building that connective tissue inside your own organization, that’s exactly what we do in the AI Lead Accelerator: hyperadaptive.solutions/accelerate.

And if you’d like the full five-stage path, Hyperadaptive: Rewiring the Enterprise to Become AI-Native is out now at hyperadaptive.solutions/book.

For the past year, they had been working with two levers. The first lever was the carrot. More access, more training, internal demos, recognition for the early adopters. These efforts had given them power users, but left everyone else in the dust. The second lever was the stick. Adoption metrics in performance reviews, manager accountability, a clear ‘use AI or it shows up in your rating’ signal sometime in Q1.

Like many leadership teams I sit with, they didn’t see a way forward beyond this approach. They were tired of the gap between what they knew was possible and what their people were actually doing. This exhaustion is real across every industry right now. It’s the lived experience of a year and a half of brute-force AI adoption, and it’s wearing good leaders out.

Is this really an AI motivation problem?

The brute-force frame goes like this. AI is the future. Competitors are moving. We’ve bought the tools, we’ve offered the training, we’ve put it in the strategy. If our people aren’t using AI, the problem must be our people, which means their motivation, their willingness, their resistance to change. So we reach for the levers that work on people, which are the carrot and the stick.

This is the same diagnosis Microsoft, Meta, and Nvidia are running, with bigger budgets and louder mandates. Microsoft put AI use directly into reviews. Meta is grading employees on ‘AI-driven impact’ starting this year. Nvidia’s Jensen Huang said publicly that an engineer earning $500K who isn’t spending $250K on AI tokens is a warning sign.

And then there’s Duolingo. CEO Luis von Ahn went ‘AI-first’ in April 2025 and tied AI use to performance reviews. A year later, he reversed it. Employees had started asking whether they were being asked to use AI for AI’s sake. His new framing, on a podcast a few weeks ago, was that the most important thing in performance is doing the job as well as possible. If AI helps, use it. If it doesn’t, he’s not going to force the issue. That reversal is the tell.

Duolingo had usage. What it didn’t have was value, and the gap between the two is exactly where the mandate cracked.

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What Deming Saw Coming in 1986

The deepest version of this argument is forty years old. W. Edwards Deming wrote it in Out of the Crisis in 1986. His diagnosis was that the system people work in, and the interactions inside that system, account for something like 90 to 95 percent of performance.

Re-read that: the system people work in accounts for 90-95 percent of performance.

The individual contribution sits inside that. Which means when you try to motivate your way past a system problem, you spend a lot of energy and get almost nothing back.

Deming was unsparing about performance reviews tied to numerical goals. He called the idea of a merit rating ‘alluring,’ and then named exactly how it fails: ‘pay for what you get; get what you pay for; motivate people to do their best, for their own good. The effect is exactly the opposite of what the words promise.’ That was 1986. He was talking about manufacturing quotas. He could just as easily have been describing a 2026 AI usage dashboard.

His most-quoted line is “people work in the system, and management creates the system.” Which means management owns the system. The brute-force approach to AI treats adoption as a people problem, which is the one place a leadership team has the least direct control. The system is where they have the most.

When was the last time your AI strategy meeting focused on workflow design instead of adoption rates?

So What Does Management Actually Own?

A lot, as it turns out, and almost none of it is on the carrot-stick continuum.

Management owns the four dials I write about in Hyperadaptive, which are people, purpose, process, and resources. The mistake is to turn only one. Introducing new incentives without changing the workflow. New training without changes to capacity allocation. A new AI Lead without changes to decision rights. The dials are interconnected, and turning one and asking why the system didn’t move is the modern equivalent of asking why people aren’t being motivated enough.

Deliberate, judgment-led adoption looks different. It looks like Richard Thaler and Cass Sunstein’s idea of choice architecture, applied to AI. You arrange the context so the AI-integrated path is the path of least resistance:

• AI tools sit as the default view on the intranet, not buried three menus deep.

