Issue 287, October 22, 2026

Most of us have lived through at least one downsizing or restructuring, and if you have, you remember the week or two before it happened. Everybody knew something was coming. The budget numbers had been adjusted, the leadership meetings were running long, and there were people in the conference room nobody recognized. In the hallway and over lunch, it was talked about openly as everyone tried to figure out what was coming and who it would affect. People drew conclusions and explored scenarios. But if you went around and asked each person whether their own job was on the list, most would have told you no, that they were safe because their role served a critical function. Maybe marketing. Maybe the team that just got reorganized. Not them, though, not their role, and not this year.

Then the list came out, and a good number of the people who were sure they were safe found their names on it.

A study out of Germany this fall describes almost exactly that hallway, only this time the subject is AI, which may be the best case study we have ever had of this happening in real time.

The University of Konstanz has been tracking AI at work since 2024. Florian Kunze, who studies organizational behavior there, leads the study, and in May of this year his team surveyed 1,105 working people. Forty percent of respondents expect AI and automation to have a negative effect on the labor market over the next ten years. Interestingly, only seventeen percent are worried about losing their own job. One of the researchers, Carolina Opitz, described it this way. People see AI as something society will have to deal with in the future, not as something that affects their own work today.

Those perceptions are interesting given the moment we are in, which I continue to call a very challenging and dynamic time. AI is not yet mature, humans are adjusting and trying to plot the future (which we are horrible at doing), and the media and AI companies continue to put forward doomsday scenarios that may be disconnected from how things actually unfold in the weeks and months ahead. What is happening, what is assumed to be happening and what the future might hold are cloudy at best.

Back to the research. Pew asked Americans much the same question in 2023. Sixty-two percent said AI would have a major impact on workers generally over the next twenty years. But only 28 percent said it would have a major impact on them personally, and about half said the impact on them would be minor or nothing at all.

So in two countries, three years apart, most people agree that AI is going to change work. They just don’t think it is going to change theirs.

We Have Always Done This

There is nothing new about this kind of thinking and where we land about ourselves. We do it about almost everything.

Back in 1980, a psychologist at Rutgers named Neil Weinstein asked a group of college students how likely they were to experience a long list of things in their lives compared with the other students on campus. Some were good, like owning their own home or liking their first job out of school. Some were bad, like having a drinking problem, a heart attack or getting fired. As a group, the students rated their own chances of the good things higher than their classmates’ and their own chances of the bad things lower. Not everyone can be better than average at avoiding trouble, but on the whole they believed they were. Weinstein called it unrealistic optimism, and researchers have found it over and over again since then, in different countries, at different ages and for many different kinds of risk.

A year later, a Swedish psychologist named Ola Svenson asked people how their driving compared with the others in his study. In his American group, 93 percent put themselves in the top half for driving skill, when by definition only half of them can be. We all know that driver, and if we are honest, most of us see ourselves the same way. The accident always happens to someone else, the person texting at the red light, the person driving too aggressively, anyone but us.

One detail in Weinstein’s work matters here. People were most optimistic about the risks they believed they had some control over, and about the ones they had never experienced themselves. Now think about your job. It has always been there. You are good at it, you show up for it every day, you have built the expertise to carry out your responsibilities, and nothing has happened to it yet. It checks both boxes, which is probably why it feels so safe.

It Isn’t Denial

It would be easy to say people are just in denial and leave it there, because for some that is the case. But a study published this summer suggests something else might be going on.

In July, George Ford, the chief economist at the Phoenix Center, a policy research group in Washington, went back through Pew’s survey data and asked what happens to that gap once people start using AI themselves. If using it makes people comfortable, the heavy users should feel even safer than everyone else. If the gap is just part of who we are, using AI shouldn’t make any difference. And if working with it up close makes the risk feel real, the heavy users should be the ones most concerned about their own jobs.

That last one is what he found. The people who use AI often turned their general concern into concern about their own work at a rate about ten percentage points higher than the people who use it occasionally, and that held even after he accounted for the demographic and economic differences between the two groups. In other words, the distance most people keep between “AI will change everyone’s work” and “AI will change mine” comes mostly from not having used it much, not from anything real about how exposed their job is. Ford said the heavy users are “the rest of the workforce, just a bit early.” That is likely because the heavy users are developing a better understanding of what the tools can do and where they are headed than those who have had limited exposure.

We know that some people really are less exposed than others. A nurse or an electrician has good reason to read the headlines differently than someone who spends the day writing reports for their organization. So not everyone who feels safe is wrong. For most people, though, the feeling isn’t based on anything they have actually experienced, at least not yet.

And that feeling is easy to hold onto when you look at what Konstanz found about people’s day-to-day work. Only 38 percent of the people surveyed use AI at work at all, up three points from the year before. Only 23 percent see it making any real difference in what their organization produces, and only 12 percent have noticed fewer entry-level jobs because of it. For most people, AI is still something they read about rather than something they see happening at the desk next to them. It’s hard to worry about something you haven’t seen or experienced in any significant way.

What “Not Me” Costs

The trouble, however, is that the belief has consequences long before anyone’s job is actually affected.

Start with readiness. Achilles Armenakis and his colleagues at Auburn University spent years studying what it takes for people to be ready for a change. They found it comes down to a handful of beliefs held at the same time. The change is needed. Leadership is behind it. It can work. And it actually matters to me, which they called personal valence. Someone can believe the first three completely and still be missing the fourth, and if a change belongs to somebody else, there is no reason to prepare for it. You can see this in almost any organization rolling out AI right now. The optional training gets polite attendance and very little follow-through. People nod along at the town hall, agree that the industry is changing, and go back to doing their work exactly the way they did it before. The pilot is happening in another department. That’s not resistance. People aren’t pushing back against something they think is coming for them, because they don’t think it is coming for them.

Then there is what is happening in the middle of the organization. When most experienced people picture the work AI will take over, they picture someone else’s work: the first drafts, the data pulls, the meeting notes, the spreadsheets the new hire used to update. It is the junior work, not theirs. And on that point they are partly right, because that work is going first, which I covered in this newsletter recently. But that junior work is also how the people coming up behind them learned to think, and the managers who used to hand it out and walk someone through it are exactly the layer organizations are now looking at hardest. The experienced professional who is sure AI is coming for the intern’s job, not hers, is missing what is actually disappearing. It is the work her own successor would have learned the job on. And nobody in the middle is watching it, because everyone in the middle believes it is somebody else’s problem.

The quieter the change, the easier “not me” is to be believed. A few weeks ago I wrote about small waves, the contracts that don’t get renewed and the jobs that never get posted. None of those show up in the hallway. There is no list. So the people who still have their jobs have nothing in front of them that says they might be next.

When It Finally Becomes Mine

Back in the hallway, the distance didn’t close a little at a time. It closed the morning the list came out, all at once, and it will be the same with AI. One day the tool shows up in your own workflow, or your role gets redesigned, or the work you took the most pride in suddenly takes a fraction of the time it used to.

