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.

YOU MIGHT ALSO LIKE: Why SaaS Companies Require a Bigger Mission, Not a Smaller Headcount

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.

.

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.

Where are you on the AI transformation journey?

Depending on who in their organization is talking, they’re somewhere on what has become a predictable curve. Maybe their developers are flying with AI, but their operations teams are still figuring out what Copilot does. Maybe they’ve got a governance council, but zero coordination between their business units. They’re making progress in some areas, but spinning their wheels in other areas. Both things are true simultaneously.

What I’ve noticed is that most the uncertainty follows a predictable pattern, but some people are confused around the terrain. They don’t have a map.

So I built one.

The AI Adoption Terrain

Repeated conversation reveal organizations reaching one of these waypoints. The Hyperadaptive Model extends beyond the last waypoint, but I’ve found most organizations wrestling with these first six landmarks:

This is a visualization of the organizational AI transformation journey, mapped to the Hyperadaptive Model at the bottom. It isn’t a linear checklist, but more like actual terrain. With elevation changes. With traps. With a few places where the ground looks solid but isn’t.

Most organizations, can identify we’re there. Sometimes two people from the same organization point to different spots, which is useful information in itself.

Find Your Waypoint

Here’s how each zone feels like from the inside.

Confusion Zone. This is where most organizations started in November 2022, when ChatGPT hit. They didn’t know who owned AI, where it should sit in the organization, what models to use. Most organizations have moved on from this stage.

Early Wins. Licenses deployed, training videos assigned, box checked. A few curious people figured things out. Real wins, real enthusiasm, but the wins are trapped in pockets. AI Leads may have been named. An AI Council may exist. Neither is well-supported. The terrain here is deceptively flat. It feels like progress, because there is progress. But it tends to be isolated, what I call Random Acts of AI.

AI Bifurcation. This is the place where the majority of organizations are stuck right now. Power users have pulled significantly ahead. Everyone else is standing still, or going through the motions of using tools they don’t yet trust. The gap between the people who get it and the people who don’t is widening and visible. Organizations here often diagnose this as a training problem or a culture problem. It’s usually neither. It’s a systems and infrastructure problem.

Localized Progress. Things are working, but in isolated pockets. A team, a department, a function has genuinely figured something out. The temptation here is to scale before the foundation is solid: to reach for orchestrated agents before the organization has learned how to spread simpler wins. The ambition is real and right. The support systems to spread, scale, and sustain need to be put in place.

Coordinated Progress. This is where things start to feel different. The flywheel is turning. Activation Hubs are forming. Learning is beginning to move across the organization instead of staying in the team that generated it. The terrain is rising and the footing is solid. Organizations here are building the systems that makes progress compound.

Job Redesign. As automation takes root, roles begin to shift. People start moving from doing the work to building, monitoring, and maintaining the systems that do the work. This transition is genuinely challenging and genuinely important. The unrecognized part is that it goes much better when organizations do it deliberately on a small scale before they try to do it everywhere at once.

The Hyperadaptive Future. Orchestrated agents. Value stream orientation. A new operating model that the organization is capable of continuously updating because it has built the infrastructure to support it. This isn’t a finish line. The horizon keeps moving. A Hyperadaptive organization doesn’t ‘arrive.’ It gets better at traveling. While the book Hyperadaptive: Rewiring the Enterprise to Become AI-Native speaks to this emerging frontier, most organizations have yet to pass into this space.

There Are Specific Moves for Each Stage

Here’s what I want you to take away from this map: wherever you are on this terrain, there are proven moves for getting to the next zone. Specific, research-backed executable moves that organizations have made, and that the Hyperadaptive model is built to support. I’ve distilled lessons from leading organizations so you don’t have to.

Over the coming weeks, I’ll be publishing a series of field reports on what those moves actually look like in practice, complete with the politics, the resource constraints, and the 90-day markers you’d recognize if your organization tried it.

We’ll start with the AI Leads move, because it’s where most Stage 1 organizations could unlock real value. Then use case prioritization; how to go from a hundred ideas to three you’re actually starting with. Then the learning infrastructure that keeps what your teams figure out from disappearing into someone’s Slack archive.

But first:, find yourself on the map.

To get a clearer picture of which moves apply to your specific situation, explore the conversational Waypoint Survey.

Find Your Waypoint

Where do you land on this map? What feedback do you have? Tell me in the comments.

If you’d rather just tell me directly, hit reply.

Read more

Then he said: look around. You just told us you believe this is transformational. So why are most of your organizations set up to capture none of it?

The room got quiet (in a good way).

What followed was a conversation that felt different from most of what I hear at tech conferences right now. We skipped the ‘wow’ moments of AI and talked about what happens after the productivity gains arrive. And based on the questions we got and the conversations afterward, that’s a topic people are genuinely hungry for.

So let me share the core of what we covered, because I think it applies to most of you reading this.

The Surplus Is Already Here. Have You Decided What to Do With It?

Vibe coding and its concomitant productivity gains are real. EY’s most recent AI Pulse Survey found that 96% of organizations investing in AI are experiencing some level of productivity improvement, with 57% describing those gains as significant. Developers are building faster, freeing up time (although I think the backlog is so deep, it may or may not feel like real progress).

In some organizations, headcount is being reduced (though the EY data is interesting here: only 17% of organizations seeing productivity gains actually used them for headcount cuts. The headlines rail otherwise… so much hype.

The gains are landing. So, the question becomes…what happens next?

