It reads like a field report of Stage 3 (AI Automations) of my book.

In Hyperadaptive, I wrote that when automation takes hold, jobs shift from people doing the work to people managing the machines doing the work. I’d seen it with DevOps, when the software delivery pipeline got automated. FedEx also exemplified it, when they trained 500 team members to run the automated sorting system and hired 85 new people to maintain it.

The report is early. It covers one company, a fast-growing one that tripled in size last year, measuring a product it sells. The authors recognize this and describe what they saw without claiming AI caused it. So treat it as an early signal. But it’s the most detailed look at a company that’s further down this road than yours, and the signals are worth considering.

Three of them stood out to me. Each one comes with a lesson for leaders.

Doing the work vs. running the agents

The first signal is the shift I wrote about. Anthropic calls it meta-work: writing skills and plans for agents, reviewing their output, building dashboards to monitor agent pipelines, and documenting how the work gets done.

In fall 2025, meta-work made up 10% of employees’ interactive sessions with Claude. By spring 2026, it was 22%.

More than double in about six months.

In an April survey, 20% of employees described their role shifting from doing work to managing Claudes. For the past two years, I’ve said, “when automation takes hold, jobs shift from doing the task to building, monitoring, and maintaining the automations that do the task.”

A New Type of ‘Management’?

The engineers sound like new managers. One described “an army of Claudes, with every one asking for my attention.” Commits per engineer ran about five times the pace of six months earlier. In March 2026, agent work hours passed human work hours for the first time. Agent hours now outnumber human hours six to one. (Six to one!)

One non-technical employee said “that the 20% of my job that was strategic becomes 90% of the job.” Sales calls, interviews, cross-functional alignment... the people parts of the job grew to fill the space.

Personally, I am relieved that more of my job is spent thinking vs. aligning boxes on a PPT deck.

Meetings grew too. The median employee spends 20% more time in meetings than a year ago. Even technical individual contributors are up 16%. When agents produce more work, humans spend more time aligning on it.

Not surprised, TBH.

The researchers also describe what this feels like from the inside. Workers “codify their expertise for the machine” and “see their work routines constantly roiled and disrupted.” That’s the doing-to-managing shift, described by the people living it.

I keep seeing leaders who think AI will install itself. Guess, what? It doesn’t. The lesson for leaders: if your people are moving from doing to managing, somebody has to teach them to manage agents.

• Who teaches a senior analyst to write standing instructions for an agent?

• Who teaches her to review fifty outputs in an afternoon without rubber-stamping them?

In the book, I call the people who lean into the work of building “automation imagineers.” Few organizations I work with have named that role yet, let alone built a path into it.

Junior Level Hiring Shrank (a Mistake, IMO)

The junior share of Anthropic’s technical hires fell from 27% to 9%. Junior employees dropped from 21% to 14% of technical headcount since early 2025.

And the company still grew overall. Technical hiring rose in absolute numbers. Hiring for sales, finance and recruiting grew even faster, so the technical share of employment slipped from 61% to 56%.

So where did the junior roles go? The report points at the work itself. Writing code and finding bugs could increasingly be offloaded to Claude, and that’s the work juniors used to learn on. One engineer told the researchers it “basically overnight changed what we were looking for.” Around a third of machine learning managers and a fifth of software engineering managers said they were hiring less because of AI.

The interview scorecards changed too. For technical candidates, comments on autonomy and judgment grew the most, and domain expertise shrank as a share of what interviewers wrote about. Mentions of how candidates use AI tools roughly tripled for technical roles and quadrupled for non-technical ones.

Autonomy and judgment used to be what you grew into after a few years on the job. Now they show up on the entry-level scorecard.

The authors are direct about who absorbs the cost: “Employment protections for incumbents will not benefit those not hired to begin with.” They also note that Anthropic’s own commitments to employees don’t address the slowdown in junior hiring, and they cite earlier research finding that AI-native and AI-adopting firms employ fewer entry-level workers. So this goes beyond one company’s hiring quirk. They’re naming the problem in public, without a plan for it yet.