William Bridges taught us that every transition starts with an ending, with letting go of something. A person who has spent a year believing a change belongs to someone else hasn’t let go of anything. They haven’t started their transition at all. So when it finally reaches them, they are not partway through it. They are at the very beginning, and they are starting from a place of having been caught off guard, which is about the hardest place there is to start from.

Last week I wrote about being on a different clock, how leadership finishes its own transition months before the workforce hears about a change, and how far apart those two groups end up. “Not me” pushes the workforce’s starting line back even further. The change may have been announced in the spring, but for the person who was sure it wasn’t about them, the clock doesn’t start until the day the notice lands in their inbox.

And when it does land, their reaction looks a lot like resistance. The questions come late, the frustration comes all at once, and the person who nodded through every town hall is suddenly upset about something that was “already covered.” That is where the misread comes in. Leadership saw a calm workforce for months and took the calm as acceptance. When the calm breaks, the easy explanation is that these people are resisting, when really they are just starting.

If Ford is right, this is going to happen to a lot more people as AI makes its way into more jobs, and it is going to happen to each of them on their own, at their own desk, on a different day from everyone else.

No List This Time

Think about that hallway one more time. Nobody in it was foolish. Each of them had real reasons to believe their job was safe. A strong review, a long history with the organization, an important client, a manager who valued them. Every one of those reasons was something they knew from the inside, while everyone else’s risk was something they could only see from the outside. That is how it works for all of us. Our own situation always looks more particular, more protected and more in our control than anyone else’s.

But the hallway at least had a list. One morning the names went up and everyone finally knew where they stood. AI won’t give us that morning. It will arrive one inbox, one workflow, one redesigned role at a time, and for a lot of us it will feel like someone else’s story right up until the day it isn’t.

So when you think about what AI is doing to work, whose job comes to mind first?

If it isn’t yours, look again.

Related Reading

The Disappearing Middle (Issue 284). On the early-career work AI is absorbing and the managers who used to turn it into judgment. Everyone in the middle assumes it is happening to someone else.

On a Different Clock (Issue 286). Last week, on why people start their transition when a change reaches them, not when it is announced.

The Misread (Issue 285). On structural friction read as individual resistance, which is what a late start tends to look like from the front of the room.

Small Waves (Issue 281). The changes too small and too quiet to show up in anyone’s count.

The Identity Renegotiation (Issue 276). What AI asks people to give up before it gives anything back. That renegotiation only begins once the change feels personal.

On The Human Factor Podcast

Measuring What Change Feels Like, Part 2, with Dr. Michelle Pipes. The second of two conversations, out this week.

Research Sources

1. Kunze, Florian, Opitz, Carolina, and Gerdiken, Elena (2026). Konstanzer KI-Studie 2026: Verlangsamtes Wachstum der KI-Nutzung am Arbeitsplatz, aber nicht für alle Beschäftigten gleichermaßen. Ergebnisreport Juli 2026, Universität Konstanz. Report and press release, September 8, 2026. Survey of 1,105 employed people in Germany, May 2026. Source for the 40 percent and 17 percent figures, the 35 to 38 percent usage figures, the 23 percent and 12 percent figures, and Carolina Opitz’s comment, paraphrased from the German.

2. Pew Research Center (2023). AI in Hiring and Evaluating Workers: What Americans Think. April 20, 2023. Source for the 62 percent and 28 percent figures.

3. Weinstein, Neil D. (1980). Unrealistic Optimism About Future Life Events. Journal of Personality and Social Psychology, volume 39, number 5, pages 806 to 820.

4. Svenson, Ola (1981). Are We All Less Risky and More Skillful Than Our Fellow Drivers? Acta Psychologica, volume 47, number 2, pages 143 to 148.

5. Ford, George S. (2026). Substance or Spectacle? AI Use and the Personal–Societal Risk Wedge. Phoenix Center Policy Bulletin No. 87, July 20, 2026. Press release and full bulletin. Analysis of Pew Research Center American Trends Panel data, Wave 119.

6. Armenakis, Achilles A., Harris, Stanley G., and Mossholder, Kevin W. (1993). Creating Readiness for Organizational Change. Human Relations, volume 46, number 6, pages 681 to 703. See also Holt, Daniel T., Armenakis, Achilles A., Feild, Hubert S., and Harris, Stanley G. (2007). Readiness for Organizational Change: The Systematic Development of a Scale. Journal of Applied Behavioral Science, volume 43, number 2, pages 232 to 255, the source of personal valence as one of four readiness beliefs.

7. Bridges, William, with Bridges, Susan (2016). Managing Transitions: Making the Most of Change, fourth edition. Da Capo Lifelong Books. First published 1991.

Issue 286, October 15, 2026

I have been in Portugal and the south of France for the past two weeks, working, teaching and exploring. For all of that time, I have been five to six hours ahead of the people I work with. By the time my team and my clients sit down at their desks, my day is more than half over, and by the time they hit their stride, I am thinking about dinner. When I am at my sharpest, they are asleep. We are on different clocks, and no amount of effort on either side of the pond changes that.

The other side of it is the jet lag, which is the reason I had trouble coming up with a topic last week and, as it turns out, the reason I ended up with this one. The CDC’s current guidance says the body adjusts at about an hour a day when you fly east, so a five- to six-hour shift takes most of a week to absorb, and I’ll be adjusted just in time to fly home and start over. For the first several days, the alarm goes off at seven, and your body insists it is the middle of the night. You spend the morning in a fog; you know perfectly well what you should be doing, but there is no desire to take anything on, and then at eleven at night you are suddenly wide awake. Your body is on the old clock, whether you fight it or not.

Somewhere in the middle of one of those first foggy mornings, I realized I was describing what most of the workforce experiences during most change initiatives.

The leadership team of an organization decides on a change. They have been living with it for months. They argued about it, got comfortable with it, worked through what it means, and moved on to the next thing. Then the announcement goes out, and from that morning forward the workforce is on a completely different clock. They heard about it only yesterday, which puts them at the beginning of something leadership finished a long time ago, and when they react the way anyone reacts at the beginning, leadership reads it as slow, or resistant, or simply not getting it.

In reality, it’s a time difference being read as a human character difference. Last week I wrote about the misread, the habit of reading structural friction as individual resistance. And yes, I was in a bit of a jet-lag haze writing that piece as well. What I am describing is one of the most common versions of the misread and one of the easiest to miss because everyone involved is looking at the same calendar but not recognizing what time zone each is in.

Change Is the Event. Transition Is the Clock.

William Bridges drew the line, and it’s one I keep coming back to. Change is situational: the new system goes live, the department merges, the reporting line moves, and it happens on a date. Transition is always psychological. It’s the process of letting go of how things were, living for a while in the in-between that Bridges called the neutral zone, where the old way is gone, and the new way isn’t real yet, and eventually finding your footing. None of that happens on a date. It happens at whatever pace a person can manage, and that pace is different for everyone.