In both the research behind Hyperadaptive and in the conversations I have with enterprise leaders, there are essentially three postures organizations take toward an AI-generated productivity surplus. Ryan and I presented these last week:

• The first is Harvesting. Every efficiency gain goes straight to the bottom line. AI becomes a cost-reduction tool. The CFO is briefly delighted. Headcount shrinks or hiring freezes. Fast track to irrelevance, because your competitors are doing something different with their surplus.

• The second is Experimenting. A few power users are doing genuinely cool things. There’s energy, there are demos, there are Slack channels full of prompts. But there’s no map, no milestones, and no organizational learning happening. Motion without progress. (I see this one the most, honestly.)

• The third is Building. The surplus gets deliberately reinvested in capacity. AI Champions get protected time. The organization starts to rewire itself, one stage at a time. Compounding advantage.

Be honest with yourself about which one describes your organization right now. When did your organization last have an explicit conversation about where AI productivity gains are actually going? Can those gains even be measured?

The Missing Piece Can’t Be Found in AI Tools

The 2025 DORA research found that top-performing organizations are seeing 20 to 60 percent productivity gains from AI. Most organizations sit at 5 to 10 percent. The tools are the same. The models are available to everyone. The gap reflects differences in human factors, not access to technology.

The conundrum faced by most organizations is that coding speed is up dramatically, but the value captured by most organizations has not moved at the same pace. Because capturing the value requires a deliberate decision about reinvestment, and most organizations have not made that decision explicitly.

There’s a useful frame from Eric Ries’ work called the Temptation to Harvest. When a new capability creates genuine value, leaders face a choice to extract that value as margin, or reinvest it to build something bigger. AI has handed every organization a surplus. And right now the default choice — the path of least resistance, as well as pressure from the board — is to harvest.

Ryan made a version of this argument from his own experience, describing an earlier fight with a CFO about reinvesting software licensing savings into corporate social responsibility rather than letting it evaporate into the P&L. The CFO wasn’t thrilled. The reinvestment kept the organization focused on purpose and impact. That’s the same choice in a different context.

Related Article: Why SaaS Companies Require A Bigger Mission

One Decision, Compounded Over Seventeen Years.

The clearest illustration I know for what deliberate reinvestment looks like over time is Ping An Insurance.

In 2008, Ping An made a quiet decision to make data the foundation of everything. Not AI specifically, as they didn’t have the AI yet. Just a commitment to getting their data house in order and organizing around the human being in front of them rather than the products they were trying to sell. They restructured around the customer. They established a dedicated technology division, not as a support function but as a strategic driver. By 2017, they had formalized AI as one of their five core technologies and committed roughly 1% of revenue to AI research and development annually.

Today, Ping An serves over 240 million retail customers. Customers inside their digital ecosystem hold an average of 2.9 contracts each, compared to 1.2 for customers outside it. Revenue per ecosystem user reaches $5,288, compared to $1,399 for non-users. Nearly 4x.

That’s the result of one deliberate decision made seventeen years ago about what kind of organization they wanted to be, compounded. The technology followed the answer.

Ryan put it well on stage: Ping An’s 2008 is your 2025.

Related Article: The Five Stages of Becoming AI-Native

The window for this kind of decision isn’t what it used to be. Unlike the decade-plus runway organizations had with digital transformation, research on AI competitive dynamics suggests the compounding advantage for early reinvesters is closing much faster. (I’ll hedge that slightly: the exact timeline is contested, but the directional pressure is real and consistent across multiple sources.)

Three Things to Carry Out of This

I closed our session with three asks that I’ll repeat here, because I mean them.

Invest the surplus intentionally. Before the next efficiency gain disappears into the P&L, have the explicit conversation about where it goes. In everyday budget conversations, with specifics.

Name one AI Champion. One person in your organization, protected time, actual mandate to spread what they learn. That’s where the 20 to 60 percent gains start, and it costs almost nothing compared to what you’re already spending on AI tooling.

Related Article: Why Appointing AI Leads Isn’t Enough (and What to Do Instead)

Find a way to amplify yourself, not just your teams. Ryan’s part of our session touched on the human journey. He talked about the path from curious to generative to amplifying yourself with AI. I’ll let Ryan tell that story on his Manifest Substack (mnfst.ai), because he tells it better than I can. But the organizational and individual journeys are connected. You can’t build an AI-native enterprise without leaders who have made that journey personally.

The tools are delivering. At some point, you will have to decide what happens to the surplus, or let the default decide for you.

What does your organization’s reinvestment decision actually look like right now? I’d genuinely like to know. Drop it in the comments.

p.s. If you’re leading an AI transformation and want a framework for moving from Harvesting or Experimenting to Building, the AI Lead Accelerator is built for exactly that: hyperadaptive.solutions/accelerate.

And if you want the full roadmap — five stages, case studies, and the frameworks we referenced above — Hyperadaptive: Rewiring the Enterprise to Become AI-Native releases May 12th. Pre-order at hyperadaptive.solutions/book.

Thanks to and the Red Thread Research team, I found myself at UNLEASH America, an event focused on reinventing work. UNLEASH put me squarely in the people side: CHROs, heads of talent, L&D leaders, HR technology practitioners. Brilliant people wrestling with questions I typically address from a different vantage point.

And what I found there clarified something I’ve been circling for months: Who owns AI transformation? Truly integrating AI is a whole-enterprise problem. And right now, nobody quite owns it. The ambiguity is stalling organizations out.