• AI usage shows up first in team meetings, not as the last bullet.

• Manual workflows still exist for cases that need them, and they’re no longer the default.

You aren’t telling anyone what to do. You’re designing the environment so that the right thing is also the easy thing. You are designing the infrastructure so it supports ongoing AI usage.

That’s the move, and it’s the move that takes judgment. Every workflow is different. The choice architecture for a finance team isn’t the same as the choice architecture for a customer success team. You can’t mandate your way to that level of specificity. You can only design your way to it, one workflow at a time, with the people who actually do the work in the room.

The leadership team I sat with picked up on this almost immediately. They had been spending energy debating which lever would work better. They left the conversation asking what support structures encourage AI usage and workflow reinvention at every turn?

The brute-force version of AI adoption asks people to push through a system that wasn’t designed for the work it’s now asking them to do. The deliberate version starts at the other end and asks what the system needs to look like so the pushing isn’t required in the first place. One of these makes management’s job harder in the short term, because designing systems is harder than telling people to do things. The other makes management’s job impossible in the long term, because no amount of mandate produces adoption that compounds.

People work in the system. Management owns the system. In 2026, management is must design a system to support AI adoption.

The Hyperadaptive Model is one such system.

If you’re leading an AI transformation and finding yourself stuck on the carrot-versus-stick debate, this is the kind of system-design work we do inside the Running Hyperadaptive Orgnizations class. AI Lead Accelerator. Cohorts are forming for next quarter at hyperadaptive.solutions/class.

Hyperadaptive: Rewiring the Enterprise to Become AI-Native is out now from IT Revolution Press, with more on the four dials, choice architecture, and what management actually owns. Find it at hyperadaptive.solutions/book.

While it feels like a good thing, hiring a someone like this worries me, but not for the reason you might think. Hiring a CAIO is a great call. What worries me is organizations who offload the shared responsibility of AI, thinking AI is something you can hand to one person. That once you make this hire, the rest of the organizations can go back to their day jobs.

A Name on the Org Chart Feels Good, Right?

IBM’s May study of 2,000 CEOs found that 76% of organizations now have a Chief AI Officer, up from 26% a year earlier. When a number jumps that fast, it feels like a bandwagon, rather than a well-thought out strategy.

A few weeks ago I wrote about the ownership vacuum, the reality that nobody owns AI transformation in most organizations, and how that ambiguity stalls out progress. So a Chief AI Officer looks like the clean answer to that vacuum, right? One name, one person accountable, the ambiguity finally resolved.

It could be true. And for some orgs, it will be true. For others, there is an assumption hiding inside the title that says becoming AI-native is one person’s job. It’s a destination, something you arrive at, hand off, and check off the list. To “become AI-native.” Unfortunately, becoming AI-native is nothing like that. The tools change every few weeks, the work keeps reinventing itself, and you can’t hand a moving target to a single office and walk away.

CHECK OUT: Who Owns AI Transformation?

Here’s the Thing. Everyone Needs to Own AI.

The opposite of handing AI off to one person is shared responsibility. More difficult, yes. What is needed? Also yes. In the Hyperadaptive Model, the shift gets owned by the whole organization, because with something that moves so quickly and affects everyone all the time, it is the only place that makes sense.

The frontline owns their own processes and keeps reinventing them, because they’re closest to where the work is really changing. Your AI Leads help them turn the latest thinking into practice instead of leaving every team to wing it alone. Your AI Activation Hubs take a good idea from the one corner that discovered it and move it sideways to everyone else, fast. And underneath all of that, you need governance that flexes, a few living layers of it, rather than one gate sitting at the top.

All of that is wiring, new pathways for how decisions, learning, and reinvention actually move through a company. And appointing a Chief AI Officer doesn’t install that wiring. It puts a name on the problem and hopes the wiring shows up on its own.

Here’s a question worth pondering. When someone on your team discovers a genuinely better way to use AI this month, what’s the defined path for that to reach everyone else? If you can’t trace it, the wiring isn’t there yet, no matter whose name is on the door.