Bridges also described something he called the marathon effect. Picture the start of a big city marathon. The front runners are off and moving at full pace before the people in the back have taken a step. By the time the back of the pack crosses the starting line, the leaders are miles down the road, and if they bothered to look back, everyone behind them would look like they were standing still. In an organization, the leadership team is at the front. They started their transition when the idea first came up, in the strategy session, in the board conversation, in the months of discussions before deciding. By announcement day they have already let go of the old way. They have done their own grieving, if there was any, and they are focused on the new thing.

So on the day of the town hall, the room contains two groups of people at completely different points in the same transition, and neither group quite understands where the other is. Leadership is done deciding and can’t understand why there are so many questions about things that were settled months ago. The workforce is at the starting line, hearing it for the first time, doing exactly what leadership did at the starting line, which is to feel uncertain and ask questions as they process what it means to them, their teams, and their departments. Leadership experiences those questions as pushback, even as criticism, and often gets defensive as a result. The workforce experiences leadership’s impatience as proof that nobody wants to hear from them and that they should just suck it up and move on. Both are wrong. Each side has simply forgotten, or never knew, where the other one is standing.

What the Gap Sounds Like

You have surely experienced this gap. A leader starts a sentence with “as you know” about something most of the room is hearing for the first time. Someone asks a question and gets “we already covered that,” when it was covered three months ago in a meeting they weren’t in, or in an email that was easy to let slip by unopened. An executive in the middle of a Q&A is visibly bored with her own slides, because she has seen them twenty times and the people in front of her have seen them once. And then, a few weeks later in the leadership meeting, come the questions I have heard in some form more times than I can count. Why aren’t they further along? What aren’t they getting? Didn’t the training help? The honest answer, most of the time, is that they only just started processing it.

The gap shows up in the measures too. Adoption gets measured against the date the change went live, because that is the date on the project plan. But the project plan is the leadership’s clock. For most of the organization, the change began the day they heard about it, and for a lot of them it began the day it first touched their own work, which may have been weeks after that. So the dashboard says the organization is ninety days in and adoption is behind, while the people being measured are thirty days in and roughly where anyone would be at thirty days, still trying to figure things out. The number is accurate, but as usual it is measuring the wrong thing and starting at the wrong point in time.

Why Leaders Forget They Had the Time

Leaders aren’t being careless when they do this. They just don’t think about it, and it happens to nearly everyone, for two reasons.

The first is that transition happens in private. In 1984, Richard Lazarus and Susan Folkman published a finding that Michelle Pipes and I talk about on this week’s episode of The Human Factor Podcast, the first of two with her. People do not respond to a change. They respond to their appraisal of what the change means for them, and that appraisal gets formed well before the official communication reaches them. Leadership did its appraising over months, with information, with each other, with the chance to argue and to be reassured. It was a long process and most of it happened out of view, in conversations that were never written down. By the time the announcement is drafted, the people drafting it have forgotten that the conclusion they are now communicating took them half a year to reach. They remember the conclusion and not the time it took.

The second one has a name. In 1989, Colin Camerer, George Loewenstein and Martin Weber published a paper in the Journal of Political Economy describing what they called the curse of knowledge: once you know something, you have a very hard time imagining not knowing it, and you consistently overestimate how much of what you know is obvious to other people. It is why experts write bad instructions and why the person who built the spreadsheet can’t understand why nobody else can use it. In change, the leadership team has spent months becoming experts in the change they have decided to implement. The reasons for it feel obvious to them because they have already processed the reasons over and over again. So the announcement comes out in the language of the people who are already in the know, aimed at people who aren’t, and the leaders, of course, can’t hear the difference.

It isn’t a flaw or an error. It is what happens to anyone at the front of the pack. But that view backward has consequences, because the fix that gets adopted is almost always the wrong one.

The People on Both Clocks

There is one group that lives on both clocks at once, and two weeks ago I wrote about what is happening to them. Middle managers usually hear about a change a little before their teams do, sometimes a week, sometimes a day, occasionally an hour. That isn’t enough time to do their own transition, but it’s got to be enough because they have to stand in front of their people and communicate the decision as if they had been part of making it all along. So they are at the starting line themselves, still absorbing it, while being asked to pace the people behind them and report upward to people who are miles ahead and wondering what’s taking so long. Anyone who has been a manager during a reorganization knows the feeling of defending something out loud that you only heard about that morning. It’s a particular kind of exhaustion.

Pushing Through

Here is what I did on my second day in Portugal, which is what I do on the second day of every trip east, and the second day is usually worse than the first. I decided I would simply push through the jet lag. I would get up at a normal hour, take on some work, stay awake until a normal bedtime, and force my body onto the new clock by sheer will. I lasted until about three in the afternoon, fell asleep sitting up, and lost my mind the rest of the day.

Organizations push through in the same way and with the same result. When the workforce appears to be behind, the response is to compress: shorten the rollout, add a second wave of communications that mostly repeats the first wave louder, bring the training in earlier, set a deadline for adoption and tie something to it. All of it amounts to the leaders at the front of the pack yelling back at the people who just left the starting line to run faster. And all of it ignores what Bridges spent his later career saying, which is that you can schedule the change but you can’t schedule the transition. People let go of the old way at the pace they can let go of it, and pressure doesn’t shorten that. What pressure does, unfortunately, is make people stop telling you where they actually are, and a transition that has gone quiet is easy to mistake for one that is finished.

The Other Direction

Sometimes it runs the other way, and that one catches leaders by surprise. The workforce is ahead. The people doing the work saw the problem the change is meant to solve a year before leadership did, lived with it, built workarounds, and gave up expecting anyone to fix it. When the change is finally announced, they are not at the starting line. They were at the finish line long ago and have moved on to something else, which is usually quiet skepticism. Leadership, just now at its own starting line and full of enthusiasm, reads that as resistance too, when in reality it’s the exhaustion of people who have been waiting at the far end of the course for someone to show up. The clocks are still different, just in the other direction, with one group that started early and another that is only now getting its shoes on.

Whose Clock

I am writing this at seven in the morning, through the fog, which tells you how the adjusting is going. By the time most of you read it I will be off the plane and home, about to do the whole thing again in the other direction, where at least the body is kinder and gives you an hour and a half a day.

So a few things to notice this week.

The next time someone in a leadership meeting says “as you know,” ask yourself who in the organization actually knows it, and since when. The next time you hear “why aren’t they further along,” ask how long it took the people in the room to get where they are. Check the date on the adoption report and ask whether it’s the date the change went live or the date the people being measured first heard about it. And look at the people who seem checked out before the change has even started, and ask whether they might have finished this race a year ago, alone.

Leadership is on one clock, and the people doing the work are on another, and the gap between them says less about anyone’s attitude than about when each of them was allowed to start.

So when you look at your own change, the one that is live right now, whose clock are you reading it by? And what would the same picture look like if you read it by the other one?

Related Reading

The Misread (Issue 285). Last week, on structural friction read as individual resistance. The clock gap is the version of it that hides inside a calendar.

The Grief You Can’t Name (Issue 237). On the losses inside a change and why they have to be acknowledged before anyone can move. Leadership did its grieving in private. Everyone else is doing it now.

