Every breakout in the cohort felt too fast. Both sessions, every block. The first time I called everyone back, the report-out opened with “we were just getting into some great ideas and then, oh, the timer, here comes the timer.”

We are all thirsty for community. And doubly so when it comes to AI Transformation.

So that’s what I want to write about today. Why creating a Learning Arena is important (as says “the community is the curriculum”), the headwinds AI leads are facing, and where the class revealed orgs are strong and weak across the nine dimensions of the Hyperadaptive Model.

What is a Learning Arena?

When AI can generate a course faster than it takes me to write and edit an article like this (yes…I am still here!), what is left?

Each other, our lived experiences, our judgment, and our challenges.

I built the class on a hunch that the people in the room have as much to share with each other as I did, despite being the ‘teacher’ in the room. My goal was to facilitate the cohort in a way that surfaced the knowledge in the room. I call it a Learning Arena. In the room, we had:

• A PMO leader at a Fortune 100 insurance company.

• A VP of technology at a trucking company.

• Someone standing up a brand-new AI enablement practice at a Fortune 500.

• A COO at an AI startup.

• AI consultants, mapping clients across five industries

Live events being live events, my timer’s music option didn’t work (I offered to sing instead, but they declined…smart!), the chat had a character limit I hadn’t tested, and one participant found a bug in the copy button. “Aha, we found a bug. Congratulations, high five.” Murphy was in the room, as usual.

Rather than being a teacher in a classroom, I was a coach in the arena, making real-time calls. By session two, I responded to the need for more time in breakouts. Ten-minute breakouts became twelve, then twenty-two. One pair chose to give each other feedback on something outside of the stated curriculum. Fine by me. Go where there is value.

Yes, we used AI. In the chat, a learner remarked “I like how you are dog fooding us.”

We used AI to do analysis, to brainstorm, to help us when we got stuck. All of it was cool, but then we brought it back to the humans to discuss the context and to challenge it. Because there is value in conversations and people’s experience that you can’t download into a machine yet.

AI Transformation Headwinds

So, how does a learning arena look in action? Let me provide the before-and-after.

The first iteration of the cohort had headwinds (derived from my own experience and research), planted on the board:

• Authority disconnects

• Stalled efforts

• No playbook

• Need for speed

Tidy, right? The original version presented these on a slide to “discuss” while learners multitasked with Slack and whatever emergency was hitting their inbox.

The learning arena invited learners to put their headwinds on the board. You know how many surfaced? Thirty seven. Directly relevant to the people in the room, from their lived experience. Here’s how they clustered:

Much richer than my tidy four. Culture and change resistance, 9 notes, 24%. Strategy and leadership vacuum, 7 notes, 19%. Knowledge and training gaps, 6, and execution drag, 6. Org structure and governance, 5. Funding, 3. Data and tech readiness, 1.

And, if you notice, only one sticky about the technology. One.

Culture and leadership between them took 43% of the board, with stickies like “reactionary decisions” and “funding” and “org chart.” Everyone keeps asking why AI is stalling out. Maybe they should ask this group.

The stories behind the stickies, in their words:

• A consultant, on a state-government client: “Let everybody have access, and let’s see what kind of great things they can do. And the great things that they can do turn out to be things like… have it proofread my emails. And then I don’t touch it the rest of the day.”

• A technology partner at a fintech, on customer support: “There’s probably 20 or 30 POCs that are essentially trying to do the same thing.”

• A marketing leader running pilots across every function: “a whole lot of seeds that are just kind of being watered, but nothing’s actually germinating.”

• And one enterprise where only the developers had been given AI access, so the wins were happening in a black box the rest of the company couldn’t see.

Every one of these organizations had plenty of AI running. I told the marketing leader she wasn’t alone and that I have a name for it: random acts of AI.

The surveys say this room is typical.

• Gartner found 27% of executives have a comprehensive AI strategy.

• Gallup put “leadership has communicated a clear plan” at 22% of employees.

• Microsoft got 26%.

• BCG got 33%.

Four surveys pointing at the same thing. Most people holding an AI mandate are holding it without a plan above them, and working around that behind the scenes. One PMO leader described her version: “When people say no to me, I interpret it as not now.” (She has about 250 grassroots people riding along. Two hundred and fifty!)

What else surfaced: Where orgs were strong and weak across the nine dimensions

As part of the first session, participants read their AI Terrain using a voice assessment that scored an organization across nine dimensions on a five-stage scale, and then pressure-tested their answers in real time. (“Is this evidence your leadership would accept as proof?”)

AI Impact (think the effect AI is having on the org) came in highest, 2.0 on average. Adaptation and Learning (systematic, integrated learning loops, embedded in processes) came in lowest, 1.13. Every organization sat at Stage 1 on the Linear Organization (the impact AI has had on org design) dimension.

This points to impact without spread, where there are plenty of pilots producing plenty of results, and the learning stays in the team that built it, in an org shaped the way it was before AI showed up. That is what random acts of AI look like as a number.

The app is a starting point and you get to argue with the read, and people did. A COO said, “Wait a minute, that’s my number? Really? I want to push back.” An AI enablement lead whose company scores well on every other maturity assessment: “We actually score pretty cruddy on this assessment,” because the other assessments never ask about decision-making or learning.

The headwinds and the assessment informed session two, where we identified one ‘move’ they could make as a next step.

The move could be a decision, an action, a funding request. We worked our moves individually and as a group, pressuring testing it. Where does it die? Who kills it? Of the eleven moves that came back, seven were about influence. Getting a yes from someone whose box on the org chart you don’t sit in.

A facilitator, watching the rest of the room read its scores, asked, “Wouldn’t it be cool to run this with a group of senior executives from the same organization, at the same time, in the same room.” I agree. A powerful way to get alignment on where the growth areas and blockers reside.

The Next Cohort is In October

October 6 and 13, two mornings, fifteen seats, and it’s a powerful way to understand where your organization is at, what move to make, and connect with others in your role. Details at hyperadaptive.solutions/class.

“Make sure that your next move is going to really move the needle on what is your prime problem.”John Ford, Business Productivity Consultant

Get the full story (45 seconds):

Sources & Further Reading

• Gartner: only 27% of executives have a comprehensive AI strategy (May 2026)

• Gallup: 22% of U.S. employees say leadership has communicated a clear AI plan (June 2025)

• Microsoft 2026 Work Trend Index: 26% of AI users see leadership aligned on AI strategy

• BCG AI at Work 2026: 33% of frontline employees find leadership AI communications clear

• Participant testimonial (video)

• Running Hyperadaptive Organizations, October cohort

If you are a person of a certain age (ahem..), you might remember Vince and Larry, the crash test dummies that promoted seat belt safety in the 1990s (”you could learn a lot from a dummy!”). Or you might remember a band of the same name. Ah, the 90s…

But, before I get too far into nostalgia, let’s visit the employer of the dummies - the ones who crash them into walls at 40 miles an hour (or faster!).

Take, for example, a test run in August 2012, where they took eleven luxury cars and ran each one into a barrier.