And…AI Doesn’t Live in a Lane

There something else a title quietly does, and it’s sneakier. It drags our old silo thinking onto something refuses to behave like a silo.

Look at how we’re built. IT, finance, HR, operations, each one tidy in its own lane. So when AI shows up, we do the only thing we know how to do: we build it a lane and assign it a Chief AI Officer. The problem is that AI doesn’t sit in a lane. It’s pervasive, showing up in every role, every process, every decision in the building.

We’ve watched a technology this pervasive arrive before. When electricity replaced the steam engine on the factory floor, the productivity gains didn’t come from dropping a motor where the old engine used to sit. They came years later, once factories rewired their entire layout around distributed power. Nobody appointed a Chief Electricity Officer, because the technology was far too pervasive to belong to one office. AI is the same kind of shift.

The minute you give something that pervasive a single box, you create friction, because everyone starts trying to file the box. Is this an IT thing? Who reports to this person? What’s theirs now, and what’s still mine? I’ve watched teams burn months on exactly those questions and call it progress. It’s the old org chart fighting the new reality, and the org chart usually wins.

So, Should You Hire One?

Unequivocally, yes. A great one can be exactly the person who owns installing all of that wiring: the hubs, the enablement, the design of those governance layers. That’s real, senior, genuinely hard work, and somebody needs to own it.

It comes with two conditions, though.

First, be relentlessly clear, out loud and often, that this person is not a new silo. Their job is to wire the whole organization to own the shift. The day they quietly become the place AI ‘lives,’ you’ve rebuilt the exact problem you were trying to solve.

Second, even with the best person in that seat, the coordination still has to run across every function, because wiring only works when the current flows through all of it.

Paul Hudson, Sanofi’s CEO, has said he won’t delegate this to the technologists, because handing it off even to a brilliant chief digital officer would leave him, in his words, “instantly obsolete.” That line has stuck with me, because it names the real boundary. You can hire someone to help build the thing, but the reason your company actually changes has to be the leadership team itself.

What I’d Leave You With

Bring in someone (or a group of someones) to help lay the wiring. Be aware that no individual or group can become a different kind of organization on your behalf. That part belongs to everyone, frontline to boardroom, or it doesn’t happen at all. Shared accountability, guided toward common goals, continually refreshing itself, is how we get from here to AI-native.

Where to Learn More

If you’re the one carrying this mandate and you’d rather not work out the wiring alone, that’s the entire reason I built the Running Hyperadaptive Organizations cohort. It’s a small working room where we take the model and pressure-test it against your actual organization, alongside peers sitting in the same seat you are. The current round closes enrollment June 11. hyperadaptive.solutions/class.

What is consistent across all of them is the situation.

They have been given the AI mandate. They have not been given the system to run it.

They have a board that wants progress. They have peers in other companies doing some version of this same job, but none of them sit at your altitude in your specific organization. They have a stack of frameworks, vendor decks, and McKinsey articles. None of those tells them success patterns.

I wrote Hyperadaptive to put the operating model in your hands. The book is the foundation. But a book can only do so much. Frameworks live differently in the messy reality of your specific organization than they do on a printed page.

So I built the cohort.

Running Hyperadaptive Organizations is the working room where the operating model meets your actual environment. Registration is open for enrollment now and closes Friday, June 11.

By the end of session two, you will have gained:

1. An understanding of enterprise AI-adoption journeys, drawn from real conversations with peers in their real-world language. Not an AI-generated diagnostic. You will see clearly where you are on the path: where you are ahead, and where you are lagging.
   

2. The power moves specific to your stage of the journey, plus any power moves you have missed along the way. Clear ways to focus your organization’s energy on the highest-value work, with the metrics to know whether those moves are actually working.
   