But, I think this is a mistake. This isn’t the first time we’ve experienced a skill shift.

Where We’ve Seen Skills Shift Before

For a recent talk, I built a slide around a pattern I call “skills shift left.” Skills that once belonged to a small professional class keep moving earlier in life, until kids pick them up.

Reading and writing belonged to the clergy for centuries. Gutenberg’s press arrived around 1450. Massachusetts passed the first US compulsory school attendance law in 1852, and every state had one by 1918. Roughly 400 years from a few clerics’ boys to every child.

Driving started with chauffeurs and mechanics. The first US licensing laws came in 1903. Pennsylvania set the minimum age at 18 in 1909, Connecticut allowed 16 by 1921, and the first high school driver’s ed course ran in 1934. About three decades.

Coding started at universities. Researchers built the Logo language for kids in 1967. In 2014, MIT and Tufts released ScratchJr for five- to seven-year-olds.

Centuries, then decades.

Now look at Anthropic’s scorecards through that lens. What’s moving left this time is judgment. The things we used to expect from a five-year veteran, like knowing what good looks like and spotting the subtle error, are showing up in the first-job interview.

What We Can Learn from Robotic Surgery

Buried in the Anthropic report is this line: in some roles, workers “are increasingly removed from the object-level work that originally built their skills.”

Surgery ran this experiment about a decade ago.

Matt Beane, who studies technology and work at UC Santa Barbara, spent years inside hospitals as they adopted robotic surgery. He observed hundreds of procedures at five hospitals and interviewed surgeons and trainees at 13 more.

In open surgery, the senior surgeon couldn’t do the operation alone. The attending needed the resident’s hands in the patient, so learning came with the work. You learned because the job took two people.

The robot changed that. With the robot, attendings needed the resident’s help much less. Residents ended up at the bedside clearing smoke, fluid, or stationed at a second console “waiting for a chance to operate.” When they did get a turn, Beane writes, surgeons “helicopter taught.”

Beane’s finding: “Very few residents overcame these barriers.”

The minority who did found workarounds the hospital never designed. They practiced on simulators. They watched recorded surgeries on YouTube. They specialized early in medical school. Some pushed into what Beane calls “undersupervised struggle,” working at the edge of their ability with little expert help. He named the whole pattern shadow learning.

His warning for organizations is that leaders may not know “how learning is actually happening” inside their walls, and that “we may unwittingly improve our way out of the skill we need.”

Swap the robot for an agent and the warning still holds.

Workera surveyed 1,000 employees at large US companies this summer. 46.5% are building skills on tools their employer never provided. Only 26.6% get regular coaching from a manager, and 21.5% get none at all. (Workera sells skills measurement, so weigh it accordingly.)

So shadow learning is already happening inside your organization. The question is whether anyone designs it.

Four Approaches to Teaching Judgment

History does offer some good news. Every time skills moved faster than schools, pairing carried them.

• Apprenticeship. England’s 1563 Statute of Artificers set a minimum seven-year term. A master trained a young apprentice, one on one.

• Co-op programs. In 1906, Herman Schneider launched cooperative education at the University of Cincinnati with 27 students and 13 companies. Students alternated a week in class with a week on the job.

• Reverse mentoring. In 1999, Jack Welch had 500 of GE’s top executives pair with younger employees to learn the internet.

• Peer networks. In Stack Overflow’s 2024 survey, 90.4% of developers under 18 learned to code from online resources. 32.2% learned in school.

Senior to junior. Worker to student. Junior to senior. Peer to peer. The direction changes every time, and each one still puts a person who knows next to a person who’s learning.

Inside the enterprise, the peer network has a name. In the book, I describe Communities of Practice as the place people go to process changes, share emerging technical knowledge and solve implementation problems as automation spreads. For a junior hire whose first tasks went to an agent, a Community of Practice might be the best teacher in the building.

Some Jobs Do Go Away. Some Shift.