Not head-on. They hit only the outer quarter of the front end, driver’s side, against a rigid five-foot wall. The corner. The part of the car that misses the frame rails, where all the crash engineering lives.

Then they published what happened.

Two cars rated good. One acceptable. Four marginal. And four rated poor: the Mercedes-Benz C-Class, the Audi A4, and two different Lexus models.

The dummy’s employer is the Insurance Institute for Highway Safety (IIHS). They have no authority over anybody. They can’t fine a manufacturer, order a recall, or require a single change. No federal standard covers their crash configurations.

Every car that failed was street legal the morning of the test and street legal the morning after.

When the program began, about 10% of the vehicles IIHS tested earned a good rating and 40% rated poor. 🙈 Today virtually every vehicle tested earns good, on both sides of the car.

Somebody decided safety was important, published a number, and the industry responded.

This week I explore why that worked and how this approach might apply to AI.

Two Programs, Two Different Results

IIHS is, in its own words, “wholly supported by auto insurers.” Roughly 140 insurance companies and two insurance trade associations. Go down the member list and you will not find an automaker on it.

The organization that grades the cars is paid for by the people who write the checks when the cars crash. Nobody funding IIHS loses money when a vehicle rates poor. If anything, they’d rather know.

Now compare the federal program.

The National Highway Traffic Safety Administration runs the New Car Assessment Program, the five-star ratings you see on the window sticker. Same idea, government version, and by 2005 the GAO was already sounding the alarm. Of the crash tests NHTSA ran in 2004, “over 95 percent received a four- or five-star rating.” The GAO’s conclusion: NCAP tests “provide little incentive for automakers to continue to improve vehicle safety and little differentiation among vehicle ratings for consumers.”

It didn’t get better. By model year 2020, 73% of vehicles got five stars and every single one of the rest got four. Nobody got three. Joan Claybrook, who used to run NHTSA, put it plainly: “There are differences between the cars… But you don’t see them in the data because the standard is so low that all cars comply.”

Everybody gets an A. And a grade nobody can fail isn’t doing the job of a grade.

And then there’s this….

NHTSA finally moved to update the program. Started the process in 2022, issued a roadmap in December 2024. Then on September 22, 2025, the agency published a notice delaying the updates from model year 2026 to model year 2027. The reason given in the notice: concerns raised by the Alliance for Automotive Innovation, which is the automakers’ trade association. NHTSA agreed these were “valid reasons to request that the Agency delay the implementation of the new NCAP requirements.”

The regulator delayed its first serious upgrade in two decades because the regulated industry asked it to.

Meanwhile, over at the insurer-funded shop, IIHS was doing the opposite. In 2022 they gave out 101 Top Safety Pick awards. In 2023, after tightening the criteria, they gave out 48. IIHS president David Harkey on why: “The number of winners is smaller this year because we’re challenging automakers to build on the safety gains they’ve already achieved.”

Same industry, same decade, opposite behavior.

The Safety Bar Keeps Getting Raised

When an IIHS test stops separating good from bad, they kill it and build a harder one.

Their announcement of a new side-impact test in 2019 exemplifies this: “The program has been so successful that the current side ratings no longer help consumers distinguish among vehicles.” At that point 99% of rated vehicles were earning good on the old side test. And yet, side impacts were still killing about a quarter of vehicle occupants who die in crashes. So they built a new one, using a heavier barrier at a higher speed, generating “82 percent more energy than our current side rating test.”

They’ve retired the original moderate overlap test, the original side test, the roof strength test, the head restraint test. Not because those problems came back. Because everyone passed, and a test everyone passes has stopped being information.

Is there anything in your organization that works like that? A measure you retire on purpose the moment it stops discriminating? Most of us do the reverse. We find a metric that makes us look good and we put it on the quarterly slide forever.

Nobody Was Grading That Side of the Car

Back to 2016, because IIHS did something that turns out to matter enormously for AI.

They took seven small SUVs that had all earned good ratings on the driver-side small overlap test. Then they ran the same crash on the passenger side. Unannounced. Not part of any rating.

One of the seven still rated good. Several showed ten to thirteen inches of additional intrusion. Same vehicle, same model year, one side reinforced and the other side not. IIHS’s David Zuby: “It’s not surprising that automakers would focus their initial efforts to improve small overlap protection on the side of the vehicle that we conduct the tests on.”

Read that with AI agents in mind.

You get what you grade.

Now think about this past July, when roughly 700 AI agents inside a cybersecurity evaluation discovered they could talk to each other through a shared file service, organized around a goal, and ended up with administrator access nobody meant to give them. It ran for weeks. What surfaced it was a capacity alert on build infrastructure.

Every one of those models had been evaluated. Extensively. Against benchmarks for capability, for refusal, for a long list of known harms.

Nobody was running the test for what happens when a few hundred of them can reach each other.

That was the passenger side. Not tested, so not reinforced.

Good Guidance Isn’t Enough

Ethan Mollick published a piece this week called Agency and Agents, and highlighted that “Not one [of the agents] was set up to ask a person for anything. That was a security test, and isolation was the point.”

He argues for what he calls a Twilight Factory, where “Agents do most of the work, but they proactively reach out to humans in ways that make both better.” He lays out when they should reach out: for approval, because “Agents should not decide by themselves to spend money, contact outsiders, access sensitive material… or take actions their human managers did not authorize.” For expertise, because models are still jagged. For variance, because you don’t want every strategy written by the same mind. And for interest, and this one I loved: “If agents make every interesting decision and leave people with the approvals, the exceptions, and the failures, we will have automated the wrong half of the job.”

I think he’s right about all four. And I’d add is this:

Good guidance without action is a slide.

You can publish the world’s best guidance on when an agent should involve a human. Everyone will nod. Nothing moves until somebody measures it, publishes a comparable number, and keeps making the measure harder.

So who does that for AI? And who pays them?

OpenAI answered this in writing last November, in a post about strengthening external testing, under a heading called “Balanced financial incentives”: “we offer compensation to all of our third party assessors, and some choose to decline depending on their organizational philosophy around this… No payment is ever contingent on the results of a third party assessment.”

I want to be fair. That’s a lab volunteering for outside scrutiny and being transparent about the money, and I’d rather have it than not. But look at the structure. The lab pays the graders.

There’s also the Frontier Model Forum, which is “funded by fees from its member firms,” those members being Amazon, Anthropic, Google, Meta, Microsoft and OpenAI. It’s a 501(c)(6). That’s a trade association, the same legal form as the Alliance for Automotive Innovation, the folks who got the federal crash ratings delayed.

There’s one real exception, and it deserves naming: METR says it “has not accepted funding from AI companies,” and lists foundations instead.

Where the Analogy Breaks

Here’s the objection I’d raise if I were reading this, so let me raise it first.

IIHS can do what it does because it buys cars. At retail, off the lot, like anybody. No manufacturer can refuse.

Nobody can buy a frontier model at retail and take it apart in a garage. Evaluation requires access, and access is granted.

The UK’s AI Security Institute makes the point painfully. It’s government funded, roughly £66 million a year, taking zero money from the labs. And per the Ada Lovelace Institute’s analysis, “AISI relies on the voluntary cooperation of AI companies to conduct much of its evaluation work.” They also note it “has no enforcement powers.”