Small Waves (Issue 281). A conversation at the beach, and the changes that never get announced at all. The other end of this issue, where nobody is on any clock because nobody told them a race had started.

Managing Individual and Team Transitions for Transformation Success. From 2021, where I first wrote about Bridges’ three questions: what will we no longer be doing, what will be different, and who will lose what.

The Misfortunes of Uninformed Urgency. Urgency that begets action before thinking. Pushing through a transition is the same mistake with a deadline attached.

On The Human Factor Podcast

Measuring What Change Feels Like, with Dr. Michelle Pipes. The first of two conversations, out this week, on Lazarus and Folkman, appraisal, and why the dashboard measures the change while the human signal goes dark.

Research Sources

1. Centers for Disease Control and Prevention (2026). Jet Lag Disorder. CDC Yellow Book, 2026 edition. Source for the average rate of adaptation of about one hour per day for eastward travel and one and a half hours per day for westward travel.

2. Bridges, William, with Bridges, Susan (2016). Managing Transitions: Making the Most of Change, fourth edition. Da Capo Lifelong Books. First published 1991. Source of the distinction between change and transition, the three phases of transition, and the marathon effect.

3. Lazarus, Richard S., and Folkman, Susan (1984). Stress, Appraisal, and Coping. Springer. Source of the finding that people respond to their appraisal of an event rather than to the event itself.

4. Camerer, Colin, Loewenstein, George, and Weber, Martin (1989). The Curse of Knowledge in Economic Settings: An Experimental Analysis. Journal of Political Economy, volume 97, number 5, pages 1232 to 1254.

5. Pipes, Michelle (2026). The Human Factor Podcast, Season 3, Measuring What Change Feels Like, part one. Recorded September 2026, released October 15, 2026.

Issue 285, October 8, 2026

Somewhere in your organization right now there is an initiative that isn’t landing. Adoption is thinner than the plan said it would be. The middle of the building has gone quiet. The people you were counting on are asking a lot of procedural questions and almost no enthusiastic ones. So a meeting gets called, and somewhere in that meeting somebody says the word. Resistance. Or they say it more gently. Change fatigue. A communication problem. Not everyone is bought in yet.

With that one word, the problem moved. It stopped being something about the organization and became something about the people inside it. And the fix follows the diagnosis: more training, better messaging, a session on adaptability and resilience. What almost nobody says in that meeting is that the conditions those people are working in would produce the same behavior from anyone, including everyone sitting at the table.

I have written a lot about resistance over the years, including the twelve kinds you can’t see and the way it works as identity protection rather than opposition. This issue is about the other half of the problem, the one I spent an hour on this week with Jenny Vazquez-Newsum for this week’s episode of The Human Factor Podcast. A lot of what gets called resistance isn’t resistance at all. It is friction built into how the organization works, and it gets read as a people problem because people seem easier to fix. That is the misread, and it happens in organizations of every size and type.

The Word Does the Work

In 1977 a Stanford psychologist named Lee Ross described something he called the fundamental attribution error. When we watch another person’s behavior, we overweight what we assume about the person and underweight the situation they are standing in. And it is exactly what a leadership team does when a rollout stalls. It looks at the behavior and reaches for something about the people, somebody to point a finger at.

The management research goes further. In 2008, Jeffrey Ford, Laurie Ford and Angelo D’Amelio published a paper in the Academy of Management Review called Resistance to Change: The Rest of the Story. Their argument was that the people running a change contribute to the very behaviors they then label as resistance, through their own actions and inactions, and that reaching for the word resistance is self-serving for them, because it keeps the problem on the other side of the table. They also made the point that resistance, when it is real, is an indicator of engagement and a valuable source of feedback. People who have checked out don’t push back. They comply and wait.

Once the word is said, the fix is already chosen, and that is what makes the misread expensive. Not the meeting and not the label, but the six months of training, messaging and resilience workshops that follow, all aimed at people who were never the problem in the first place.

What the Friction Usually Is

Jenny has spent two decades on this gap. She came up through Coro Southern California building cross-sector leadership programs, holds a doctorate in education from UCLA, has worked with more than 500 leaders across more than 200 organizations, and wrote Untapped Leadership in 2023. Then she built a method, which she calls contextual agility: the capacity to read an environment, understand its history and nuance, and choose a response that fits the moment rather than relying on habit or a universal formula.

When I asked her what she finds underneath a people problem, her answer was that no challenge is just a people challenge. It’s also a systems challenge, so everything she designs is a people-plus-systems intervention. You can run a training on Monday, and the question that matters is what happens the next day when those people go back to a system that was never set up to let them use what they learned.

Her example was a trust problem, which is a conversation she has often. A team says the trust isn’t there. They aren’t wrong, and some work has to happen around that. But she makes them go further and point to where in their processes, their rhythms, their feedback cycles, trust is being eroded by the design of how the work gets done. The team is usually right about the symptoms. The cause, though, sits a level down, and almost nobody thinks to look there.

Most of us have seen a version of this, even if it looked a little different at the time. An organization decides its middle managers need to get better at giving feedback, so it runs a program on giving feedback. Reasonable. But those managers are compressed in their roles, told they have authority they don’t actually have, and sitting inside a set of power questions nobody will say out loud. The program lands on top of all that and changes nothing, and six months later the organization concludes the managers didn’t take it seriously. That is the misread. The feedback problem was a role design problem that looked like a people problem. Last week I wrote about what happens to the middle when the tools free up a manager’s time and the organization just hands them more. This is the same middle, read wrong by leadership.

Jenny told a story about a regional philanthropic organization that brought her in to fix hiring and onboarding after a hire that didn’t pan out. The COO knew something could be better and couldn’t say what. What she found, once she had talked to the people doing the hiring and looked at what came before, was that the organization had a strong relational culture, hiring managers with a lot of ownership, and no connecting architecture at all. Nobody had defined what it meant to succeed there. The organization sought out a fix for a hiring problem and found its culture was really the issue. That happens more often than anyone would think.

The View From the Top Is the Narrowest One

So why does the organization get the reading wrong? Part of the answer is that the people with the authority to name the problem are the people with the least accurate view of it. Leadership often doesn’t have the detailed knowledge of what happens day to day.

Jenny uses a picture for this with leadership teams. A large elephant, surrounded by people in white lab coats, each pressed up against one part of it, each announcing what they have found. A rug. A plant. A hose. All of them wrong and all of them confident, because each is looking at the part they can reach. The people who can tell it is an elephant are the ones standing back, and in an organization the ones standing back are usually the ones on the margins, by rank, by tenure, by function, by whatever keeps them from being fully inside. All of us, she says, are too close to an elephant somewhere. And the higher you sit in an organization, the closer you are, because the position itself narrows the view. She is direct with executives about this, and she says they take it well once they understand it is the nature of the chair and not an attack on them.