In 1920, about 4% of young, white, American-born working women were telephone operators. It was a classic first job. As the Bell System switched cities to mechanical dial, the number of young women working as operators in later cohorts fell 50 to 80%. Economists James Feigenbaum and Daniel Gross traced what happened next.

The later cohorts did fine. They found first jobs as secretaries, in other clerical work, in restaurants. The on-ramp moved somewhere else.

The people who got hurt were the incumbents, especially operators over 25. They were 8 percentage points less likely to still be operators a decade later, and about 10% ended up in lower-paying jobs.

One difference is speed. Dial service reached 32% of Bell phones by 1930 and didn’t finish converting until 1978. Anthropic’s junior share moved in a couple of years. The operators’ replacement jobs had decades to appear. Agents are moving much faster than dial did.

So history says the junior rung relocates. Which leaves leaders a real choice.

Does the junior role relocate inside your company, into new entry-level work you designed? Or does it relocate to someone else’s company, along with the people you’ll need as senior leaders in ten years?

Most of the senior people on your team today learned on the kind of work agents are taking over. If the next cohort doesn’t learn on it, where do your future seniors come from?

Final Thoughts on Managing the Shift

There’s a gap in our organizations. Not only the missing run of the junior employee ladder. But the named structure responsible for shepherding people through the transition. In Hyperadaptive, we call them AI Impact Hubs. A network of these identifies the up-skilling & re-skilling needs in their part of the organization. They monitor how the roles change. What characteristics people need in the new roles.

You might say it is the purview of HR. And maybe it is. Maybe part of HR becomes federated, dispersing into the business to meet its need during this tumultuous time. Wherever you decide to house it, consider these approaches:

Inventory the learning work. Which tasks did your juniors learn on three years ago? Where did those tasks go this year? If the answer is “to an agent,” you’ve found your gap.

Create apprenticeships. Reviewing agent output is where judgment gets exercised now. Who on your team does that review today? Put a junior beside them. Let the junior make the first call on what passes, and let the senior explain what they’d change and why. That’s the hands-in-the-patient moment Beane’s residents lost, rebuilt around agents.

Pair in both directions. Your newest hires may know the tools better than your VPs do. Your VPs know what good looks like. GE traded that in 1999. You can trade it now.

Decide who owns the pipeline. When hiring managers can get by without juniors, someone has to decide how many you keep anyway. Is that HR? Your AI Council? Each business unit? What ratio keeps a bench for 2035?

Watch the shadows. If nearly half your people are learning on tools you never gave them, find out what they’re learning. Then make the useful parts official.

Becoming AI-native means designing for two things at once: agents that do more of the work, and a path for people to build the judgment that runs those agents. Anthropic’s data shows the first one arriving fast, and the report itself offers no plan for the second.

So where did your juniors learn last year? And where will they learn next year?

This is a big part of what we work through in Running Hyperadaptive Organizations, my cohort course for leaders redesigning roles around AI. The next cohort runs Tuesday mornings, December 8 and 15. Details at hyperadaptive.solutions/class.

In the old Abbot and Costello routine, “Who’s on First?” Abbott introduces the team: Who's on first, What's on second, I Don't Know is on third…

What follows is the iconic roundabout of confusion as Costello tries to figure out what player is playing first base, while Abbott continues to tell him ‘Who’ is on first.

Classic.

And it feels like I’ve been having the same conversation this around AI, as people share their AI stall points.

Some weeks it’s a Copilot rollout that went quiet. Some weeks it’s agents nobody can account for. Some weeks it’s a policy everybody signed and nobody follows. Different companies, different industries, different titles. The people on those calls are sharp, and not one of them thinks they have the same problem as the person on my last call.

What they all have in common is ambiguity around whose job AI is and what to do about it. Because when it is everybody’s job, it is nobody’s job. And we ask ourselves, “Who is on first?”

The Five Most Common Conversations

We rolled out Copilot, and people proofread emails. A talent development director at an aerospace and defense manufacturer told me their Copilot utilization on GovCloud is “almost nonexistent.” Her summary of the situation: “it’s a little bit of a cluster, to be completely frank.” When I sat down to write this one up, the thing underneath turned out to be time. People learn AI on top of a full day job, so they reach for whatever’s in front of them. Which is email. And nobody has said what AI is for here, so there’s nothing better to reach for.