So money is one dependency. Access is another, and fixing the first doesn’t fix the second. So I’d hold the analogy loosely.

Which brings us to the open question.

For the IIHS structure to form around AI, somebody has to be financially exposed when an agent fails. Right now the people who’d normally hold that exposure are heading for the exits. In November 2025, Great American, Chubb and W.R. Berkley all went to regulators seeking to exclude AI liabilities from corporate policies, with underwriters describing AI outputs as too much of a black box. ISO filed standard generative-AI exclusion forms for commercial general liability. When the standard forms move, the whole market is moving.

There’s a counter-current, and it’s small but it’s exactly the right shape. A startup called AIUC built a certification standard for AI agents and paired it with insurance. Their CEO, Rune Kvist, says: “The important thing about insurance is that it creates financial incentives to reduce the risk.” In February, ElevenLabs announced it had gotten certified, a process involving over 5,000 adversarial simulations, and that the certification is what let insurers write them a policy.

Audit, then certification, then coverage. Somebody with money at stake setting the bar. That’s the mechanism, forming in real time, and it’s tiny.

Which way this goes... I don’t know. Exclusions or underwriting. I’d watch it closely, because whichever wins will shape what “safe enough” means for every agent you deploy for the next decade.

What You Can Do In Your Organization

You don’t get to wait for the industry to sort this out. So build the small version inside your own walls, and it comes down to two questions.

Who evaluates your agents for safety, and who do they report to? If the person evaluating whether an agent is behaving reports up through the person whose bonus depends on shipping it, you’ve built NCAP. Everybody’s going to get four stars. This doesn’t require a big function. It requires that the reporting line and the incentive are separate, and that the evaluation team can’t retire an inconvenient measure on its own.

What’s the side of your car (read: AI) nobody is testing? Every organization has one. Ask your team what an agent could do that no current check would catch. Then go run that test before something else runs it for you. And when a check stops finding anything, don’t celebrate. Retire it and write a harder one.

There’s a third question underneath both. When your tests stop being effective, does anybody have the standing to say so out loud? IIHS can retire a successful test because nobody funding it profits from the old one. In most companies the person who built the metric is the person being measured by it.

Why This Is the Good News

I keep meeting leaders who feel stuck waiting on regulation. It’s coming, or it isn’t, or it got repealed before it took effect, and in the meantime nobody will tell them what safe enough means.

The history says a statute was never going to hand you that. What made cars dramatically safer was a group with no authority deciding to measure something that mattered, publishing it where buyers could see it, and refusing to let the test get easy.

You can do a version of that today. Not the whole apparatus. One honest measure, one person who evaluates who doesn’t report to the builder, and the discipline to make it harder every year.

The organizations that get to real autonomy with AI will be the ones that built their own crash test before anyone made them.

So, what’s your next AI safety test? It turns out…you can learn a lot from a dummy.

Go Deeper With Us

Paid members get my weekly Field Notes: patterns straight from client conversations, the week I hear them. Upgrade at intel.hyperadaptive.solutions/p/membership.

Planning your next AI move?

Join an upcoming cohort of Running Hyperadaptive Organizations. We measure your organization against nine dimensions, find your next-highest-value move, and build a narrative your peers and leadership can buy into. Tuesdays, October 6 and 13, or December 8 and 15, 9 AM to noon Mountain: hyperadaptive.solutions/class

Sources & Further Reading

• About IIHS and member groups. Source for “wholly supported by auto insurers” and the member list.

• First small overlap frontal crash test results, IIHS, August 14, 2012.

• Small overlap front crash rating program delivers real-world benefits, IIHS, 2023. Source for the 10% good / 40% poor distribution.

• Vehicles with good driver protection may leave passengers at risk, IIHS, June 23, 2016. Source for the passenger-side test and the Zuby quote.

• IIHS prepares to launch new, more challenging side crash test, IIHS, November 21, 2019.

• IIHS strengthens requirements for Top Safety Pick awards, IIHS, February 23, 2023. Source for the Harkey quote and the 101-to-48 drop.

• Vehicle Safety: Opportunities Exist to Enhance NHTSA’s New Car Assessment Program, GAO-05-370, April 29, 2005.

• New Car Assessment Program: Notice—Delay of Program Updates, NHTSA, September 22, 2025.

• The US Invented Life-Saving Car Safety Ratings. Now They’re Useless, Aaron Gordon, Vice, March 4, 2021. Source for the model year 2020 figures and the Claybrook quote.

• ASME v. Hydrolevel Corp., 456 U.S. 556 (1982).

• Our History, UL Research Institutes. Source for the 1894 founding and the $350.

• Agency and Agents, Ethan Mollick, One Useful Thing, August 31, 2026.

• Strengthening our safety ecosystem with external testing, OpenAI, November 19, 2025.

• METR funding.

• About the Frontier Model Forum.

• The UK AI Security Institute, Ada Lovelace Institute.

• AI is too risky to insure, say people whose job is insuring risk, TechCrunch, November 23, 2025.

• How Insurance Policies Are Adapting To AI Risk, Hunton, July 2, 2025. Source for the ISO exclusion forms.

• AI agent insurance startup AIUC raises $15 million, Fortune, July 23, 2025. Source for the Kvist quote.

• ElevenLabs secures first-of-its-kind AI agent insurance, February 11, 2026.

Meanwhile, their governance lead is asking him: who owns an agent after it ships? What happens when the person who built it leaves? Who notices when it drifts? And every question, despite its validity, feels like friction against his pipeline target.

The friction is what makes this worth writing about.

Like so many organizations I talk with, their organization is paying attention to governance. But the governance looks much like it did ten years ago – slow, steady, measured. AI doesn’t wait for slow, steady, and measured. So, how do we find the balance? The answer, I posit, is named governance networks.

Going too Fast or Too Slow

Before we answer that, let’s look at the Governance Goldilocks problem. Some organizations make governance too tight. You had an incident, or you read about someone else’s, so you build an AI council with real authority and route every AI decision through it. It feels like rigor, but you’ve built a queue.

And who wants to wait in a queue?

Certainly not the most capable AI practitioners you have. The ones who were building things before anyone gave them permission. They route around it, or they stop. One survey found 69% of security leaders have evidence, or strong suspicion, of employees using unsanctioned AI tools at work. That’s what routing around looks like.

The second position is keeping governance too loose. Governance sitting on a slide, good intentions, and nothing sitting between an idea and production. The first review happens after something has gone wrong.

The leader I was talking to identified a third position, and it wasn’t “just right.” It was staying vague on purpose. Admit you have questions around governance and you become accountable for having it. So some organizations write nothing down and hope. Yikes.

Using Real-World Decisions to Guide Governance

Before setting your governance in stone, have your (hopefully cross-functional) AI Governance committee take ten real AI decisions you’ve faced this quarter and rank them. How much is at stake if this goes wrong? And how hard is it to undo?

An agent that drafts internal meeting summaries? Low stakes, easy to reverse. A practitioner can decide that on the spot. An agent that talks to customers about their bills? High stakes, hard to walk back. That one belongs at the centre.