The picture has research behind it. In 1986, the sociologist Patricia Hill Collins described what she called the outsider within, people granted close proximity to a group’s inner life without ever being granted membership in it, and argued that this position produces a distinctive and useful angle of vision. Karl Weick and Kathleen Sutcliffe, studying aircraft carriers, nuclear plants and firefighting crews, found that the organizations that avoid catastrophe share a habit they called deference to expertise: when something is going wrong, decision authority moves to whoever has the relevant knowledge, including downward to people with no rank at all, and moves back when the event is over. And a 2008 study in Personnel Psychology by Subrahmaniam Tangirala and Rangaraj Ramanujam followed 606 nurses across 30 work groups and found that whether a committed, engaged person actually spoke up about a problem depended on the procedural justice climate of their unit. Same person, different unit, different behavior. The silence came from the unit, not the nurse.

So the people who can see the elephant are the ones the organization listens to least. The organization has, often without meaning to, taught them to stay quiet, which I wrote about in Structural Silence last spring. And when they do speak, two people can describe the same problem and one gets treated as a finding while the other gets treated as an attitude. The difference is very rarely the accuracy of the description.

Pushing the Right Place in the Wrong Direction

Even a well-meaning organization that sees the problem clearly can make it worse.

In 1999, Donella Meadows wrote a paper called Leverage Points: Places to Intervene in a System, ranking twelve places you can push on a system from the weakest to the most powerful. In it she tells a story from her teacher, Jay Forrester at MIT. Forrester said people know intuitively where the leverage points are. He would analyze an organization, find the leverage point, go in, and discover that everyone was already paying enormous attention to exactly that spot, and pushing it in the wrong direction.

That is what resilience training is when it gets offered as the answer to a structural problem. The organization has correctly noticed that something is happening to its people, and then it responds by asking those people to absorb it better rather than changing what they are being asked to absorb. It found the right leverage point and pushed it the wrong way. Jenny’s four lenses- lived insight, systems thinking, personal agency and shared vision- are meant to be held together, and she says the mistake leaders make most often is reaching for personal agency first, the let’s-go lens, because action feels like leadership. Her other finding is the one that stayed with me. She runs an index built on the four lenses with organizations, and systems thinking comes back lowest, consistently. Her explanation is that the systems questions are the questions about power, and we don’t talk about power in organizations. We hold it, resist it, or work around it, and almost never examine it in a neutral way. I agree with her completely. People are afraid of that conversation, and the misread lives in the space that fear leaves open. I have watched it for years, in my own experience, and it rarely gets called out.

I have called the broader habit uninformed urgency for years, and for a while it was my favorite topic to explore and write about. The pressure a leader feels is real and it begets action, and the action is often not preceded by the thinking that would point it in the right direction. Jenny put it this way: she doesn’t want to slow anyone down; she wants to stop the speed that runs over the insight that would have gotten them there faster.

When the Reading Is Done by a Machine

There is a newer version of the misread that most organizations are walking into right now, and it comes from the reading being done by software. Engagement scores, sentiment dashboards, adoption metrics, the AI summary of the pulse survey. Those tools are good at the surface, and the surface is where the misread lives. A dashboard can tell you that adoption in a part of an organization is thirty points behind. It can’t tell you that the manager was handed two other teams in the spring, that the system went live right before the busiest month of the year, or that the one person who tried to say so was told to be more positive. The number is accurate and the reading is wrong, and a number like that is more dangerous than no number at all, because nobody argues with it. Remember how we default to numbers as truth. They provide an element of clarity.

Robert Austin, writing in 1996 and building on Peter Blau, called this measurement dysfunction. The moment a number starts being watched, people work toward the number, honestly and in plain sight, and the number quietly stops standing for the thing anyone cared about. An index of conditions is not immune to that. The moment a leadership team starts watching a score about its own conditions, the conditions start performing for the score. Jenny’s answer to that is worth repeating. Not measuring is its own failure, so you measure, and then the work begins. The index surfaces something, and the organization has to go and look at what it surfaced with the people who live in it. The survey that gets taken and then nothing happens isn’t a measurement. It is a ritual, and people figure that out really quickly.

I asked Jenny what a method built on reading conditions does in a world where the reading is increasingly done by something that cannot read a room. Her answer was that she finds a kind of protection in the fact that context changes tomorrow, and changes from her to you, and that a machine is not going to read that. AI can be a tool, she said, and it can be useful at a certain level. What it is good at, unfortunately, is the easier answer, and the easier answer is the misread. Her practice is read the context, act, then read it again, because the moment you act, the context has changed. That is slower than a dashboard, and it is the only version that is actually looking at the elephant.

The Minimum Viable Reading

Everything above takes time, and the moment a leader most needs an accurate reading is usually the moment they don’t have time to get one. So I asked Jenny what she tells a leader who has forty-eight hours, a decision that can’t wait, and no real read on the conditions. Her answer was that there is a minimum viable reading, and it is four questions.

Who is closest to the consequences of this decision?

Who is shaping the response, and who is left out?

What happens in the system when I make this choice; what gets rewarded and what gets squeezed?

And how does this connect to what we are all here for?

That takes a few minutes, not six weeks, and the point is to build the habit when things are quiet, so it is there when they aren’t.

And the one assumption she wishes leaders would stop making, after 500 of them, is that the people experiencing the problem don’t have the answers. The leader believes it’s theirs to solve on behalf of everyone else. The people living inside the friction usually know exactly what it is and how they could work best, and most of what they know would come out as feedback, up and sideways, if anyone had built a place for it to go.

So if there is an initiative in your organization right now that someone has described as a resistance problem, a few things to look at before the next meeting.

Who decided what the problem was, and where were they sitting when they decided it?

When the fix was chosen, was it chosen for the people, or for the conditions they are working in?

Is anyone in the room standing far enough back to see the whole elephant, and if that person exists, has the organization taught them to stay quiet?

And when somebody describes the same stalled rollout you are looking at, do you hear a finding or an attitude?

Season 3 of the podcast opened with a question about what organizations count and who decided. Jenny’s work asks the question that comes one step before that. Before you count anything, and before you choose any intervention at all, who decided what the problem actually was, and what were they standing on when they decided it?

Related Reading

The Resistance You Can’t See (Issue 238). Twelve hidden types of resistance and how to redirect them. This issue is the companion: what to do when it isn’t resistance at all.

Structural Silence (Issue 256). Why organizations train people not to speak. The misread depends on it: the people who could correct the reading are the ones who have learned to keep it to themselves.

The Disappearing Middle (Issue 284). Last week, on the managers in between. Told they have authority they don’t have, and then read as the problem.

Receptacle or Receptor? (Issue 283). Conditions, not types. The same employee is a receptor in one meeting and a receptacle in the next, and the organization decides which.

Invisible Friction Is Slowing Your Strategy (Issue 231). From last fall, on the capacity, capability, culture and behavior gaps that stall a strategy, and the way human responses get mistaken for character flaws.

The Misfortunes of Uninformed Urgency. On urgency that begets action before thinking, which is where most misreads are born.