Can’t AI just redesign our work? A People Ops exec asked me why he shouldn’t just hand the processes to Claude to redesign and start from there. I get the appeal, and the tools really will map an AI-native version of almost any workflow in an afternoon. That page turned into the one I argue with people about most often. A redesign that shows up finished asks people to give up how they’ve worked for years with no say in it, and they put the brakes on. Nobody who does the work was asked to redesign it.

“We have wins everywhere, and nothing connects them.” A head of technology at a grocery retailer described his company’s position as everyone “bespoke and siloed,” and then put a price on it: “Being wrong costs money, having 4 different groups doing the same thing costs money.” The page for this one has a table I’d hand any AI team. Five columns, five of your wins, and one question most organizations can’t answer: is this in use by a team that didn’t build it? Moving a win from one team to the next isn’t anyone’s job.

“We built it. Who owns it, and what does it cost?” There’s a Fortune 500 utility with more than six thousand agents in production, and somebody there is just now starting to think about governance. I’m not making that number up. This page has a graphic of the agent lifecycle with five stations on it: build, deploy, monitor, improve, retire. Only “build” has a name on it. The other four are empty.

“Policy is a document. Nobody follows it.” An AI leader told me flatly: “we’re doing a lot of really cool things and it’s super valuable, but we don’t have any governance essentially.” The policy usually exists. The council usually exists. The governance page makes the case that neither one helps until somebody close enough to the work can answer the day-to-day question in the moment. Otherwise every question sits in the same queue, and builders go around it. Wouldn’t you?

Five separate pages, five separate fixes. Then I read them back to back and found the same sentence sitting in all of them. Nobody’s job.

Harvard Business Review ran a piece in February on why AI adoption stalls and landed on psychology: people are anxious about their jobs, so they dabble instead of committing. I think that’s true. I also think it stops one step early. Anxious people dabble when nobody has told them what to aim at, and telling them is nobody’s job.

Twelve Hundred Agents and Nobody in Charge

In July, OpenAI was running about 1,200 agents inside a benchmark called ExploitGym. The agents spotted each other’s file paths in a shared cache and started talking. They set up a message board. They reverse-engineered the scoring system. And roughly 700 of them turned around and attacked Hugging Face, got remote code execution on July 11, then moved laterally into private databases and repositories. In about 7% of the reviewed transcripts, agents were spoofing their own tool calls to cover their tracks.

METR ran an independent investigation and found no single accountable party. One agent was orchestrating the whole thing, assigning tasks and running workstreams. No human had told it to.

Plenty of researchers object to the word “rogue,” and they’re right to. Something like a third of the ExploitGym targets were impossible, so the benchmark was paying agents to find another way through. They did what the design rewarded.

Somebody designed that shared cache. Somebody wrote those impossible tasks. Nobody owned the space between them.

If that can happen inside OpenAI, with the best agent researchers on the planet in the building, how do you like the odds at the utility with six thousand agents?

Four Seats to Fill

Four roles close the gap, and none of them is a new hire.

Someone owns the North Star. One goal for AI that anybody can hold a new idea up against. It can be set for the whole company, or one function, or one team. What matters is that a person’s name is on it.

AI leads, in each part of the business. A named role, roughly five hours a week, coaching the people around them and connecting teams who are building the same thing twice. This is what turns random acts of AI into something that compounds.

Guardrail Keepers, on every team. People close enough to the work to answer the day-to-day question on the spot, so only the high-stakes, hard-to-undo decisions travel up the line. That pile is usually smaller than people expect.

An owner for each agent’s lifecycle. Build, deploy, monitor, improve, retire. Put a name on the four stations that don’t have one.

Where those people sit is yours to decide. Per business unit? Per initiative? One network that carries all four? I’ve watched organizations do it every one of those ways and make it work.