3. How to start operationalizing the Hyperadaptive Model in your organization, including how to programmatically support your AI leads, how to build a network of AI activation hubs, and how to exchange notes with peers on what is working on the front lines.

These are concrete learnings you can start using immediately.

The cohort is where theory meets reality for you and your peers. We take the concepts from the book and pressure-test them against the messiness of organizational structures and your unique environment. You hear from peers who are working through the same questions, and you see how they are tackling them. The cohort is small on purpose.

What I see as the comparison set:

• A paid executive roundtable: $5,000 for 12 months, two hours a month. Structured peers, but a real commitment, and you wait a month between conversations.

• Free discussion boards: $0. You can ask anything. The advice is sketchy, and you cannot ask the detailed private questions about your specific situation.

• The book: $28. Great content. But how do you navigate the messy reality of your own organization from a book alone?

For $797 you get six hours of structured working time with peers in the same seat, plus office hours, plus the three learnings above. In eight days. Applied to your situation.

Cohort details:

• Two live working sessions, three hours each, one week apart

• Thursday June 18 and Thursday June 25, 9:00 AM to 12:00 PM Mountain

• 15 seats. Capped on purpose.

• Founding rate: $797 (Cohort 2 prices to $1,497)

• Enrollment closes Friday, June 11

The guarantee: If you finish session one and you do not have a clear read on where you are on the journey and the start of your power moves, you can leave and your money is refunded in full. No forms. No surveys. No questions.

If you have been carrying the AI mandate alone, this is the working room I built for you.

Register at → hyperadaptive.solutions/class

Not quite ready to join?

If you aren’t ready for the class, no worries. Read the book. Join the paid community to access peer roundtables. Or just hang out here. I’ll keep writing and sharing with you.

“Honestly, I’ve been running my prep work through Claude for the last six months. I just haven’t told anyone.”

She wasn’t bragging. She wasn’t asking permission. She was confessing something that should have been surfaced to her organization, but she chose to keep it quiet.

I have been hearing a version of this conversation since I started working with AI. While shadow AI usage isn’t as pervasive as it once was, this person still described their workflow in lowered tones, the way you might mention you’ve been secretly feeding steak to the dog. As if it might count against them.

The AI split underneath the obvious one

I have written about the bifurcation problem, where there is a split between power users and everyone else. To solve this, I share why naming AI Leads isn’t enough. This obvious split is the one most leaders can describe in their sleep. Twenty people in the organization are doing all the interesting AI work. Two hundred are still asking someone else to do it for them. We name AI Leads. We fund training. We hope the gap closes.

But there is another divide underneath the visible one. According to the Awareways Trend Report 2025, 48.8% of employees who use AI at work hide that usage from their employers. Their stated reason is fear of judgment. Almost half. Hiding.

That number shines a light on why it’s so hard to measure AI impact. The split is not only between power users and everyone else. The split is between the people who can use AI out loud and the people who use it in private. The first group gets named. The second group gets missed.

Which means most of what we measure when we measure AI adoption is wrong. The dashboards in the AI program office count the visible users. The actual user base coud be roughly twice that size, and the second half is invisible by design.

Why people hide their AI usage

If you have not had this conversation directly, you might assume people hide their AI use because they are unsure whether it is allowed. That is part of it. It is also the safest version of the answer, and it isn’t the full one.

The deeper reasons have nothing to do with the tools.

People hide because their manager has not named AI as something the team is supposed to use. The WRITER 2026 AI Adoption Survey is sharp on this. Only 35% of employees say their manager is an AI champion. And 80% of Gen Z employees in the same survey say they trust AI more than their manager for work tasks. (Sit with that one for a second.) When the person you report to has not opened ChatGPT, you do not bring it up. You quietly use it and ship faster work without explanation.

People hide because they fear judgement. AI amplifies skills - it can make a deliverable look more polished. But those same individuals have watched what happens when someone admits they used AI for a deliverable. The work gets discounted. The question shifts from ‘is this good’ to ‘is this real.’ Once you have seen that happen, you stop signing up to share what’s behind your good work..