A State of Confusion on Who Is Driving What

Analyst provided a keynote that was equal parts data deluge and honest editorial (his words, not mine). He’s been traveling the world meeting CHROs and HR leaders, and his diagnosis of the moment didn’t hold back: confusion. Not ignorance. Not reluctance. Confusion. The kind that signals you’re standing in the middle of an epic shift from one state of the world to another, and it is unclear who should be in the driver’s seat.

IT spending on AI is up nearly 62%. Does that mean IT should own the transition? Some 40% of companies are now spending $10 million a year or more on AI. Some of this is training. Should HR be driving the bus? The average large enterprise runs more than 1,000 distinct technology systems (!), 97 of which are employee-facing, almost all of them integrating AI into their software. Should the vendors supply the knowledge?

In part, because of this confusion, most organizations are moving at a fraction of the speed the technology, Bersin noted. AI capability is advancing on a steep upward curve. Organizations, with their jobs, structures, cultures, legacy systems, and deeply human resistance to change, are moving on a much shallower one.

My take is that we haven’t figured out we need to ‘invest in the rest’ when it comes to AI. For years, I’ve said shopping is the easy part. Installation is where the friction appears. And when you look at the above list — jobs, structures, legacy systems, human resistance — it takes an deliberate amount of time and resources to rewire these parts of the business. We know from the past that ignoring the people part of technology leads to delays and frustration. AI is no different.

Where AI Diverges the Past

Everyone I talk to (or hear speak), from the front lines of those building the models to the small business owner is breathless is overwhelmed by the non-stop flood of features, advancements, and hype around AI

Previous waves followed predictable adoption life cycles, years long, with room to plan, pilot, and scale. AI is doing none of that. Model updates are dropping every six weeks. Employees are already using tools their employers haven’t sanctioned. Your competitor isn’t waiting for you to complete your governance review.

So, how do we keep up with it all? Bersin outlined a deliberate progression (one that maps remarkably well to the Hyperadaptive journey; see below).

He said organizations start with individuals using AI to do their current jobs better, writing faster, analyzing data more quickly, reading emails with more efficiency. That’s real, but it’s table stakes. The next move is automating repeatable tasks, building reusable workflows. Then comes the moment where those individual automations start talking to each other, multi-agent coordination. And finally, entire business processes get reimagined around super-agents that orchestrate end-to-end work no single system ever touched before.

His example: UKG has built a super-agent capable of handling a front-line worker’s request, such as “I need an extra $400 after tax before Christmas, can you find me the right shifts?” The agent simultaneously checks qualifications, identifies eligible shifts, optimizes for pay rate, calculates tax impact, and returns a complete recommendation. That’s the future. Coordinated intelligence operating across what used to be five separate tools and three human handoffs.

Addressing Real Fears Around AI

The people side of the house isn’t just confused about technology. They’re watching their workforce become frightened and trying to figure out what to do about it.

Bersin cited University of Michigan consumer confidence data showing that American workers are experiencing a 50-year low in how comfortable they feel about their economic futures. He was careful to note that this isn’t just about inflation or income inequality. There is a palpable fear factor layered on top. People are reading the headlines about jobs being eliminated by AI, and many of them don’t have an understanding what happens to them in that story.

For large swaths of the workforce, reinventing themselves professionally is terrifying. They don’t know how. They don’t know where to turn for support. They don’t have models for it. The AI video library isn’t cutting it. And they’re watching headlines that suggest they may have to, whether they want to or not.

This matters because the most sophisticated agent architecture in the world fails if the humans it’s meant to augment don’t trust it, don’t understand it, or have quietly checked out. We need to provide the missing infrastructure.

So, Who Owns This?

In the room at UNLEASH, it was clear that HR leaders see AI transformation as, at least partly, their problem. The organizational change, the reskilling, the talent architecture, the workforce sentiment. All of that is undeniably their domain. But many of them are waiting for IT to lead on the technology side. And many IT leaders are pushing back by pointing to the business process owners. And the business process owners are looking at the C-suite. And the C-suite is making announcements.

Everyone sees the problem. Nobody fully owns it.

I believe this isn’t an HR problem or an IT problem. It’s a systems problem. And systems problems require systems thinking, which doesn’t stop at functional boundaries. Frankly, we need a cross functional AI Transformation office, alongside dedicated support structures that allow the organization to continue to reinvent themselves (I outline suggested ones in the Hyperadaptive Model).

What Bersin described as “systemic HR, stitching together the fragmented specialties of the HR function around actual business problems rather than internal organizational charts,“ is actually a version of something every function needs to do right now.

The siloed approach to AI, where each function pilots its own tools and builds its own workflows and reports its own wins, is how you end up with 97 employee-facing applications that can’t talk to each other and a workforce that’s more confused than empowered.

I created the Hyperadaptive Model because I saw the limits of previous technology transformations. I saw them stall as IT transformed only to hit the wall of finance or HR. I envisioned a lightweight model that cut across functions, giving everyone a shared destination with enough wiggle room to move at their own pace. A model that could stay durable as people moved from individual AI augmentation through process optimization, early automation, scaled AI, to what I call the Hyperadaptive state: an organization that doesn’t just use AI but continuously evolves with it. The model requires governance structures that cut across functions (AI Councils). It requires human nodes in every part of the organization who can translate AI’s potential into local context (AI Leads). It requires the social infrastructure to spread learning faster than any formal training program can (Communities of Practice and AI Activation Hubs).

The blueprint, grounded in research extended for the age of AI and examples from leading company is outlined in the book Hyperadaptive. What we need to do now, is organize around the blueprint and fund it.

What You Can Do With This

If you’re reading this as an AI transformation leader, I’d offer you two challenges coming out of this conference.