On The Human Factor Podcast

The Misread, Season 3 Episode 036. The conversation with Jenny Vazquez-Newsum this issue comes from: what is underneath a people problem, the elephant, whose reading counts as data, the Untapped Capacity Index, and the forty-eight-hour decision.

Structural Silence, Season 2 Episode 019. Why organizations train people not to speak.

Research Sources

1. Vazquez-Newsum, Jenny (2026). The Human Factor Podcast, Season 3, Episode 036: The Misread. Recorded September 2026. Direct quotations and examples from the recording transcript. Jenny’s book is Untapped Leadership: Harnessing the Power of Underrepresented Leaders (Prometheus, 2023); her method and the Untapped Capacity Index are at untappedleaders.com.

2. Ross, Lee (1977). The Intuitive Psychologist and His Shortcomings: Distortions in the Attribution Process. In Advances in Experimental Social Psychology, volume 10, pages 173 to 220. Academic Press. Source of the fundamental attribution error.

3. Ford, Jeffrey D., Ford, Laurie W., and D’Amelio, Angelo (2008). Resistance to Change: The Rest of the Story. Academy of Management Review, volume 33, number 2, pages 362 to 377. Source for change agents contributing to the behaviors they label as resistance, the label as self-serving, and resistance as feedback.

4. Collins, Patricia Hill (1986). Learning from the Outsider Within: The Sociological Significance of Black Feminist Thought. Social Problems, volume 33, number 6, pages S14 to S32.

5. Weick, Karl E., and Sutcliffe, Kathleen M. (2007). Managing the Unexpected: Resilient Performance in an Age of Uncertainty, second edition. Jossey-Bass. Source of the five principles of high reliability organizations, including deference to expertise.

6. Tangirala, Subrahmaniam, and Ramanujam, Rangaraj (2008). Employee Silence on Critical Work Issues: The Cross-Level Effects of Procedural Justice Climate. Personnel Psychology, volume 61, number 1, pages 37 to 68. 606 nurses across 30 work groups.

7. Austin, Robert D. (1996). Measuring and Managing Performance in Organizations. Dorset House. Builds on Peter Blau’s definition of dysfunction; source for the point that people optimize for what is counted and the measure stops representing what was intended.

8. Meadows, Donella (1999). Leverage Points: Places to Intervene in a System. The Sustainability Institute. Source of the twelve leverage points and the Jay Forrester story.

Issue 284, October 1, 2026

Most of the talk about AI and jobs is about numbers. How many jobs go away, in which fields, and how fast. Those are fair questions, but they are questions about the immediate and near future. The one I keep pondering doesn’t seem to be getting much attention. What happens five years from now, ten years from now, when the people we count on to know things were never given the chance to learn them?

The experienced person sitting across from you in a meeting likely didn’t show up to work with fifteen years of judgment already in place. They earned it doing the research nobody else wanted to do, writing first drafts that got marked up, sitting in the back of a meeting, learning, by watching senior leaders conduct discussions about substantive topics. A lot of the judgment built over time came from doing work that, by today’s standards, looks routine. And this type of work is exactly what we seem to be automating first.

That, to me, is the problem that we aren’t seeing in organizations today. We are changing how the work gets done without changing how people learn to do it nor addressing how they develop the critical thinking skills that they need. And it seems to be happening in two places at once. At the start of a career, where the early years that used to build judgment are getting cut or radically changed. And in the middle of the organization, where the managers who used to pass that judgment along are being flattened out or let go. Two types of middles, both changing or disappearing, and the cost of both is likely to show up later. We are often focused in the immediate and fail to see the unintended consequences of our actions and decisions. This is surely the case in the present moment.

The Work Beneath the Work

If you have ever brought on an intern or trained a new hire, you know how this goes. The repetitive work goes to them. Pulling the data together. Updating the spreadsheet. Drafting the first version of the memo or the report or the member, customer or shareholder communication. Sitting in on the meeting and taking the notes. Checking the numbers somebody else produced. It doesn’t matter whether it is a marketing department, a finance office, a hospital, an association, or a law firm. That is how the first year or two has always worked, for us and for everyone who came before us. Some of it is boring. Some of it is repetitive. And a lot of it can now be done, at least as a first draft, by an AI tool in a few minutes.

From a productivity point of view, the case is obvious, everyone is seeking to save time. Why have a new person spend an afternoon putting a summary together when the tool can do it in a few minutes? And likely faster than the time it would take you to communicate what you want someone to do?

Why have someone compare two documents by hand, or write up meeting notes, or take the first crack at a draft, when the models do it faster? There isn’t anything special about menial, repetitive work which is why we always viewed our own efforts as induction activities and expected everyone coming after to do the same. Today, it just seems easy with a prompt or a couple of sentences to get the work quickly out of the way. But we have to ask what was that work really doing?

It was doing two things at once. The new hire pulling the data together was also learning where the numbers come from, which ones to trust, and where they don’t add up. The one drafting the report was learning how to make a case, how to justify their thinking and analysis, and what happens when someone with twenty more years of experience pokes holes in it. The one in the back of the meeting taking notes was watching how a seasoned person deals with pushback, office politics, and not having all the facts. None of that was in the job description but all of it was at its core, the point.

Those experiences produced professionals who, over time, would rise up in the ranks, know the work, and know how it gets accomplished. When an organization takes the work away because the tool can produce the deliverable faster, the productivity gain remains very real and tangible. But the learning that came along with the task doesn’t show up anywhere in the young staff. There is then a growing gap that we aren’t addressing.

What the Numbers Say So Far

The research on this is still early, but there is enough of it available to see a pattern.

In August, Erik Brynjolfsson, Bharat Chandar and Ruyu Chen at the Stanford Digital Economy Lab updated their study of what AI is doing to employment, using payroll records from ADP that cover millions of American workers. They don’t see widespread job losses across the economy tied to AI, which is worth keeping in mind the next time a headline says otherwise. What they do see is concentrated among the youngest workers. Among people aged 22 to 25 in the jobs most exposed to AI, employment now sits about 19 percent below where it would be if it had kept pace with people the same age in less exposed jobs. Experienced workers show no comparable gap. And the way it’s happening is not through layoffs. It is through fewer young people being hired in the first place. Nobody is being pushed out the door. The door is just not opening.

So the research is not saying AI has wiped out a fifth of entry-level jobs yet. It’s saying that in the fields most exposed to the technology, the youngest workers have fallen well behind their peers, and that the reason is fewer first jobs. If organizations stop hiring into the roles where careers used to start, then where the next generation of experienced people will come from isn’t going to follow what we considered to be the path.

A second study, by Samuel Westby, Alicia Sasser Modestino and Peiran Cheng, published in June through IZA, looked at 5.7 million U.S. job postings for software developers from 2019 through early 2025. After ChatGPT came out, postings for junior developers dropped 14 to 15 percent compared to postings for senior ones. At the same time, employers started asking for more experience within the same job titles, about a third of a year more, and the junior postings that were left put more weight on problem-solving, communication, and attention to detail. So there are fewer doors, and the doors that are left ask for more experience to get through them; at the same moment, the usual way of getting that experience is being automated. Those changes look unrelated, but they aren’t.