So, I ask: If half of your team was using AI privately and never told you, what signal would even reach you? What metric do you have in place that would catch this? And..most importantly, does it matter? If so, why or why not?

Most organizations I talk with measure AI use through licenses purchased, official tools rolled out, training hours logged. None of those measurements pick up the person doing real work in a personal ChatGPT tab on the side.

What the hiding AI usage costs organizations

This is where it gets interesting and potentially, a little sad.

The ActivTrak 2026 State of the Workplace report names a pattern that we should be talking about more often. Burnout risk in 2025 fell 22%, to just 5% of employees. Disengagement risk, in the same period, rose 21% to nearly one in four. Their framing was precise. ‘These aren’t employees who are checked out. They’re employees whose capacity isn’t being used.’

I want you to hold that next to the hiding number.

If half your workforce is using AI privately to get their existing work done faster, and the time savings go invisible because they cannot acknowledge how they got there, that capacity has nowhere to go. It does not get reinvested into the next big thing. It does not show up as a contribution in their next one-on-one. It sits there, quietly, and over months it becomes disengagement.

We were told AI would free people up to do their most meaningful work. For a meaningful share of employees, what it actually freed up was time they cannot tell anyone they have. And capacity you cannot name is capacity you cannot use.

The standard response to the divide problem has been to train harder, more, run another lunch and learn. None of that addresses the actual diagnosis. You can train every employee on every tool, and if your culture rewards visible non-AI work and inadvertently punishes visible AI work, you have half your workforce hiding and won’t know what is going on.

The cultural work around AI is much harder and slower than learning to prompt. It looks like managers modeling AI use in front of their teams. It looks like sharing hours where people can showcase their wins and their failures with AI. It looks like designing the AI Activation Hubs to create safe surfaces for everyone’s usage. These are not technology decisions. They are leadership decisions.

Final Thoughts

When the head of operations told me she had been using Claude in secret for six months, she inadvertently revealed the culture in her organization. The interesting question to ponder is what her organization did over those six months that made hiding feel like the safer move.

Has someone in your organization told you something they had been doing with AI for months that you did not know about? Do you disclose your AI usage? Why or why not?

I would love to hear from you in the comments.

P.S. A Limited Opportunity to Address This

If you are leading AI transformation and want a working room to figure out how to make AI use visible and valued in your specific organization, we have 15 spots to learn from each other and the author. Running Hyperadaptive Organizations. brings the concepts of Hyperadaptive to life, providing a learning arena to work through your hardest issues. The cohort runs June 18 and June 25. Founding rate $797 through June 11. hyperadaptive.solutions/class

Based on the book Hyperadaptive: Rewiring the Enterprise to Become AI-Native (IT Revolution Press, May 2026) is available at hyperadaptive.solutions/book.

What happened on Last Week

Reinforcing Graeme’s sentiment are two announcements that were made last week. Two of the most important AI companies in the world made the same announcement on the same day. Anthropic launched a $1.5 billion joint venture with Blackstone, Hellman & Friedman, and Goldman Sachs to embed Claude engineers directly inside mid-size businesses. OpenAI launched its own $4 billion deployment venture with TPG, Brookfield, Advent, and Bain Capital. Five and a half billion dollars in a single day, both aimed at the same problem: integrating AI into organizations.

The frontier labs have spent the last three years building capability and they have it. The problem is the friction that happens when the capability meets organizational inertial. Both ventures, in their own language, are saying the challenge has moved from building frontier models to absorbing it.

Anthropic’s venture exists to address the scarcity of engineers who can implement frontier AI systems at speed. That’s the technical cut at the absorption problem, and it’s a real cut. It is also the smaller half of the work. Engineers can install the model. They cannot install the operating system that lets the model do useful work. Decision rights, funding flows, performance metrics, role definitions, learning loops, the actual cadence of work, all of those have to evolve too. That harder layer is what enterprise leaders have been quietly grappling with while the AI press argued about benchmarks.