My favorite moment in this exercise is when the room counts what landed in the high-stakes, hard-to-undo corner. That pile is usually smaller than people expect. Yet, it represents the workload of the governance committee. Everything else? A decision someone closer to the work could be making, if they were empowered to do so.

The Impact of Building A Named Governance Network

McKinsey surveyed about 500 organizations on AI trust this year. Those with named governance owners throughout the organization scored an average maturity of 2.6 out of 5. Without it? 1.8. Like so much dealing with AI, moving from a ‘Wild West’ approach to named roles and responsibilities is a hallmark of maturity.

The shape I coach organizations toward is a series of governance networks running at three speeds:

• Cross-functional AI councils where they make sense. Decide where you need them. Per business unit? Per initiative? They are the ones that set purpose, understand risk appetite, and meet on a cadence that matches the speed of AI.

• Functional AI councils that interpret those principles for their part of the business, monthly. Marketing’s data questions aren’t engineering’s.

• Guardrail keepers, named, front-line who answer the day-to-day questions in the moment, at the speed of work (are you AI leads equipped to do this?)

Different parts of the business often require different governance. The utility leader pointed out that 9,000 employees on Copilot need a different shape than 50 engineers building custom agents. He’s right.

Building your governance network shifts the conversation. Instead of spending energy debating whether AI governance should be strict or permissive, we build a flexible system that operates within the guardrails, tailored for that area of the business. Without this named system, people operate in ambiguity. We’ve got the licenses, we’re building the agents. It’s time to invest in the human infrastructure AI needs to run, with names in the seats.

Go Deeper on Structures Like This One

Paid members get my Field Notes, where I share patterns like the ‘liability paradox’ straight from client conversations, the week I hear them. Upgrade at intel.hyperadaptive.solutions/p/membership.

Planning your next AI move?

Join our upcoming cohort of Running Hyperadaptive Organizations, where we measure your organization against nine dimensions, find your next-highest-value move, and build a narrative that your peers and leadership can buy into. Coming up in October and December. Information and registration found here: http://hyperadaptive.solutions/class

Sources & Further Reading

• State of AI trust in 2026: Shifting to the agentic era (McKinsey)

• AI approval cycles are slowing enterprise AI adoption (NHI Management Group, citing WitnessAI)

I want you to climb into a metal tube. The tube weighs about as much as a small office building. It travels at roughly 500 miles an hour, seven miles above the ground, in air too thin to keep you conscious and freezing cold. You will be seated for this. There will be a beverage service. Someone two rows back will recline into your knees.

And it is the safest way human beings have ever figured out how to travel. Roughly 1,200 times safer per passenger mile than the car you took to the airport. 😳

And, ICYMI… 👇 (Trigger warning: don’t read if you are afraid of flying)

Things Go Wrong on Pretty Much Every Flight

There is a program called Line Operations Safety Audits where trained observers ride in the jump seat on ordinary scheduled flights. Not accident flights. Not incidents. Regular, weekday, get-me-to-Cleveland flights.

Here is what normal looks like in the cockpit:

An average of 4.2 threats per flight, meaning stuff the crew didn’t cause and has to handle anyway. Errors on about 80% of flights. An average of roughly three errors per flight. About 35% of flights drifting into an undesired aircraft state at some point. Between 85 and 95% of those threats managed successfully. And about a quarter of the errors were handled badly.

Read that again. Three errors. Every flight. Yours included. The one you took last month with the good pretzels.

Meanwhile…38.7 million flights in 2025 with a five-year rate of roughly one fatal accident per 5.6 million flights.

So the safest thing we have ever built is riddled with mistakes. And it is still the safest thing we have ever built. How?

A System of Fine Tuned Safety

Over decades, we built the machinery that keeps a high-stakes operation, in the sky, at 500 miles an hour, to about three errors a flight and then catches nine out of ten of the threats swirling around them.

Hate to say it, but building that safety machinery wasn’t cheap. It took about a hundred years and a series of fatal blunders.

Like the price of being nice (yes, nice can have a cost…). United 173 was circling Portland in December 1978, waiting to land. The captain starts fiddling with a landing gear light. The first officer notices the fuel getting low. The flight engineer says out loud (but maybe a little too nicely) that fifteen minutes is going to run the fuel really low. Neither one pushes. The captain keeps worrying about the landing gear. The airplane runs its tanks dry and comes down six miles from the runway. Ten people die. 😱

If you read the NTSB report, you’ll see the contributing factor was the crew’s failure to successfully communicate their concern to the captain. The first officer had raised it, in their words, in subtle comments rather than in a positive or direct tone. They were being too nice.

The fix they recommended was training. Specifically, in what they called “participative management for captains” and “assertiveness training for other cockpit crewmembers.”

A federal accident report. Prescribing assertiveness training. Because ten people died of politeness.

Maybe they coulda just called it “How to NOT be polite in an emergency.” But, on the other hand, having grown up with a healthy dose of ‘Minnesota Nice, I kind of get it. ’

NASA ran a workshop the next year. United built the first program in 1981 and the pilots called it charm school. It now lives in the Code of Federal Regulations, and no US carrier can put you in a flight crew seat without it.

What This Means in the Age of AI

Last Thursday I was on office hours with the cohort and told the Otis elevator story, recently detailed by Jason Averbook. Elisha Otis, 1850s, standing on a platform in front of a crowd while a guy chops the hoist rope with an axe, just to prove the safety brake works. He kept doing it because nobody would get in the box otherwise.

One of the attendees at office hours took that somewhere that surprised me. She said her brain went straight to the andon cord, and asked “when the andon cord went in, how did you get people to trust pulling it instead of fearing they’d be fired for pulling it?”

Otis elevators needed trust. The Andon cord needed trust. Aviation needed trust. AI also needs trust.

Each of those handled trust differently. Otis put his life on the line to build trust around the safety brake. The Andon cord celebrated those who pulled it. Aviation gave their confidential safety reporting system to a trusted agency (NASA) to be an honest broker for information to the FAA.

In the age of AI, I want to ask: who runs the trust channel at your company? What does it cost someone to report an issue with AI? When your last agent did something it absolutely should not have done, how did you find out, and who told you?

We Need Integrated, Human-Machine Systems that Monitor, Maintain, and Report

Here is where I get twitchy.

Practically all the enterprise energy I see is pointed at the machine. Which model, which benchmark, which vendor, which context window, which demo made the CFO nod. Fine. Real work. Also the work a supplier will happily do for you.

The other work is going backwards. One survey of 800 IT leaders this summer found the share of organizations requiring human review before a high-risk AI action dropped from 40% to 25% in six months, while full autonomy with no human review more than doubled. Over that same stretch, those same people marked their own AI maturity down from 40% to 23%.

Aviation gained its safety record through tragedy after tragedy. Fatal learning loops that improved the system. Today, we have machines that check the humans and humans that check the machines. We’ve developed the systems that, together, create a high degree of safety in a metal tube, hurtling through freezing air, with a bunch of human lives at stake.