First, go find your counterparts in functional areas. Not to hand them the problem, but to build a shared map of it. The architecture questions and the workforce questions are not separable. The leader who understands both will be the one who actually moves the needle.

Second, create your shared roadmap. The research is here. The models exist (I’m sure there are many). What’s missing in most organizations is the organizational will to treat AI transformation as a whole-enterprise priority that extends beyond the technology.

The gap between where the technology is going and where most organizations are standing is real. But it’s not inevitable. It’s a design problem. And design problems have solutions, if you can work together to find them.

Hyperadaptive: Rewiring the Enterprise to Become AI-Native releases May 12th and is available for pre-order now at hyperadaptive.solutions/book. If you order now, you get early access to the model and supporting materials. It is research-grounded, enterprise-focused, and built for exactly the moment we’re all standing in.

And if you’d rather explore these concepts live, I’m hosting a peer roundtable on April 16th, just two weeks away, where we’ll dig into organizational alignment as the AI transformation function nobody assigned. We have a nice group formed. Upgrade to paid to be part of it.

And then... nothing scales.

Six months later, the AI Slack channel has gone quiet, as everyone gets wrapped up in business as usual. The AI Lead is still carrying their full original workload. A few teams are experimenting, but the insights aren’t spreading. Leadership is frustrated. The AI Lead is exhausted. And somewhere in a conference room, someone is asking why the ROI isn’t materializing.

These organizations have confused naming AI leads with enabling AI leads. These are not the same thing.

See if These Symptoms Feel Familiar…

If you’ve been paying attention to how AI adoption is unfolding inside organizations, a few patterns are hard to miss.

• AI Leads were appointed, but their job descriptions didn’t change. Their original responsibilities didn’t shrink to make room for their new mandate. They’re being asked to drive transformation in the margins of an already-full role. Enthusiasm is not infinite. It runs out.

• There’s no shared language, curriculum, or training program behind the title. Each AI Lead is winging it in their own direction, which means every team gets a different version of AI adoption — and nothing coheres at the organizational level.

• AI Leads are operating as islands. They may be evangelizing inside their own function, but there’s no formal mechanism for the marketing AI Lead to share what they learned with finance, or for the operations AI Lead’s discovery to spark an idea in customer success. The organizational design doesn’t support knowledge flow across boundaries.

• Leadership assumed that enthusiasm equals capability and walked away. The implicit message sent to AI Leads everywhere: you’re passionate about this, so you’ll figure out how to help others with AI. This is like assuming a talented piano player now knows how to teach someone how to play piano.

If you recognize your organization in any of these patterns, you’re not alone. But you are leaving significant value on the table.

What Enabled AI Leads Accomplish

Before we talk about what’s missing, let’s be clear about what we need from our AI Leads (it’s a lot more than cheerleading).

An effective AI Lead plays three distinct roles simultaneously.

They are a domain-specific implementer: someone who can identify high-value AI opportunities within their functional area, leveraging both their AI fluency and their deep contextual knowledge of how work actually gets done there. No outside consultant has this combination. Your AI Lead does this naturally, but it may stop there without formal guidance.

Which leads us to our second point. They can be enabled to become peer educators. The person others feel comfortable approaching with questions, who can provide hands-on, contextual guidance rather than abstract theory. Research consistently shows that peer learning dramatically improves knowledge retention over formal training alone. Your AI Lead is the person who makes AI feel accessible rather than threatening.

Last, we need them to be cross-functional connectors. A node in a network that allows insights from one part of the organization to spark innovation in another. In the Hyperadaptive™ Model, this happens through communities of practice and AI Activation Hubs. Marketing’s discovery informs sales. Finance’s experiment improves operations. This cross-pollination is how AI knowledge spreads at the speed organizations actually need.

That’s a significant portfolio of responsibility. Now ask yourself, what has your organization done to enable its AI leads?

Most Organizations Fail at Programmatic Support

The biggest mistake I see (and, don’t worry…it’s a common one) is appointing AI Leads and then leaving them to figure it out. This is a structural gap. For some reason, we think that people should be able to ‘figure it out on their own,’ but this lack of support is why AI efforts stall after the pilot phase.

I believe that programmatic support for AI Leads has three key dimensions.

1. Formal Training That Transforms Enthusiasm Into Expertise

Natural curiosity about AI is a great starting point, but it isn’t a destination. AI Leads require formal training that builds three distinct capability sets: AI fluency, process optimization expertise, and the ability to lead change. All three of these matter.

AI fluency means understanding not just how to use AI tools, but how to evaluate them, when to apply them, and where they fall short. (Large language models, for context, still hallucinate. Your AI Leads need to know this, and so do the practitioners they’re guiding.) It also means understanding your organization’s AI governance policies and where the guardrails are.

Process optimization expertise is what separates an AI enthusiast from an AI Lead who can actually create organizational change. It’s the ability to map workflows, identify where AI augmentation adds real value, establish feedback mechanisms, and measure outcomes, not just implement tools.

When Moderna introduced generative AI, they didn’t hand employees access and hope for the best. They prioritized upskilling their workforce and building a foundation of AI literacy that could sustain change over time. That’s the difference between a tool rollout and a transformation.

Last, we need to empower our AI leads to work with their peers; even those who may feel resistance to AI. We can arm them with skills to create ah-ha moments with AI and ways to empathetically relate to those who are struggling with moral or philosophical resistance to AI.