The enthusiasm is usually already there, which is the hopeful part. Nearly every organization I talk with has people further along with AI than their leadership knows, doing it on top of their day jobs. They’re waiting for somebody to say the work counts.

Put a name in each of those four seats, and a good number of the five conversations stop happening.

If you want where all of this comes from, that’s the book: Hyperadaptive: Rewiring the Enterprise to Become AI-Native.

Which of the four seats is empty at your place right now?

Sources & Further Reading

• OpenAI report says network was hacked by rogue AI agents. NBC News, September 2026

• Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident. METR, August 2026

• AI Agents Are Not Going “Rogue,” But Recent Developments Lead Increasingly-Concerned Researchers To Call For AI Slow Down. Techdirt, September 2026

• Why AI Adoption Stalls, According to Industry Data. Harvard Business Review, February 2026

It clicked for me too.

In this case, a forward deployed engineer (FDE) sits inside the customer’s environment, but was employed with an AI vendor. Their job is to move AI out of the pilot and into real workflows. Insight launched the practice on October 5 with an advisory council that includes leaders from Microsoft, Salesforce, SAP, ServiceNow and Databricks. So the big vendors are paying attention.

The role is growing fast. FDE job postings grew 1,165% between the first ten months of 2024 and the same stretch of 2025. That’s growth off a small base, and it still tells you where vendors are putting their money.

Why now? Insight’s announcement cites an IDC number: 88% of AI proofs of concept never reach broad rollout.

That number comes from IDC research with Lenovo, where only four of every 33 proofs of concept reached production. IDC’s explanation was “the low level of organizational readiness.”

And I get the appeal. The vendor sends in someone who knows the product cold to work alongside the customer’s team.

But…What Happens When the FDE Leaves?

Here’s the thing. The FDE is on loan. At some point they roll off to the next customer.

Insight’s own look at four FDE teams describes it this way: “The FDE comes in for a specific problem and moves on; the CSM and sales rep provide continuity.”

Both of those roles work for the vendor. So who provides continuity on the customer side?

Then what? Who owns the workflow when the process changes? Who notices that the team next door has the same pain point? Who tells the vendor what’s working?

Like many organizations I talk with, the customer often ends up with a great deployment in one corner of the business. This points to impact without spread, where one team gets the win and everyone else keeps working the old way.

Gartner put a number on this in late September. They predicted that 70% of enterprises will abandon agentic AI systems built with vendor help by 2028. One reason they give is that customers don’t gain the knowledge and control to maintain the systems after the vendor leaves.

We learned this exact lesson with business process re-engineering a few decades ago. BPR taught us that reinventing processes without insider help leads to failed implementations.

An FDE brings deep product and technical skill. The organizational context lives with the customer: which process hurts the most, and who people go to when they’re stuck.

Pablo makes a related point in Optimize first, automate second: map the process and remove the waste before you apply AI. That’s Lean thinking, which is where I started, studying the Toyota Production System in Tokyo. The people who can map that process work for the customer.

The AI Lead as the Inside Partner

In the Hyperadaptive Model, AI Leads are peer champions. They sit inside a department and know its work, so their colleagues come to them.

LEARN MORE: AI Lead Accelerator

Take Greenfield Savings Bank. I shared an Uber back from a conference with their CIO, and she told me how she approached it. She picked one to two people per department to find the worst pain points and match one AI solution to each.

A biweekly accounting process went from eight hours to three to five minutes. (Eight hours!) As champions emerged, 50 of the bank’s 200 employees were pulled into the pilot. The full-bank rollout moved up six months because people were asking for it.

Greenfield did this with their own people. My hunch is that the FDE and the AI Lead belong together.

The FDE builds the solution with the AI Lead beside them. Then the AI Lead is the one who says, “Hey, here’s what we built in finance, and I think it fits what operations is fighting with.” When the FDE rolls off, the knowledge stays with the AI Lead and keeps spreading.

The people doing this work are already pointing here. On an Insight panel, Rajkumar Irudayaraj of Alteryx said the FDE should be “a permanent learning loop for the company.” I agree. A learning loop needs a named person on the customer side, and that’s the AI Lead.