And late last month the Federal Reserve Bank of Dallas added a third piece. Samuel Dodini and Tucker Smith followed graduates of Texas public universities from before and after late 2022. Graduates from majors more exposed to AI saw a relative drop in employment, and among those who did find work, first-year earnings fell about five percent compared to graduates from less exposed majors. Texas is one state, but if you have a son or daughter, a niece or nephew, or a neighbor’s kid who graduated in the last two or three years, you have probably heard a version of this already, and it didn’t come from a study.

The Other Middle

Most of us have lived through at least one downsizing, and if you have, you know who goes first when an organization decides it has too many layers. It isn’t the people doing the work and it isn’t the people at the top. It is the managers in between. I have written before about middle management’s impossible position, and Season 2 of The Human Factor Podcast spent a full episode on the middle management trap. Middle managers are the translators. They take what leadership wants, square it with what the organization can actually do, and help the people doing the work understand why it matters. They also carry the knowledge that never makes it into a manual, like why a process exists, which relationships matter, how decisions really get made, and where the organization is weak. Tina Berger and I covered this in depth in last week’s episode, the gap between the work as it is written down and the work as it actually gets done.

Middle managers also do something that almost never shows up in their goals, which is helping a less experienced person build skills and judgment. Think about the manager who taught you the most, perhaps even became a mentor. Odds are it wasn’t in a training session. It was the one who noticed you were in over your head, who explained why the recommendations you were so proud of were going to land badly, who walked you through what a hard decision was going to cost before you made it. That manager wasn’t passing information down the chain. That manager was building you, one piece of advice and one conversation at a time.

AI now makes that role harder in a way the usual reorganization never did. As organizations look for fewer layers, faster decisions, and less coordination, everything that looks like management work is being examined for what can be automated. Some of that is fair. No manager should spend the week putting together status reports, reconciling spreadsheets, or relaying information that could have gone straight to the person who needed it. Take that away and you have done them a favor. But when the administrative work goes, what happens to the time that gets freed up? Does it turn into time for coaching, for watching how people work, for passing along what the manager knows? Or does the organization just give that manager more people, more projects, and more initiatives to look after, on the theory that the tool made room? That seems to be what is happening, either because people feel they can take on more or because they fear more will be assigned now that the tools have made everyone more efficient.

I have yet to see an organization answer that question on purpose. It gets answered by default, and the default is more tonnage, to borrow David Edward’s phrase from last week. Anyone who has survived a round of cuts knows what it feels like to inherit two other people’s teams and be told the new system will make it manageable.

Information Is Not Expertise

One of the most common mistakes in the AI conversation is treating access to information as if it were the same thing as expertise. An experienced doctor doesn’t just know more facts than a resident. An experienced engineer doesn’t just have more formulas. A seasoned membership director doesn’t just have more data on the members. What they have is the ability to tell when the information in front of them is incomplete, when a familiar pattern doesn’t apply, when the obvious fix is going to cause a bigger problem somewhere else, and when the cost of being wrong calls for more care. That was learned by doing the work and by being told, sometimes bluntly, that the last attempt was wrong.

A 2025 study from Microsoft Research and Carnegie Mellon, presented at the CHI conference, surveyed 319 knowledge workers and collected 936 real examples of how they used AI at work. The more confidence people had in the AI, the less critical thinking they reported doing. The more confidence they had in themselves, the more critical thinking they did. Across the examples, the thinking itself changed, away from doing the work and toward checking, combining, and overseeing what the system produced. I wrote about this in The Confidence Transfer in July, from the point of view of experienced people handing their judgment to the machine. Here it is the same problem from two other points in a career.

Checking and overseeing are reasonable work for an experienced professional. It’s a different thing to ask that of someone who has never done the underlying task and has nothing to check it against. More and more, we are asking people to supervise work they never learned to do, and treating the ability to recognize an answer that sounds right as if it were the ability to know whether it is right.

None of this means the young professional who is good with AI is a lesser professional. A 2025 Microsoft study, published in Management Science, ran field experiments with 4,867 software developers at Microsoft, Accenture, and a Fortune 100 company. Developers who had an AI coding assistant completed about 26 percent more tasks, and the less experienced developers picked up the tool faster and gained the most from it. A separate Microsoft study of 125 interns in the summer of 2024 found that the ones who used Copilot more felt more connected to their teams, used it to get unstuck and to figure out the company’s internal jargon, and overwhelmingly said it helped them get things done faster. Anthropic’s June Economic Index, which surveyed about 9,700 of its own users, found that 68 percent said they were learning more with AI and 57 percent felt it had made their skills more valuable. The same report adds that these are self-assessments, and that skills can erode even while someone reports learning more.

So the technology can speed up learning and it can also hollow it out, often in the same office, and the difference is not whether AI was used. Picture two new hires on the same team. One uses the tool to chase down competing explanations, question the assumptions, and take apart an idea until she understands it. The other takes the polished output, drops it into the report, and moves on. On every productivity measure the organization uses, the two of them look identical, and they are headed in completely different directions. What separates them is how the work was set up, what each was expected to understand, and whether anyone with experience ever looked at what they produced and asked why.

Twenty years of experience doesn’t automatically produce judgment either. A person can do the same thing wrong for twenty years and get very good at doing it wrong (we have all worked with that person and our eye rolls in meetings and conversations were hard to conceal). What produces judgment is the quality of the experience, living with the consequences, and being willing to change your mind. AI doesn’t remove the need for any of that. What it removes is the way those things used to happen on their own, which means somebody now has to make them happen on purpose, and in most organizations the system isn’t yet structured to those ends.

The Bill Arrives Later

Organizations understand succession planning. They identify the critical roles, figure out who could step in, and worry about what happens if a key person leaves. But every version of it I have seen starts with people who are already there. It assumes the organization has already developed enough people with enough experience, and the right span of responsibility by title or job description, to be considered.

Now picture an organization that spends several years cutting back its junior ranks because the tool does most of what those people used to do, and thinning its management ranks because the tool made the coordination easier. The experienced staff stay productive. The technology investment pays off on schedule. Costs come down. For a while, it looks like a well-run business. Then the experienced people start to retire, move on, or simply wear out, and the bench the organization assumed was there turns out to be really thin. The gap didn’t open when they left. It opened years earlier, when the organization changed how the work was done and never changed how expertise was built. And because a smaller group of experienced people can now produce more with the tools, the shortfall looks like strength right up until it isn’t.

Some entry-level jobs never taught anyone anything, and some management layers were pure bureaucracy. AI is a chance to fix that of course. But fixing it means separating the work being automated from the skills that work used to build, and treating the second one as something you plan for rather than something you hope for. Use the tool to speed up the research, then ask the new person to explain why they trust those sources and where they disagree. Let it draft the recommendation, then put the up-and-coming employee in the meeting where the recommendation gets challenged. Take the reporting out of a manager’s week, then protect the hours that come back for the coaching that was never on the dashboard. None of that is about keeping people busy. It is about deciding that growing your people is something the organization is still responsible for and has to ensure remains into the future.