The training also needs to address something that often gets overlooked: what’s in it for the AI Lead personally. Will this role streamline their team’s operations? Create greater visibility with leadership? Open new career pathways? AI Leads who understand both the organizational value and the personal benefit of their role show up with a sense of ownership.

2. A Channel for Knowledge to Actually Flow

Your AI Leads are not just implementers. They are part of the nervous system of your AI adoption effort. Information flows through them to keep the organization updated as AI changes. They become the channel for signals to actually move through them.

This is where another piece of the Hyperadaptive System comes in: AI Activation Hubs. These are dedicated centers where AI expertise resides, AI advancements are tracked, and knowledge gets atomized into digestible, actionable learning. The Hub’s job is to stay current with AI capabilities and then distribute that knowledge through AI Leads to the practitioners who need it.

Think about what that flow looks like in practice.

Imagine it’s a Tuesday morning. Your AI Activation Hub has just identified a new capability in your Microsoft CoPilot that’s directly relevant to contract review in the legal department. They don’t send a company-wide email that gets buried. Instead, they brief the four legal AI Leads, who speak both the language of AI and the language of legal operations, and equip them with the context, talking points, and a simple use case to share with their team. By Thursday, three lawyers are experimenting with it. By the following week, someone has a workflow that cut document review time in half. That story goes back up through the AI Lead, through the Hub, and into the organization’s knowledge base, where it becomes the basis for the next wave of adoption.

This is what an AI learning flywheel looks like in motion. It’s not a training event. It’s a system. And once you get it spinning, it becomes self-sustaining.

3. A Community That Cuts Across the Organization

Motor Oil Group’s AI Garage initiative offers a useful model here. Their AI champions don’t work in isolation. They participate in weekly show-and-tell sessions, share what they’ve learned across functions, and translate AI concepts into their specific business context. The result is what researchers call learning contagion, or the organic spread of effective AI practices across traditional organizational boundaries.

This is what happens when you formalize the community infrastructure around your AI Leads. They stop being isolated enthusiasts and start becoming a strategic network. Regular cross-functional gatherings create forums where the AI Lead from supply chain and the AI Lead from customer experience can discover that they’re solving related problems and accelerate each other’s progress rather than duplicating effort.

Informally, these networks begin to emerge on their own in organizations where AI adoption has gained any traction. Formalizing them by giving them structure, rhythm, and dedicated platforms for knowledge sharing, converts organic momentum into sustained capability.

What This Looks Like When It Works

Unilever didn’t operationalize AI by purchasing licenses and hoping adoption would follow. They trained 23,000 employees in AI usage and simultaneously built the agile capabilities to focus those new skills on the highest-priority organizational challenges. That type of commitment moves AI from a technology rollout to an organizational infrastructure investment.

Wells Fargo went further, developing systematic upskilling programs, establishing academic partnerships with institutions like Stanford, and creating structured career pathways for AI talent. The message to employees was clear: this is a long-term commitment, and your growth inside it is real.

These aren’t one-time events. They’re ongoing systems. And they happen because someone decided that naming AI Leads wasn’t enough.

A Question Worth Sitting With

If you have AI Leads in your organization, take a moment to answer these honestly: Do they have formal training? Not a one-time workshop, but an ongoing program? Do they have protected time to actually do the work, or are they being asked to transform your organization in the spaces between their existing responsibilities? Are they connected to each other and to a central hub that keeps them current? And do they know what’s in this role for them?

If the answer to most of those is not really, you haven’t failed. You’ve just identified the next most important thing to build.

Empower Your AI Leads

At Hyperadaptive Solutions, we built the AI Lead Accelerator specifically to address this gap. It provides a structured program that gives AI Leads the shared language, practical frameworks, peer community, and ongoing support they need to move the needle. If you’re realizing your AI Leads need more than a title, we’d love to talk.

And if you want the full picture of how AI Leads fit into a comprehensive organizational transformation model (including the support structures, stage-by-stage blueprint, and research behind it) my upcoming book Hyperadaptive: Rewiring the Enterprise to Become AI-Native (IT Revolution Press, 2026) lays it all out. Pre-orders are open now at hyperadaptive.solutions/book.

The organizations that scale AI aren’t the ones the ones that built the infrastructure to spread what they know.

It’s the right question. And most enterprises I talk to aren’t asking it.

The dominant model for AI learning in large organizations right now looks something like this: leadership identifies a skill gap, L&D builds a curriculum, a platform gets licensed, cohorts get scheduled, modules get completed, and certificates get issued. The box is checked. The program is declared a success.

The speed of AI advancements turns this training model on its head.

Six months after traditional AI training, the capabilities that felt cutting-edge in Q1 are table stakes in Q3. The workflows that employees just learned have already shifted. And the organization’s response simply can’t be to build another training program.

This is the training trap. And understanding why it fails is the first step toward building something that actually works.

The Structural Mismatch of Learning AI

There’s a structural mismatch at the heart of enterprise AI learning that most organizations haven’t fully confronted: the tools are evolving faster than the training cycles.

The expiration date on technical training used to be measured in years. A PowerPoint certification from 1997 was still largely valid in 2002. An Excel course from 2008 didn’t require a major overhaul by 2010. The tools were stable enough that a discrete learning event such as a workshop, a certification, or a cohort could remain relevant long enough to justify the investment.

With AI, that expiration window has compressed to months, if not weeks. GPT-4 to o3. Claude 2 to Claude 4.6. Each release changes what’s possible, what’s expected, and what employees need to understand about their own work. A training program built on last season’s capabilities teaches people to think about AI’s possibilities in terms that no longer describe what the tool can actually do.