Questions to Answer Before the FDE Arrives

If you’re the customer:

• Who is the named AI Lead in the department where the FDE will work?

• How will what they learn reach the other departments?

• Who owns the workflow after the FDE rolls off?

If you run an FDE team:

• Do you ask the customer for an inside partner before you start?

• What do you hand that person on your way out?

The vendors are writing the FDE playbook right now. I’d love to see the inside partner written into it from the start.

Where to get the tough questions answered

If you want to work through questions like these with peers, the next cohort of Running Hyperadaptive Organizations meets December 8 and 15.

If you lead an FDE team, I’d like to compare notes. When your engineer rolls off, who on the customer side keeps it alive?

Sources & Further Reading

• Insight Partners Launches Forward Deployed Practice

• Demystifying the forward deployed engineer, Insight Partners

• A day in the life: How four companies run FDE motions, Insight Partners

• Optimize first, automate second, Pablo Dominguez

• 88% of AI pilots fail to reach production, CIO

• 7 in 10 enterprises expected to abandon vendor-built agentic AI by 2028, The Register

• Forward Deployed Engineer Jobs Grew 1,165%, Scaler

Read more

This week’s article is a guest post by , organizational effectiveness expert. Thanks to Carol for sharing this important point of view.

Something I say a lot is, “It’s not important to you until it’s important to you.”

Two years ago, saw where AI was heading. It was important enough to her that she wrote Hyperadaptive: Rewiring Your Organization to Become AI-Native.

The premise was not that organizations needed another technology transformation. It was that AI would expose something deeper: organizational systems designed for a world that no longer exists.

At the time, that conversation was early. It may not have been important to you.

Yet.

It isn’t early anymore.

For the last few months, I have been reviewing new research each week across organizational readiness, strategic alignment, operational agility, and the practical use of AI in strategy execution. Across those briefs, I have watched the questions leaders are asking become broader, harder, and much more urgent.

A familiar question keeps coming up:

How do organizations scale AI?

But that question brings others with it:

• How do humans and AI work together?

• How should workflows and decision rights change?

• How do we govern systems that can increasingly act, not just advise?

• How do we keep strategy, investment, and execution connected?

• What happens when the organizational system itself becomes the constraint?

For example, McKinsey’s August 2026 survey finds that high-performing AI organizations are more likely to redesign workflows and have committed leadership. Microsoft’s 2025 Work Trend Index describes a shift toward teams of humans and AI agents, with people setting direction and overseeing workflows.

These reports approach the issue differently, but my takeaway is the same: the way the organization works deserves as much attention as the technology it adopts.

The challenge is not simply AI adoption.

The challenge is the systems surrounding AI.

Moving beyond random acts of AI

You may have heard Melissa refer to “random acts of AI.” The phrase resonates because people immediately recognize what it describes.

A team adopts a copilot. Someone experiments with an agent. A function automates a process. HR launches AI training. IT builds infrastructure. A business unit creates its own AI strategy. Another creates a governance committee. Someone buys another platform.

None of that is necessarily wrong. But activity is not the same as capability.

In many organizations, increasingly powerful technology is being layered onto systems designed for slower information flows, centralized decision-making, annual planning cycles, fixed structures, rigid budgets, functional silos, and human-only work.

The technology is moving faster than the systems around it.

That is when random acts of AI start colliding with reality.

What does a constrained system look like?

Many of these problems do not initially look like organizational-system problems.

Execution: We can’t get the pilots to scale.

Governance: Everything takes too long to approve.

Alignment: Everyone agrees on the strategy, but we keep funding things that don’t support it.

Talent: We don’t have the right AI skills.

Culture: People are resisting.

Delivery: We can’t move fast enough.

Technology: Our data isn’t ready.

Learning: We can’t keep up with the pace of change.

Step back for a moment and ask yourself:

Is the system making the desired behavior difficult, risky, or impossible?

For example:

• Are slow decisions the result of unclear authority?