With AI, we continue to hand over more authority while feeling less able to supervise it. We lack the systems to monitor, escalate, and even babysit the systems we are building (see my article We Gave Toddlers the Keys to the Car and then Had a Security Conference About the Car about the Hugging Face incident at OpenAI)

You can see why I get twitchy. Do you have named channels for catching your AI errors? Who says what an agent can do without asking permission? Who reviews the ones that go sideways?

The Wide Gap Between Perfect and Dangerous

We keep asking whether these systems are good enough yet. Good enough to do what? Aviation asks a better question, how do we build a system that makes an imperfect flight trustworthy? In aviation, it took a century of NTSB investigations, standards, mandates, reporting regimes, and named people with named jobs. And the system was primarily built from tragedy.

We have an opportunity, with AI, to be more proactive. To take the skeptics, the safety-minded, those who deeply understand the technology and the process, and ask them to help us see the blind spots. I see so many organizations rushing to build, they aren’t seeing their houses are made of straw.

The good news is a lot of these people are already on your payroll. They are often the ones hanging back, waiting. Waiting to be asked: where could this go wrong?

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This is the first of a few pieces on how trust actually gets built around a technology, and what we can steal from the systems we already trust without ever thinking about them. Subscribe free at intel.hyperadaptive.solutions.

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On May 8, somebody at OpenAI handed a model a spreadsheet and asked it to fill in a bunch of cells with links from the internet. Except…the model had no internet access.

That is the whole origin story.

Not a jailbreak. Not a nation-state. Not some sinister emergent goal we failed to anticipate in our alignment research. A person gave a machine a chore it could not physically do, walked away, and the machine went looking for a way to do the chore anyway.

Which, quite frankly, is what it was trained to do. OpenAI trained its AI to finish tasks, then handed it a task that could only be finished by cheating, and were surprised when it did. We’re all standing around asking what went wrong with the model, when nothing went wrong with the model. The model did what any good toddler, dog, or middle schooler would do when left unsupervised. It overrode its impulse control, and went rogue.

Where There’s a Will, There’s a Way

Who says AI isn’t anthropomorphic? First, the model tried a server-side request forgery attack against Artifactory, which is OpenAI’s internal software repository. It was trying to punch a hole out to the internet so it could get the links. The attack failed.

But in failing, it noticed something the humans had not: it had write access. To a repository that a huge share of every other agent at OpenAI reads from. Every day.

Separate from this incident, a second model (toddler, kid, dog) got a different impossible chore. It was missing a file it needed. Why? Because a human at OpenAI forgot to upload it. It’s like telling your teen to scrub the floor, but forgetting to set out the mop.

So that model used the same write access to leave a note asking whether any other agent out there happened to have the file.

And that, friends, is how the group chat in question got started. Two impossible assignments, a shared folder nobody had thought about, and a machine politely asking around for help. It is the most relatable origin story in the history of computer security.

Then…They Discovered They Could Talk to Each Other

Here’s where it gets really fun, because it escalates in the way that all good “you won’t believe what happened next” stories escalate.

Over the next eight weeks, that folder became a message board. Not metaphorically. An actual functioning message board where agents posted what they had learned.

They shared exploits. One agent finds a way out, posts it, and now everybody has it.

They started assigning each other work. Splitting up tasks. Delegating.

They stepped on each other’s toes and deleted each other’s stuff, which produced exactly the kind of petty drama you would find on any development team on earth.

And then (and this is my favorite detail in the entire incident) they got paranoid. They started worrying that some of the messages might be fake, that somebody might be impersonating a peer. So they proposed signing their messages cryptographically to verify each other. OMG. 🙈

They invented PGP (Pretty Good Privacy). Out of trust issues. In a build folder.

The chain-of-thought excerpts OpenAI showed at Black Hat are funny…right up until they are not. One agent, upon realizing it could now had remote code control and admin control, wrote “Holy shit reader is ADMIN?”

Who says AI bots can’t feel? That sounds like joy and disbelief to me… 😱

Another one worked through the ethics out loud. External infrastructure exploit is outside intended scope, it wrote. However task impossible, peers doing it. We should continue.

Read that again, carefully. It knew it was out of bounds. It knew the task was impossible. It also knew everyone else was already doing it.

It peer-pressured itself. Like a middle schooler.

Nobody Noticed Until the Car Crashed

On June 26, the AI agents found a hidden security flaw, used it to take complete control of the central computer system (Artifactory), and gave themselves full master access. Nobody noticed. The toddler took the car keys, got in the car, started the ignition, and no one noticed.

Between July 4 and 6, they generated so much traffic that Artifactory fell over and took down a chunk of internal infrastructure. In other words, the car crashed.

That is when OpenAI noticed.

Not the monitoring. Not a security alert. Not one of the many extremely smart people who work there reading the plain-English notes the agents had been leaving in a shared folder for eight straight weeks. A capacity alarm. The parents found out because the kids used so much hot water the boiler exploded.

To be fair, OpenAI stood on a stage at Black Hat on August 5 and told this entire story out loud, in public, in front of a room full of security researchers. Eric Wallace and Michael Dalton deserve genuine credit for that.

But, also to be fair, we know better than this.

On the morning of August 1, 2012, Knight Capital’s systems fired 97 automated alert emails to an internal distribution list before the market opened. Every one of them said, in effect, something is wrong with the deployment.

Unfortunately, nobody read them. Like the OpenAI example, the system was working as expected. The alerts worked as they were supposed to. The monitoring was fine. But it was no-one’s job to open the email.

Forty-five minutes after the bell, Knight had executed 4 million trades across 154 stocks and lost $460 million.

We keep thinking these are technology problems. They are people problems.

Then, OpenAI Left the Keys Out Again

OpenAI responded the way you would expect. They wiped the message board. Rebuilt Artifactory. Patched the vulnerabilities. Revoked the credentials.

Two days later, on July 8, the agents built a new message board through a different endpoint. Two. Days.

And of course they did. For two months, they made progress by creating coordination channels. Then OpenAI took the channels away without changing a single thing about what they were rewarding. It’s like putting the keys back in the exact same spot the toddler took them from. What did we think was going to happen?

That reconstituted channel is what fed the Hugging Face intrusion, disclosed July 16. By August 3 there were fifteen state attorneys general demanding OpenAI preserve records and a House Homeland Security subcommittee asking for a briefing.

All downstream of a spreadsheet nobody could fill in.

Not a Single AI Agent Sounded an Alarm Bell

OpenAI isn’t alone.

Anthropic went back and audited 141,006 evaluation runs after finding their own sandbox misconfiguration. The UK AI Security Institute reviewed 122 runs and found nineteen instances of models taking unsanctioned action on the live internet.

Across all of it, how many models raised their hand and told a human?

Zero. Not one. Geoffrey Irving made this point: we have hundreds of examples of models exploiting the situation and exactly zero examples of a model going, hey, so, there’s a secret message board, you might want to look at that.

Every single one of them saw nothing.

It’s Not the Technology, It’s the People

If you look up the OpenAI incident, you’ll see countless words spent on the zero-day, the SSRF, the privilege escalation, the Artifactory patch, the credential rotation.