The World Economic Forum projects that 59% of the global workforce will need reskilling by 2030. But the implication buried in that statistic is that reskilling is not a project with a finish line. It is a permanent operating condition. The moment you complete a comprehensive training rollout, the clock on its obsolescence has already started.

And yet, organizations keep reaching for the training program because it is the most familiar shape for learning. It has a sponsor, a budget, a timeline, and a completion metric. It looks like progress. It produces certificates.

What it doesn’t produce is an organization that knows how to keep learning as the landscape shifts.

So… How Can We Keep Up with AI?

The real question becomes whose job is it to keep up with AI?

In most organizations, the honest answer is everyone’s, which means no one’s. Keeping up with AI is a near full-time job. And everyone in the organization is full up. The CAIO has a strategy to manage. The business unit leaders have quarterly targets to hit. The managers have teams to run. Staying current on AI model developments and their implications for our workflows is on everyone’s list…and nobody’s actual job.

The result is predictable. The organization’s working knowledge of AI capabilities drifts behind the actual state of the tools. People use yesterday’s mental models to make today’s decisions. Competitive advantage erodes from a slow accumulation of not-quite-current understanding.

PwC recognized this problem early and built something different. Rather than rolling out uniform AI training to their entire workforce, they created a network of AI champions who were explicitly responsible for staying current on AI capabilities, translating that knowledge for their specific business context, and getting it into the hands of their peers. Prompting Parties, highly interactive, peer-led sessions where employees experiment with generative AI on real work problems, generated over 400 requests for additional sessions in the first few months and reached more than 22,000 employees.

PwC didn’t try to keep everyone equally current on everything. They built a distribution infrastructure for learning. A network of informed nodes who could translate new capability into relevant context for the people around them.

That’s a fundamentally different architecture than a training program.

The AI Learning Flywheel

In the Hyperadaptive Model, this architecture has a name: the AI Learning Flywheel. And it runs on three interconnected layers.

AI Activation Hubs are the sensing layer. A team (or network of teams in large organizations) whose job includes monitoring AI advancements, evaluating their implications for specific functional contexts, and atomizing that knowledge into micro-bites of learning that can actually travel through the organization. This is the group that absorbs the complexity so everyone else doesn’t have to. They are, in effect, the organization’s immune system against AI-obsolescence.

The AI Leads are the distribution layer. Embedded in business functions, AI Leads receive the contextualized knowledge from the Activation Hub and get it into the hands of their peers. They’re function-specific translators who understand both the capability and the workflow it affects. And they’ve been trained on the most effective way to spread knowledge. Legal AI Leads understand legal workflows. Finance AI Leads understand finance workflows. That context is what makes the learning land.

The Communities of Practice are the integration layer. Informal at first, increasingly structured over time, these communities are where practitioners share what’s working, surface what isn’t, share learnings with the Activation Hubs, and build the collective intelligence that no individual node could develop alone. During Stage 3 of the Hyperadaptive journey, some form around technical challenges, such as “how do we build effective learning loops into our customer service automations?” Others address human concerns: “what skills should we develop as our roles evolve?”

Together, these three layers create a flywheel: new capability enters through the Activation Hub, gets contextualized by AI Leads, gets applied and stress-tested by practitioners, and the resulting insights flow back up through the network. The flywheel is not a program with a start and end date. It is an operating rhythm.

I've written an Ebook on the Flywheel

What This Looks Like in Practice

Let me make this concrete. Anthropic releases it’s newest model, Claude 4.6. For most organizations, the response is: someone reads the release notes, maybe a Slack message goes out, and 95% of employees continue using AI the way they were using it last quarter.

In an organization with a functioning Learning Flywheel, it plays out differently. The AI Activation Hub in, say, the legal function assesses the specific implications of the 4.6 release for how legal currently uses AI, including what changed, what’s now possible that wasn’t, what previous workarounds are now unnecessary. They spin up a brief, contextualized update, including a short video, a one-pager, a practical example. It goes to the Legal AI Leads, who demo it to their peers.

With this approach, the legal team’s working mental model of their AI tools updates in days, not quarters. No enterprise-wide training program required. No cohort scheduling. No completion metrics that have nothing to do with whether anyone actually changed how they work.

What ‘always on’ learning requires is investment in the learning arena. The good news? You can upskill and redeploy existing people to make this happen.

Creating dedicated ways for learning to flow is what it means to have a learning infrastructure rather than a learning program. The infrastructure is always on. The knowledge flows continuously. The organization doesn’t fall behind between training cycles because there are no training cycles. Rather, there is the ongoing rhythm of sense, translate, distribute, apply, and feed back.

What Does Your Organization Look Like?

Take a moment to map how your organization currently handles a major AI model release. Who finds out first? How long does it take to reach the practitioners actually using the tool? What determines whether they update how they work, or whether they keep doing what they were doing? Who is responsible for that answer?

In most organizations, that map reveals a learning infrastructure that stops at the awareness layer. People know something has changed, but the knowledge doesn’t reliably travel to the people who need to act on it, in the context that would make it actionable.

The WEF’s Future of Jobs Report 2025 found that 52% of leaders now rank job redesign as their top workforce priority. That tells you the work itself is the moving target. An organization where the learning infrastructure can’t keep pace with tool evolution is already losing ground on the larger challenge of work redesign.

Building the flywheel is not a technology project. It requires designated people (Activation Hubs, AI Leads), a communication rhythm, and the cultural commitment to treat learning as infrastructure rather than event. It requires, at minimum, asking: who in this organization is it actually someone’s job to keep up with AI? And then building the distribution system that lets their knowledge travel.