And it’s why I wrote Hyperadaptive: Rewiring the Enterprise to Become AI-Native. I saw early that AI was not just another technology change. It would be bigger, faster, and more impactful that what came before it. Just like what came before it, however, it would be the humans that needed to change as much as the technology. I extended the research, layered on new insights, and built the Hyperadaptive Model to serve as a guide.

Why now, and why a framework

Most of the leaders I work with feel the absorption problem viscerally. They’ve watched productivity gains in one team fail to add up to enterprise transformation. They’ve noticed that adoption metrics keep rising while strategic outcomes don’t. They have intuitions and they have pieces of solutions. What they don’t have is the sequence. Should we start with governance, talent, processes, the funding model, readiness, or all of it at once?

The Hyperadaptive Model works across nine dimensions to bring it all together. And it is a roadmap. It doesn’t solve every detail. Anyone claiming to do so isn’t realistic. The model serves as a directional light. It tells you which way the work goes and roughly in what order, so you can stop wondering whether you should be doing something different and start improving how you do the next right thing.

The book doesn’t tell you whether to roll out Copilot before you redesign your performance review process (the answer is: probably yes). It will tell you that Stage 1 foundations are real and that skipping it is what creates the chaos at Stage 3. It will tell you that the funding model must evolve before the operating model can. It will tell you what good looks like at each stage, so you can stop guessing whether your team is ahead or behind.

The convergence

A book and a moment rarely show up together. When they do, it is mostly accident. I would be foolish to claim I planned this, and, yet, I would be more foolish to ignore what the week kept saying. The frontier labs put $5.5 billion behind exactly what the book addresses. And what readers keep telling me, in different words, is that they already sensed all of it. They were waiting for the sequence.

If this is showing up in your org and you want to compare notes, hit reply. I read everything that comes in, and these notes shape the work I do next.

If you want a place to discuss your journey to becoming Hyperadaptive, consider joining our paid substack community. We meet every month to discuss, in real-time, what is happening inside real organizations.

And if you are looking for a roadmap, the book is here! Order today at hyperadaptive.solutions/book.

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The People Leading AI Transformation Are Often ‘Voluntold’

About 40% of large enterprises have a Chief AI Officer or are actively hiring one. That number gets quoted a lot. The piece nobody mentions is what’s happening in the other 60%, plus inside the divisions of the 40% that do have a CAIO.

That’s where most of the work is being done. By VPs of Operations, Heads of Innovation, Directors of Transformation, line-of-business leaders who happened to be the AI-curious one in the room when the CEO needed to point at someone. Birju Shah, who ran AI at Uber and now teaches at Kellogg, makes the case bluntly: most Fortune 500s don’t actually need a CAIO. They need to retrain the executives they already have. I think he’s right, and I’d add this:

Most leaders are doing the work of AI transformation without a playbook, a team, a budget line, or the recognition. They are running the transformation while people still argue if this should be a role.

When did your organization last clearly define who owns AI transformation, versus who was politely asked to lead it?

The Trap Is Structural, and the Numbers Tell You So

Here’s what people in this position are walking into. Executives expect AI to deliver something on the order of 32% productivity improvement and 26% faster revenue growth. Actual revenue per employee, across the same enterprises pouring money into AI, is up about 1%. The HFS analysts who published that gap recently argued that if your CAIO is still relevant in three years, something has fundamentally failed.

Whatever you think of that conclusion, the gap itself is the part that matters. A 30-point spread between what leadership expects and what the underlying business is producing is a structural problem that any newly tapped AI leader inherits the moment they say yes.

The Bernard Marr case from last year captures the pattern. A Fortune 500 hired its first CAIO with significant fanfare. Eighteen months later they reposted the role. The stated cause was a $50M cost-savings target inside the first year. The actual cause was that the target was never realistic, but it was the basis on which the role had been justified to the board.