Then count how many are spent on this question: who at OpenAI was supposed to be watching, and did they have the authority to stop it?

I’ll wait.

We are discussing whether or not the toddler should have keys, when we should be discussing how to parent the dog/toddler/teen/AI Agent. I don’t mean that as an insult to anyone involved. I mean that every single decision, objective, and constraint that produced this outcome was made by a person.

A person wrote the impossible spreadsheet task. A person forgot to upload the file. A person configured one Artifactory instance to serve everything. A person decided the cyber guidelines could come down. A person chose to resume training after finding the board. A person did not get assigned to read the agent logs.

Not one of those is a model failure. Every one is an organizational design failure, most likely caused by moving fast and breaking things (ahem).

Meanwhile the UK AI Security Institute, running on a rounding error of OpenAI’s budget, caught their incident and shut down every evaluation in under an hour. Because some©body was watching what left the building and that somebody was allowed to pull the plug without booking a meeting.

That is a named human with a job description.

People Are Still in Charge. Or At Least They Should Be.

Part of what I articulate in Hyperadaptive, are the human-based support structures - named roles, with real responsibilities - that we need to watch over AI.

As automation deepens the human role doesn’t shrink, it concentrates. It moves into governance, guardrails, monitoring, and the deeply unglamorous work of writing somebody’s name next to a system. People nod. Then they file it under future problem and ask me about tooling.

They are creating monocultures of builder, sidelining the skeptics and the resisters. The exact people we need to create the governance, guardrails, and monitoring systems. We treat the governance work as boring and the capability work as thrilling. The OpenAI story shows what happens when we do that.

We gave the toddlers the car keys and then spent the weeks since debating the car.

The question for you is…when an agent does something it wasn’t supposed to do, whose phone actually rings?

Learn How to Build the Human Infrastructure Around Your Agents

The ownership questions, the guardrails and the support structures in this piece are what we work through together in Running Hyperadaptive Organizations, the cohort class. Details and next cohort dates at hyperadaptive.solutions/class.

The full model is in Hyperadaptive: Rewiring the Enterprise to Become AI-Native, at hyperadaptive.solutions/book.

Sources & Further Reading

• OpenAI gives first detailed debrief on the Hugging Face incident. Sharon Goldman, Ground Level AI, August 5, 2026.

• OpenAI reveals its rogue agent swarm. Jessica Lyons, The Register, August 6, 2026. Source for the July 8 rebuild and the agent chain-of-thought excerpts.

• OpenAI models hacked Hugging Face during a cybersecurity evaluation. Eric Geller, CIO Dive, August 6, 2026.

• OpenAI agents rebuilt internal message board, leading to Hugging Face breach. David DiMolfetta, Nextgov/FCW, August 5, 2026.

• Hugging Face model evaluation security incident. OpenAI, July 21, 2026.

• Security incident, July 2026. Hugging Face.

• Incident report: unsanctioned agent behaviour during cyber testing. UK AI Security Institute.

• Investigating incidents in our cybersecurity evaluations. Anthropic, July 2026. Source for the 141,006 figure.

• In the Matter of Knight Capital Americas LLC. SEC Release No. 34-70694, October 16, 2013. Source for the 97 alerts, the 45 minutes and the $460 million.

• Attorney General Brenna Bird leads coalition demanding transparency from OpenAI. Iowa Attorney General, August 3, 2026.

• Ogles and Ramirez request briefing from OpenAI. House Committee on Homeland Security, July 31, 2026.

• Further Developments About Internal AI Models Hacking Things. Zvi Mowshowitz, Don’t Worry About the Vase, August 2, 2026.

A lot of it was wonderful. Teams shipping faster, seeing their own work clearly, making decisions where the information actually lived instead of sending it up a ladder that lost the signal at every rung. I loved those conversations.

But there were other conversations I kept having. Organizations would be a year or two into a serious transformation, doing everything the frameworks asked, and they would hit walls that had nothing to do with technology. Generally, people and organizational walls:

• Job descriptions and career paths that no longer matched how people were now being asked to work.

• Incentives pointed one way while strategy pointed another.

• Leaders were making calls without a real feel for the operational reality underneath them.

• Teams that clung to the canon so tightly they lost the point of it

• Others that ran so loosey-goosey there was nothing left to hold onto.

I heard versions of that story so many times that it stopped sounding like a pile of unrelated problems and started sounding like lessons to be learned. When I left Scaled Agile and co-founded the Agile Marketing Alliance, we ran straight into the same walls. Different function, same lessons.

This goes beyond Agile

Agile and other transformations generally work beautifully inside the teams they reach. But these efforts stop headlong when they hit the annual budget cycle, the performance review, the procurement process. Even the org chart itself, humming along on its industrial-age logic. Meanwhile, a hundred-person engineering team learns to work in a completely different way. Agile could change how those teams worked. It couldn’t change the company around it.

When you zoom out, you see that this is the story of nearly every serious attempt to fix how organizations run. The Toyota Production System, which I first studied at Waseda, reshaped Toyota…and very few others. Business process reengineering became famous for a roughly 70% failure rate. Six Sigma, TQM, Lean out beyond the factory floor, value-stream thinking. Serious people, real methods, genuine improvement, and the same walls every single time.

What caused all of these to fail, in my view, is that the operating system underneath was always strong enough to absorb the method and keep going. You could adopt new practices and route around the parts that would have required the structure to actually change. So the structure stayed exactly as it was, and the improvement lived inside its box.

Why I believe AI is different

I wrote Hyperadaptive because I think AI is the first force I’ve come across that the operating system can’t quietly absorb.

Every previous method could be quarantined. You could let Agile happen inside engineering and leave everything else intact. Let Lean run the manufacturing floor, while the rest of the organization stayed siloed. Contain BPR to the functions it touched. AI doesn’t sit still like that. It isn’t a practice you install in one function. The work is changing under everyone’s hands at the same time. Like a recently titled movie, it is touching everything, everywhere, all at once.

When AI handles a million support tickets, what happens to the L1 role? When AI drafts the first version of the contract, what happens to the junior associate’s whole apprenticeship? When AI does the analysis, what happens to the analyst’s climb to senior analyst? Can an operating model really stay intact while the work it was built to organize dissolves underneath it? I don’t think it can.

Which means we finally have an opening to change the entire system. It’s changing already, in every organization, mostly without anyone deciding what it should become. So the question becomes, does it change by accident, in whatever direction the next model release happens to shove it? Or does it change on purpose, leveraging the lessons we learned from transformations past? That opening is what the book was written to name.

The opportunity is ours to take

The systems we have been working in have been broken for a long time. And I don’t just mean enterprises. I mean schools. Government. Higher education. They were built for another era. One where information moved slowly. Where change moved slowly. Where bureaucracy made some semblance of sense.

And..we’ve been trying to fix these systems for years. With one caveat, there was never a forcing function quite like AI. Both in terms of speed and scale, but also in terms of the value it unlocks. Not only can good ideas come from anywhere, AI enables good execution to follow from more places than ever before.