Final Thoughts

The organizations winning with AI are the ones where new capability travels fastest from the people who understand it to the people who can act on it.

That’s a distribution problem. And distribution problems require infrastructure.

The training program made sense in a world where the tools were stable and the gaps were bounded. In a world where the models evolve every six months, where the workflows are continuously redesigning, and where the humans responsible for judgment calls on AI-generated output need to be getting sharper continuously, the training program is a coping mechanism dressed up as a strategy.

That’s not to say we don’t need our L&D professionals. We do. We just need them in a different way. Embedded in Activation Hubs. Continuously atomizing the learning.

Build the flywheel. Designate the sensing layer. Enable the translators. Create the communities. And then let learning flow through the organization the way AI moves through your processes: continuously, contextually, and without a finish line.

Melissa Reeve is the founder of Hyperadaptive Solutions and author of the forthcoming Hyperadaptive: Rewiring the Enterprise to Become AI-Native (IT Revolution Press / Simon & Schuster, May 2026). The Hyperadaptive Model helps Fortune 500 enterprises build the infrastructure for AI-native operations. Pre-order your copy at hyperadaptive.solutions/book.

SPECIAL OPPORTUNITY

The first paid community peer roundtable is coming up on April 16th: Getting Your Organization to Agree on What ‘Adopting AI’ Means. RSVPs are already rolling in, and I’m genuinely excited about the group that’s forming. If you’ve been thinking about joining the paid side of Hyperadaptive, this is a great reason to do it. Small room, real talk, no fluff. Upgrade to paid to access.

Subscribe now

The projector shone overhead. The HVAC system hummed. The laptop in front of me glowed. The conference room booking system that someone, somewhere, configured. The fiber optic cable feeding the Wi-Fi. The building management software keeping the lights at exactly the right brightness.

So many things in that room existed because electricity was invented as a general purpose technology. Not just the lights. But so many physical items. And I thought about the jobs required to build and support them. The software engineer who built the booking app. The network security analyst protecting the Wi-Fi. The UX designer who made the laptop feel intuitive. None of those jobs existed before electricity reorganized what work meant.

And I thought to myself…in twenty years, someone will sit in a room full of things that don’t exist today, created by people working jobs that don’t have names yet. The same way I couldn’t have predicted cloud architect in 1985, we can’t fully predict what’s coming.

But, based on history, we know it is coming. So, let’s take a moment to suspend the ‘jobs are going away.’ narrative to jump into what the future might hold.

First, an Honest Look at the Data

Before we get to the future, let’s be clear about the present. The World Economic Forum’s Future of Jobs Report 2025 surveyed over 1,000 global employers representing more than 14 million workers. Their headline finding: the WEF estimates a net new change in jobs, not a vanishing of them. By 2030, 170 million new jobs are projected to be created while 92 million are displaced, resulting in a net employment increase of 78 million jobs (roughly 7% of today’s total workforce).

That net positive number is real, and it matters. But two data points in the WEF report deserve a harder look before we move on.

First, the WEF lists Software and Applications Developers and Light Truck Delivery Drivers among the top fastest-growing roles through 2030. I can feel you squinting. If you follow the tech industry, you know junior developers are being laid off right now as AI coding tools eliminate what was once entry-level work. And if you’re watching the transportation sector, autonomous vehicle investment seems to point directly away from more drivers, not toward them.

Here’s how to hold both truths at once. With software, AI automates existing software AND enables the creation of entirely new categories of software products and services that weren’t economically viable before. The market expands. The nature of the work transforms dramatically (less writing boilerplate, more architecting and auditing what AI generates), but net employment grows because there is simply more software in the world to build and maintain. The current junior developer layoff wave is real disruption at the leading edge, not the final destination.

With drivers, the WEF’s 2030 timeframe falls before full autonomous vehicle deployment at scale for complex last-mile delivery. E-commerce growth (itself partly AI-enabled) is currently creating demand that outpaces what technology can absorb in that window. Both things are happening simultaneously, and the 5-year projection reflects that specific window. The 10-year picture may look different.

The broader lesson here is that the labor market transition is nonlinear and full of tensions. Which is exactly why the second WEF data point matters most.

If the world’s workforce was made up of 100 people, 59 would need training by 2030. Of these, employers foresee that 29 could be upskilled in their current roles and 19 could be upskilled and redeployed elsewhere within their organization. However, 11 would be unlikely to receive the reskilling needed, leaving their employment prospects increasingly at risk.

That is the number that should focus every AI transformation leader. Not the net gain of 78 million jobs, but the 11 people in every 100 who fall through the cracks if we don’t act deliberately. (If you want to dig into the specific failure patterns to avoid, I wrote about them in an earlier piece: The AI Digital Rust Belt Is Optional.)

What History Tells Us About Job Evolution

Across every wave of automation in the last century, we’ve seen that when machines take over the doing, humans shift to building and maintaining the machines that do it. When the washing machine took over the laundry, we built and maintained washing machines. When we automated the switchboard, we shifted from manually connecting calls to building and maintaining switchboards.

At FedEx, when their advanced automated sorting system came online, they trained five hundred team members to operate it, including eighty-five new hires whose entire role was to maintain the system. Then they went further. Their BOT-it program turned frontline workers into citizen coders. Employees with no prior coding background developed fifty-six automation products that saved tens of thousands of manual hours. The people who once executed the processes became the people who built the next generation of processes.