Additional Reading: WHO OWNS AI TRANSFORMATION?

I see this pattern most often in people who don’t have the title. They were given the mandate informally, often as an ‘and also’ on top of their existing P&L. They inherit the same expectations gap as a CAIO would, with less authority, smaller teams, and no protected runway.

If This is You, The First Job Is Renegotiating the Mandate

The leaders I see surviving this role 18 months later, across a wide range of titles, did not start by auditing stakeholders, aligning with strategy, or building a quick-wins portfolio. They did those things. But they did them after a different first move.

They renegotiated the mandate.

In practical terms, that meant going back to the executive who handed them the responsibility and getting three things on paper before doing the work:

• A clearly named scope. Which functions, which P&Ls, which decisions are inside this mandate, and which are out. Most informal AI mandates begin as ‘go figure it out,’ and that breadth is precisely what makes them fail.

• A success definition that isn’t a dollar figure pulled from a board deck. The Marr case is instructive. If the target is “$50M in savings inside 12 months” and that number was generated by someone other than the people who would deliver it, the right first move is to put a different number on paper.

• An authority statement. Not a budget. An explicit acknowledgment of which decisions the AI lead can make without escalating, and which require the named executive’s sponsorship. This is the piece informal mandates almost always skip.

Additional Reading: Your Org Structure Doesn’t Like AI

The leaders who get this kind of clarity set themselves up for success. The ones who skip it, even excellent ones, set themselves up for ambiguity at best, failure at worst.

Final Thoughts

Many people I chat with are tolerant of the role ambiguity because it feels like being on the front line. It feels like a failure now will turn into a future success.

And it may.

But why not set yourself up for success from the get-go? (This applies even more sharply when you don’t have the title, because the role wasn’t designed at all. It accumulated.)

If you’ve been handed an AI transformation in the last six months, formally or informally, what was your situation? Were you handed a title, a wish, or a mandate?

Here’s the Good News

If you think that you need to do this without a playbook, I have good news! My upcoming book Hyperadaptive provides a research-backed blueprint across nine dimensions.

Three ways to start before you decide whether to buy.

• Listen to a sample of the audiobook → Audiobook sample

• Read the opening chapter as a free excerpt → Read the excerpt

• Or, if you’re already in: get the book plus the launch-week Reader Bundle, which includes early access to frameworks, the AI Integration Handbook, the Hyperadaptive Model PDF, and 90 day access to the paid Hyperadaptive Intelligence community.

→ Get the book + Reader Bundle

Hyperadaptive: Rewiring the Enterprise to Become AI-Native releases May 12th.

Copilot is mandatory at his org. Use it, or your performance review notices. Usage is being measured by tokens, so… A handful of engineers have set up token-consuming factories, idle prompts firing all day, climbing the internal leaderboard. (Because there is a leaderboard now).

The enterprise metrics platform is producing beautiful dashboards. But nobody trusts the dashboards. The dashboards only measure Copilot, and half the real AI work is happening in Claude and Codex. The CTO keeps asking, ‘How do we measure this?’ The board keeps asking, ‘How do we measure this?’ He is trying to answer, ‘How do we measure this?’

And the real question, the one nobody is asking yet, is: what is ‘this,’ actually?

See If This Feels Familiar 👇

If you lead AI transformation at your company, you are probably sitting somewhere around Waypoint 2 or 3 on the Hyperadaptive Map. Past the initial pilots. Past the first AI Champion rollouts. Deep enough that the pressure to show results is real. Early enough that the system is not actually producing results yet.

This is the stretch where mandates get issued. Because mandates feel like action. Copilot for everyone. AI usage tied to reviews. Adoption targets on the board deck.

JPMorgan just tied AI adoption to performance reviews for 65,000 engineers. Light user, heavy user, non-user. That is the category you get sorted into. That is your performance story now.

The logic is clean. The outcomes are not.