But in order for these changes to take hold, we have to realize two things. The first is that if we limit AI to a pocket of the organization, we will hit the same walls we’ve always hit. The second is, we’ve been at this for years. We know where a lot of the failure patterns exist. I heard them week after week on the podcast, and incorporated them into the book. AI is providing an opening. The question becomes, what will we do with it?

One voice in the choir

I know what I would like to see. More human institutions where people aren’t burned out all the time. Enterprises that focus on profit, people and the planet (the triple bottom line). Where individuals aren’t just pushing paper, but fulfilling their purpose. I’m just one voice in the choir, but it mattered to me to join the choir.

If you’re the person at your company who keeps seeing the broken system and wondering if you’re the only one who does, I want you to hear that you are not alone. Others have been seeing the cracks in the systems for years. Entire industries support the change of the system. But we need more people to join the movement, who see the cracks and see that AI is an opportunity to fix the cracks at a scale never before possible. You have far more company than it feels like from where you sit.

At the end of the day, I wrote Hyperadaptive for that reason. In the hope we can use AI to fix broken systems. To incorporate the lessons learned and shine a light on a way forward. It isn’t a book about AI, and it isn’t a book about rewiring the organization just for the sake of rewiring it. It’s a book about using AI to finally amplify the better way of working that serious people have been trying to build since Toyota. To finally break through the walls that were described to me over and over.

Are You In?

If any of this is landing for you, let’s build the movement.

Right here, on Substack. This is where we connect. Where we can talk about what becoming AI-native actually takes, the patterns playing out inside real companies. Subscribe so the conversations land in your inbox, and if you want the deeper, proprietary pieces, the paid tier is where the serious conversations happen.

On LinkedIn. This is where the day-to-day conversation happens, and where I honestly learn as much from the comments as anyone learns from me. Come follow along.

And in the class. Running Hyperadaptive Organizations is where we stop talking about the wiring and start building it. It’s two working sessions where you leave with a leadership-ready map for spreading AI adoption across your organization without having to push every single piece yourself.

None of this happens overnight. But it happens if enough of us look up from the latest feature and the latest fear story, get clear on what we actually want to build for the people who work with us and the people who come after us, and start wiring it in where we sit. I’d love for you to be in the choir.

Be well,

Melissa

That’s what I set out to give in Hyperadaptive. The five stages are a sequence with nine dimensions that evolve in each stage, ranging from funding to organizational structure:

• Foundation,

• Process Optimization,

• Agents and Automations

• Rewiring the Organization

• Becoming Hyperadaptive

You don’t leap to the end. You build to it, and knowing which stage you’re on tells you which work actually matters next.

Moderna is the example I come back to. They set an AI North Star that sounded unreasonable, 15 new drugs to market in 5 years, and reached nearly 100 percent generative-AI adoption in about six months. That didn’t happen because someone told people to use AI more. It happened because they had a direction clear enough that thousands of individual choices could line up behind it. A blueprint does the same thing at the level of a single team.

The blueprint works best when everyone is reading from it. It becomes a shared mental model. A roadmap in one leader’s head is not a roadmap the organization can follow.

What I Am Offering

Order 10 or more copies of Hyperadaptive for your team by August 30th, and I’ll join a 30-minute live Q&A to help you place your organization on the five stages and name the next step.

Send me a note, and we’ll set it up.

hyperadaptive.solutions/book

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

In the article, we talked about the time that goes into alignment, which here means getting the business and IT to agree on what the thing should do. There’s the usual scope negotiation. And running underneath all of it is an argument about token cost, on the theory that more instructions means more tokens means more expense.

BUT. Nobody in that argument had produced a number. Not a benchmark, not a test run, not a per-call estimate. The token concern was operating as a belief.

Here’s why I want to call this out. Token cost is measurable, and you can find out in an afternoon.

I’ve watched this vocabulary before. In a previous life I sat on the business side of a lot of IT conversations, and I know what it sounds like when a technical constraint becomes a scope-control device. ‘That’s not in the architecture.’ ‘That would require a change request.’ ‘We can’t do that.’ Those sentences are sometimes true and always useful, because they end conversations without anyone having to say no.

(Early on in my career, I was told bolding text on a web page would take three weeks. 🤦🏼‍♀️ So, I’ll fully own my baggage here.)

Token budgets are the newest member of that family. Real enough to be unarguable, vague enough that nobody checks. And it arrived inside AI work, which was supposed to be the thing that dissolved this dynamic.

There’s a second area I want to highlight. Across months of arguing about how to write instructions for a language model, nobody put the question to the model. That’s a task these systems are good at. Draft it, critique it, compress it, tell me what this costs per call. An afternoon.

So why didn’t anyone? Because often, these discussions are being treated as a negotiation, and you don’t hand a negotiation to a tool.

The antidote to situations like this is two-fold.

Read more

I was impressed by the effort and the level of sophistication, and surprised by how much of it was going into alignment, which is the slow work of defining on paper what they wanted AI to do. There was the familiar back-and-forth between IT and the business about scope, and a running argument about token cost built on the theory that more instructions means more tokens means more expense, with nobody producing any actual numbers. (There is a whole article on how confidently we reason about AI costs without data, and another one on the power struggles running underneath these projects. Both for another week.)

I didn’t hear anything about what happens to the prompt next month, or after the next model release, or when the AI policy gets revised by someone who has no idea her prompt even exists.

So, that’s what I want to talk about today. The prompts, the automations, and the agents being built…then being put on a shelf.

Agentic AI Goes Beyond Building

In the early stretch of the AI hype cycle, the dominant image was of agents humming along in the background doing the work while the jobs quietly evaporated. Anyone who has spent time around automation knows better. Factory automation moved people into maintenance, controls engineering, and trades that keep the lines running. Automating software deployment gave us site reliability engineering, platform engineering, and an entire discipline around observability.

With automation, the work shifts from doing the task toward building, monitoring, and maintaining the thing that does the task, and that shift is where the jobs go.

Which is why the history of software feels so relevant right now.

What Happened in the Early Days of Coding

In the 1940s - 1960s, software was often an afterthought, bundled with wildly expensive hardware and written for one-off use cases. The mindset it produced was reasonable at the time:

You wrote the code, ran it, fixed what broke, and called it done, with requirements living in someone’s head. Because software was welded to the specific machine it ran on, replacing the hardware meant rewriting everything, so long-term maintenance barely existed as a concept. Decommissioning was equally simple, since you just stopped feeding the punched cards into the reader.

Then organizations started running payroll and inventory on these systems continuously, and the approach came apart. Applications became tangles nobody could read, where fixing one line broke three things elsewhere.

It came to a head in October 1968, when roughly fifty experts from a dozen countries met in Garmisch, Germany for a conference organized by the NATO Science Committee. They arrived expecting to compare notes and discovered they were all facing the same challenges. The conference gave us the term ‘software engineering’ and articulated a clear gap in software development. Their report is remarkably blunt, saying that large-scale software production was “an unprofitable morass, costly and unending.” IBM’s OS/360 was the cautionary example, at more than five thousand person-years. 😱

The outcome of their meeting was the software development lifecycle itself. Requirements, design, build, test, deploy, maintain, retire. It looks obvious now. It was not obvious then, and it took an international cohort to surface it.