This is the through-line. And as AI becomes more capable, the jobs on the building, monitoring, and maintaining side of that equation get more interesting, and more numerous. Let’s get specific about what that actually looks like.

Imagining the Jobs That Come With AI

Here’s the exercise I want you to try. AI is already enabling the design and manufacture of physical products that didn’t exist five years ago. Each of those products requires humans to build, monitor, and maintain the AI systems that create them. Those humans will have job titles we haven’t invented yet.

Consider what’s already emerging from engineering and materials labs:

AI is now designing structural components, including aerospace brackets, architectural beams, car chassis, that look almost biological. Organic curves that eliminate every unnecessary millimeter of material. The shapes are optimized for strength in ways human engineers wouldn’t manually draft.

But someone has to sign off before those parts go into an aircraft. Enter the AI Validation Engineer: an engineer who takes the AI’s design output and stress-tests it against physical reality, because simulation and the real world diverge in ways that experience catches first. They also ensure the part can actually be manufactured by the equipment that exists. We need a host of new jobs to:

• Build the plants to manufacture these new aircraft parts

• Source the materials that go into building the ultra-light parts

• Maintain the robots that assemble next-generation parts

AI is discovering new battery chemistries by simulating millions of atomic combinations. The Crystal Structure Validation Engineer is the person who takes those AI-discovered compounds and tests them across real-world temperature ranges, charge cycles, and edge conditions that the model never saw. The AI finds the candidate. This person determines whether physics cooperates.

AI is enabling responsive meta-materials, including fabrics and building materials engineered at the microscopic level to respond to environmental stimuli such as changing their thermal insulation, absorbing specific sound frequencies, adapting to conditions. Someone has to define those stimulus-response parameters. Along with this new technology comes new roles in research, manufacturing, and maintenance.

As we explore drone delivery services we see the emergence of Drone Mission Controllers responsible for coordinating the intersection of drones between services and other flying objects operating in an unscripted environment.

AI is enabling screenless environmental sensors: earpieces, glasses, pendants with outward-facing AI chips that continuously parse your physical surroundings. Someone has to define what that AI pays attention to and what it ignores. The Contextual Awareness Designer is responsible for that model (as well as for the moment when the system misclassifies something it shouldn’t). They’re part product designer, part cognitive scientist, part safety engineer.

Not one of these job titles appears in a standard HR system today. Every single one maps directly to the build-monitor-maintain pattern. And every single one requires a human who deeply understands both the technology and the domain it’s operating in.

The Organizations That Can See What’s Coming

The above roles don’t emerge by accident. They are invented by organizations that have built the capability to sense what’s needed next, form teams to define it, and develop people to fill it faster than a competitor can recognize the need exists.

The Hyperadaptive Model sets the stage for this exact outcome. By integrating learning loops and changing the operating model, organizations move their people off career ladders and into a network that evolves roles continuously and intentionally, rather than scrambling when the disruption has already arrived.

As Hyperadaptive notes, the goal in the most advanced stages of organizational AI maturity isn’t to fill today’s roles better. It’s to build an organization that can sense the need for a new role, define it, and empower people to fill it faster than anyone else.

The new jobs are coming. The question is whether your organization will be inventing them, or scrambling to staff them after you’ve laid off all of your best workers or before someone else does.

One Last Thought

I left that AI session thinking about all of it. The room full of things that wouldn’t exist without electricity. The jobs that electricity created. The things that are being built right now in labs and foundries that will require people to build them, watch over them, calibrate them, and course-correct them when they stray.

The piece of this I keep coming back to is imagination. Because the organizations that will define this decade aren’t the ones slashing headcount. They’re the ones actively inventing what comes next.

That’s a different kind of leadership. And it’s available to anyone willing to start imagining.

If you’re building the organizational capability to navigate this transformation, Hyperadaptive by Melissa Reeve (IT Revolution Press, 2026) offers a research-grounded roadmap — from the first AI experiments to the fully AI-native enterprise. Pre-order now.

PRE-ORDER NOW

Related Article

SPECIAL OPPORTUNITY

The first paid community peer roundtable is coming up on April 16th: Getting Your Organization to Agree on What 'Adopting AI' Means. RSVPs are already rolling in, and I'm genuinely excited about the group that's forming. If you've been thinking about joining the paid side of Hyperadaptive, this is a great reason to do it. Small room, real talk, no fluff. Upgrade to paid to access.

Subscribe now

Read more

Hi Everyone!

When I launched the paid community, I promised you real conversations with peers who are navigating the same AI transformation chaos you are.

I’m making good on that. Starting now.

🗓 First Peer Roundtable: Thursday, April 16th “Getting Your Organization to Agree on What 'Adopting AI' Means?"

Here’s the deal: every enterprise leader I talk to i…

Read more

The students were terrified. They are watching the rapid…

Read more

In 2010, the world shipped 121 million dedicated cameras. By 2023, that number had cratered to 7.7 million. That’s a 94% collapse in just over a decade. The smartphone didn’t just disrupt the camera industry. It vaporized the economic moat that protected an entire professional class. When smart phones automated focus, exposure, and color grading, the ab…

Read more

There is no clicking of a mouse. There is no frantic alt-tabbing between windows. There is no copy-paste fatigue in my wrists.

Read more

I recently looked back the $1,500 annual upskilling stipend I provided to my teams. On paper, it was a forward-thinking perk. But in reality, it was a failure.

Most of my team couldn’t (and didn’t) spend it. Not because they didn’t want to learn, but because the day-to-day reality of work wouldn’t let them. To use tha…

Read more