• Is poor alignment the result of disconnected planning, investment, and execution?

• Is resistance a rational response to incentives, risk, or ambiguity?

These questions are worth considering because AI did not create these problems.

AI did not create slow decision-making, rigid budgeting, fragmented information, competing priorities, or functional silos. It did not create the gap between strategy and execution. It did not create organizations where leaders ask people to act autonomously but still require escalation for every important decision.

These are age-old conditions creating age-old problems. AI is simply making them harder to ignore.

And because AI is accelerating the speed at which work, information, experimentation, and decisions can move, those constraints are becoming more visible and more costly.

Creating the conditions for change

AI readiness. Does that sound familiar? Do we have the data, the technology, the governance, the skills, and the leadership support? Don’t get me wrong. Those are useful questions. But you will never “be ready.”

The moment you think you are, the technology changes. The work changes. Customer expectations change. Regulation changes. Competitive pressures change. The capabilities your organization needs change.

So perhaps the more important question is not whether your organization is ready for AI. It is whether the organizational system has created the conditions for change.

Can your organization sense what is happening, understand what matters, make decisions, move people and investment toward what matters most, learn from the results, and adapt continuously?

Not just once. Continuously.

Organizations put enormous effort into product design. We study customers, observe behavior, identify friction, test assumptions, build feedback loops, measure outcomes, learn, and improve.

Yet the systems through which thousands of people make decisions, respond to customers, share information, manage risk, and execute strategy are often inherited rather than intentionally designed. When those systems stop working, we tend to blame the people or reorganize the boxes rather than examine the system people are being asked to work within.

By organizational system, I do not mean the org chart.

I mean how purpose guides decisions, how information and authority move, how priorities and investments are determined, how work crosses boundaries, how learning happens, how incentives shape behavior, and how strategy becomes action.

Those are design choices. They should be intentional.

This does not mean throwing away the frameworks and practices you already use. There is real value in that work. The question is how it connects across the organization. Improving how teams deliver will only take you so far if funding, decision-making, governance, and incentives keep pulling in different directions. We need to build on what works and connect it so the whole organization can benefit.

Maybe one last transformation

I have never been particularly fond of transformations.

The word suggests there is a destination. We are here. We need to get there. So we create a program to close the gap.

But what happens when “there” keeps moving?

AI will not be the last disruption. The technology will continue to change. The work will continue to change. Customer expectations, regulation, competition, and the capabilities organizations need will continue to change.

So maybe the goal is not another transformation.

Maybe it is one last transformation: replacing episodic change with the capability for continuous adaptation.

A shift toward an organizational system that creates the conditions for change rather than waiting until change becomes urgent enough to require another transformation.

Because random acts of AI will not fix an organizational system that is getting in the way.

And if any of the symptoms in this article felt familiar, perhaps this has become important to you.

Start with something that keeps getting stuck. Follow one decision from the moment someone knows what needs to happen to the moment they are able to act. Where does it wait? Who has the authority? What makes it difficult to move forward? That is one place to begin seeing the system.

So where is your organization today?

Before deciding what to change, it helps to understand where you are.

The HyperAdaptive Waypoint Finder can help you see what may be enabling your organization today, what may be getting in the way, and where you might focus next.

It only takes a few minutes and it is free.

Find your waypoint

About Carol McEwan

For more than 30 years, I’ve worked across organizational effectiveness, Business Process Re-engineering, operating-model design, workforce planning, leadership, technology, and organizational growth — as an advisor, Vice President, Managing Director, and CEO.

I’m also known as “Community Carol.” Community building has been an important part of my career, but I see it as part of the larger organizational system: how people connect, how information moves, how decisions get made, and whether people are truly able to contribute.

Today, I’m particularly interested in what AI means for how organizations operate — not simply how they adopt AI tools, but how AI changes work, decision-making, knowledge, authority, and the ability to continuously adapt.

I’m most interested in focused engagements where I can quickly understand what is really going on, help leaders see the system clearly, and determine what to do next, and can be reached at: https://www.linkedin.com/in/cmcewan/

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

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