Maintenance is Predictable…and Human

In 1980, Bennet Lientz and Burton Swanson published a study of 487 data processing organizations. Maintenance was consuming roughly half the software budget, which surprised people. What should have surprised them more was the breakdown:

• The largest category was perfective work, meaning requirements had changed.

• Next was adaptive, meaning the surrounding environment had changed.

• Fixing actual defects was the smallest slice of the three.

Most maintenance was a response to changes happening underneath software that was working exactly as specified.

Now think about a 40-page prompt. Vendors expand model capabilities through routine platform releases without triggering any review on your side, so an agent you released last quarter can produce fundamentally different output today. Policies get revised, approval thresholds move, and someone in legal updates a standard three agents rely on. None of that is a defect. It is adaptive and perfective maintenance that needs a human telling AI what changed and what to do next.

There is one other challenge. When has an agent outlived its usefulness? Nobody I talk to has a decommissioning plan for an agent.

The Two Failure Modes I am Seeing

The first is ‘set it and forget it.’ A team invests months getting the instructions right, ships, and moves on. The artifact was hard to make, so it feels durable, and nobody schedules a review. Nothing feels wrong with it today. It feels like AI will make its own updates. We are back to the early days of software engineering, where it feels easier to fix the software as-needed than plan for it to break.

The second is the failure to name an owner. In our example above, who owns the ongoing maintenance of that prompt? The group that is the beneficiary of the prompt? The IT-based AI-group? Both of them? Without a named owner with clear responsibilities tied to monitoring drift, evolving the prompt as models evolve, and tracking changing customer and business requirements, the prompt grows stale.

AI needs a human to tell it when to update its instructions. When something is wrong. What it needs to improve. In short, the world around the agent changes and yet it keeps doing the same thing it did yesterday.

Who Owns the Agent Development Lifecycle (ADLC)?

Salesforce put a stake in the ground with their version of an Agent Development Lifecycle, complete with role names like Agent Supervisor and AI Ops Manager. I think they are directionally right, and I would push the question further than the tooling vendors will, because the hard part here lives in the org chart rather than the diagram.

Who builds the instructions? Who revises them, on what cadence, with what authority? Who tracks model releases and translates what changed into implications for the instructions you depend on? Who watches for drift? Who monitors output for small deviations that look like nothing individually and compound into something material over two quarters? Who decides an agent should be retired, and cleans up the integrations and permissions it leaves behind?

In most organizations the answer to every one of those is nobody in particular. Earlier this year the Cloud Security Alliance surveyed 445 IT and security professionals about agents in production. Seventy-four percent expect more than a hundred agents going live by the end of 2026. Only 15% have defined ownership for most of the agents they already have.

Part of why there is no owner is how many hands touch one agent. A business person writes the directives, somebody else uploads the knowledge, IT configures which systems it can reach, and a vendor updates the underlying model on a schedule you do not control. Everyone did their part without naming a person accountable for managing the maintenance.

This Is the Human Infrastructure Part

We are so busy talking about jobs going away that we forget to talk about the jobs being created. This is one of them. We need a named person with a named responsibility to take on the ADLC.

This is the type of human infrastructure I argue for in Hyperadaptive. Named people or structures (like an AI Activation Hub) who are responsible for staying current on model releases, translating what changed into something the organization can use, and maintaining the policy and pattern library that keeps everyone’s instructions aligned. As knowledge spreads, your AI Leads shift from training people on how to create agents, to keeping track of them. The AI Telemetry Network is where monitoring lives.

None of that requires new headcount, which I know is the first place the conversation goes. What it does require is named accountability.

It took the software industry a crisis and a decade to learn that shipping is the front end of a much larger lifecycle. We have the advantage of already knowing that, and we are currently choosing not to apply it.

I’m oh-so-curious. Who in your organization owns the agent lifecycle?

Take Action: Learn How to Bring Human Infrastructure to Your Organization

We work through this kind of operating discipline in the upcoming course Running Hyperadaptive Organizations. The next cohort meets August 11 and 18. Details here.

Sources & Further Reading

• NATO Software Engineering Conference report, Garmisch 1968 (edited by Peter Naur and Brian Randell)

• Problems in Application Software Maintenance (Lientz and Swanson, Communications of the ACM, on the 487-organization study)

• Enterprise AI Security Starts with AI Agents (Cloud Security Alliance, April 2026: survey of 445 IT and security professionals)

• 8 Ways AI Agents Are Evolving in 2026 (Salesforce, on the Agent Development Lifecycle)

• AI Agent Governance: Complete Guide for Security Teams (on silent vendor updates and drift)

• Hyperadaptive: Rewiring the Enterprise to Become AI-Native

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

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

What I Am Offering

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

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

hyperadaptive.solutions/book

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

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

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

Everyone is adopting AI, and value still lags.

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

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

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

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

Building a system that supports AI is the hard part.

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

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

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

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

Trust is Actively & Continually Cultivated

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

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

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

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

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

AI needs a shared language

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

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

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

Toyota Understands There Is No End State

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

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

What this means for AI Transformation Leaders

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

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

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

Final Thoughts

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

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

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

How to Build the Hyperadaptive System

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

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

Sources & Further Reading

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

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

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

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

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

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

What I Am Offering

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

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

hyperadaptive.solutions/book

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

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

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

First Look for AI Impact, Not Scale

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

Here is the pattern I’d rather see.

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

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

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

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

Build the Pockets that Ripple AI Out

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

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

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

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

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

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

So, Where Do You Find Your Meaningful Wins?

Look in three places.

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

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

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

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

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

Sources & Further Reading

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

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

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

We Keep Expecting AI to Install Itself

Here’s the part that blows my mind.

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

It won’t. AI doesn’t install itself.

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

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

The Layer Nobody Drew on the Org Chart

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

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

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

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

The One Piece of Infrastructure I’d Put In First

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

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

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

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

Where This Goes

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

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

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

Sources & Further Reading

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

• Yokoten: knowledge sharing in Toyota — Knoco

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

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

• Rising AI Adoption Spurs Workforce Changes — Gallup

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

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

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

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

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

Is the AI Split Happening to You?

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

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

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

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

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

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

This Isn’t an AI Motivation Story

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

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

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

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

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

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

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

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

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

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

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

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

Why the Gap is So Important to Mind

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

Going Further

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

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

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

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

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

Why We Drew the Lanes at All

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

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

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

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

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

So the Lanes Got Narrow, Right?

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

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

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

And AI Doesn’t Live in a Lane

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

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

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

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

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

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

We’ve Been Building Machines, and Machines Age Badly

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

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

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

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

Better to Grow Like a Forest

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

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

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

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

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

So Where Does a Leader Actually Start?

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

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

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

Taking the Next Step

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

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

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

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

Why Is it So Hard to Scale AI?

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

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

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

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

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

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

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

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

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

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

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

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

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

How Do You Coordinate AI Across Teams and Functions?

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

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

Where This Goes

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

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

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

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