Why The Dashboard Is Lying To You

Three days ago, TechCrunch published a piece that named a phenomenon I have been watching show up in every Stage 2 organization I work with. They called it ‘tokenmaxxing.’ Engineers running idle AI agents to farm usage. Fragmenting prompts so each one counts separately. Doing whatever the metric rewards.

The numbers inside this are not subtle. One study found that engineers with the largest token budgets produced the most pull requests, yes, and at twice the throughput for ten times the cost. The measurement metric and the business metric diverge sharply.

Meta has built an internal leaderboard for employee AI token usage. Salesforce is publicly pushing back, calling token consumption a vanity metric and proposing something they call Agentic Work Units instead. HRZone has been running stories on what they are calling ‘performance theatre,’ with Reddit threads of employees admitting they fabricate AI usage to satisfy mandates they privately think are wrong.

And 74% of companies buying AI tools still cannot show tangible business value from the investment. The gap between ‘we bought the seats’ and ‘we got the outcome’ is where most of these programs are sitting right now.

The dashboard is lying because the measurement system is operating on the wrong layer.

Why ‘Let’s Just Measure Better’ Doesn’t Work

When an organization is bifurcating, a small group of power users pulling ahead while everyone else runs token factories to meet the mandate, the default instinct is to fix the measurement. Better metrics. Story points adjusted for AI. Business value per commit.

I understand the instinct. I have watched smart people chase it for the last year. It does not work, and I think I know why.

The measurement disconnect is structural. It comes out of running bolted onto a Linear Organization, the hierarchical, siloed org chart where strategy flows down through layers and work moves sequentially across departments.

MIT Sloan’s research lines up with what I see in the field. Organizations that build systematic feedback loops between humans and AI are six times more likely to derive substantial financial benefits from AI. Not organizations that measure better. Organizations that design different feedback architecture into the work itself.

When did the measurement system at your company last get redesigned for what AI actually changes about the work?

So What Do You Actually Do?

The Hyperadaptive Map names three specific Moves for organizations stuck between Waypoint 2 and Waypoint 3. All three are about work design, not measurement.

The first is standing up a proper AI Activation Hub, which is almost the opposite of mandatory Copilot. An Activation Hub is a small, cross-functional team whose job is to find valuable AI work, measure it, make it repeatable, and teach peers how to do it. Not a center of excellence. Not a governance body. A learning engine.

The second is formalizing AI Leads inside each domain. These are the power users who are already pulling ahead. Instead of letting them keep pulling ahead, you change their job description. Their new job is to spread the judgment they have developed, peer to peer. The bifurcation does not close because you punish the non-adopters. It closes because the adopters become teachers.

Read More: Why Appointing AI Leads Isn’t Enough (And What to Do Instead)

The third is replacing your adoption metrics with AI Learning Flywheel metrics. Instead of ‘how many people used Copilot this week,’ you measure ‘how many new valuable AI patterns did we identify, package, and spread this month.’ The denominator changes from users to patterns. Everything downstream of that changes.

None of these Moves is measurable against token usage. That is the point.

He is going to walk into his Monday one-on-one with no plan, because he already knows the plan is not the answer. What he is bringing back is an observation: that the company is living inside the pattern, the measurement system is part of the pattern, and you cannot escape a pattern by measuring it harder.

If you are in the same seat, I would love to know what you are seeing. Are your dashboards producing confidence, or confusion? Are your power users spreading judgment, or climbing past everyone else while the token factories run?

Hit reply. I read every one.

If this is showing up in your organization, the Waypoint Finder is free and takes five minutes. It returns your current waypoint and the three Moves most likely to accelerate you.

Get the Deep Dive

If what I write is resonating, consider getting the book. Trust me, it is $$ well spent.

Or, better yet, trust John Ford and his review.

Hyperadaptive: Rewiring the Enterprise to Become AI-Native releases May 12th. Pre-order at hyperadaptive.solutions/book. The book covers the full model; this essay is one waypoint inside it.

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