Where the Judgment Used to Come From

So take a look at your own organization and ask a few questions.

Who did the first pass on the last significant piece of analysis, and did anyone with experience sit down with the person who reviewed it?

When the tool freed up a manager’s time this year, where did that time go?

Look at the people in their first three years. Are they getting work that could go wrong in ways they would learn from, or only work the tool has already made safe?

And think about the person you would least like to lose. Who, specifically, is on the path to knowing what that person knows, and what are they doing this month that will get them there?

The people you count on today were once inexperienced people who were allowed to try hard things, get things wrong, and live with the results long enough to learn from them. Most of us can name the manager who let us do that and we remember those people fondly.

If we take those opportunities away without replacing them, we will end up with organizations that are very good at producing work and steadily less able to understand it. And by the time that shows up anywhere anyone is measuring, the middle we needed will already be gone.

Related Reading

The Confidence Transfer (Issue 271). What happens to organizational expertise when trust moves from the person to the machine. This issue is the same erosion, seen from the start of a career instead of the middle of one.

Receptacle or Receptor? (Issue 283). Last week, on the tonnage mentality and what organizations turn their people into. A manager handed more to oversee when the tool frees up her time is tonnage by another name.

The Identity Renegotiation (Issue 276). What AI asks people to give up before it gives anything back. The anxiety of someone entering the workforce today is not resistance to technology. It is a reasonable response to an uncertain path.

Coaching Across Generations (Issue 230). On coaching a five-generation workforce and why the youngest expect mentorship from day one. They are asking for exactly the thing the disappearing middle takes away.

Middle Management’s Impossible Position. From the Change Leadership Series, on the load carried by the people who turn strategy into work.

On The Human Factor Podcast

The Middle Management Trap, Season 2 Episode 016. Why the organization’s most critical change agents are set up to fail, and what they carry that no manual captures.

Research Sources

1. Brynjolfsson, Erik, Chandar, Bharat, and Chen, Ruyu (2026). Canaries in the Coal Mine? Six Facts About the Recent Employment Effects of Artificial Intelligence, revised August 12, 2026. Stanford Digital Economy Lab, using ADP payroll data. Source of the 19 percent relative employment gap for workers aged 22 to 25 in AI-exposed occupations, the finding of no economy-wide displacement, and the finding that the adjustment operates through reduced hiring.

2. Westby, Samuel, Modestino, Alicia Sasser, and Cheng, Peiran (2026). Generative AI and the Redefinition of Entry-Level Software Work. IZA Discussion Paper No. 18723, June 2026. Lightcast data on 5.7 million U.S. software developer postings, January 2019 to March 2025. Source of the 14 to 15 percent relative decline in junior postings and the increase in experience requirements within job titles.

3. Dodini, Samuel, and Smith, Tucker (2026). AI Plays a Role in Weak Labor Market for College Graduates. Federal Reserve Bank of Dallas, September 22, 2026. Texas public university graduates; relative employment decline and approximately five percent relative decline in first-year earnings for more AI-exposed majors.

4. Lee, Hao-Ping (Hank), Sarkar, Advait, Tankelevitch, Lev, Drosos, Ian, Rintel, Sean, Banks, Richard, and Wilson, Nicholas (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 319 knowledge workers, 936 examples. Self-reported data, not a longitudinal measure of skill loss.

5. Cui, Zheyuan (Kevin), Demirer, Mert, Jaffe, Sonia, Musolff, Leon, Peng, Sida, and Salz, Tobias (2025). The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers. Management Science. 4,867 developers at Microsoft, Accenture and a Fortune 100 company; 26.08 percent increase in completed tasks; larger gains among less experienced developers.

6. Vorvoreanu, Mihaela, Graham, Sydney, Heger, Amy, Dhanorkar, Shipi, and Walker, Kathleen (2025). New Employee Copilot Usage: Insights into Productivity and Socialization. Microsoft Research. 125 interns, data collected July and August 2024. Findings should not be generalized beyond that population.

7. Massenkoff, Maxim, et al. (2026). Anthropic Economic Index Report: Cadences. Anthropic, June 26, 2026. Survey of about 9,700 Claude users; 68 percent report learning more and 57 percent report their skills becoming more valuable, with the authors’ caveat that self-assessments do not rule out skill erosion.

Issue 283, September 24, 2026

Somewhere in the middle of my conversation with David Edward for this season of The Human Factor Podcast, he said something I told him on the spot I would steal. He was describing a manufacturing client whose leadership had spent years perfecting the production side of the business, making it faster, cheaper and more measurable, while quietly starving the creative side, because production could be counted and creativity was a black box. When the conversation turned to growth, the efficiency story played out. The business couldn’t grow on decreasing costs. There had to be somewhere for the product to go. And David put the question he likes to put to clients: do you see your customers as receptacles for your product, something you churn out and hand to them, or are they receptors, willing acceptors and adapters of what you make, your services, your solutions and your ideas?

For his client, growth came from treating customers as receptors, and doing that meant rebalancing the whole organization: the efficiency of production against the effectiveness of problem solving. I said I would write about it, but I am taking a different and broader viewpoint as I mulled over how this applies to organizational management, change and transformation. David, again, was talking about customers. The idea to me applies to everyone else an organization touches. Employees, members, boards, shareholders, donors and partners. The people inside a change initiative or a full-on transformation and the people being impacted by it. To me, the difference between a receptacle and a receptor describes, in two words, the thing that most organizational change gets wrong about human beings, and the thing that a great many AI programs are getting wrong right now.

Let me start by describing what I mean.

A Bucket and a Lock

A receptacle is a container. It has no shape of its own that matters, only a capacity. You direct something into it, you fill it, and the transaction is complete when it is full. A receptacle doesn’t choose what it receives and doesn’t do anything with it. It waits. The relationship runs one way, from the filler to the filled, and everything of value is understood to come from the pouring end.

A receptor is a different kind of thing altogether, and the word comes from biology for a reason. A receptor is a structure with a shape, and it responds only to what fits that shape. It is meant to do one thing and was designed for it. It has a purpose it was built for and a direction it acts in. When the right signal arrives, a receptor doesn’t simply hold it. It does something, it triggers a response, it changes what happens next. The relationship runs both ways. The signal has to fit the receptor for anything to happen, which means the receptor, not the sender, decides whether the connection is made.

Now put those two pictures next to each other and think about how an organization treats the people it depends on. A receptacle has to be directed and filled to make a connection. A receptor has autonomy, a purpose and a direction of its own, and it connects when what you offer fits what it’s for. One of those is a description of a warehouse. The other, in many ways, is a description of a person.

Almost every organization I have worked with over the years would say, sincerely and with a lot of confidence, that it treats its customers, its members and its people as receptors. Almost every one of them has its systems, its budgets and its measures built for a culture filled with receptacles. That gap is what I want to explore in this issue, because I think it is an interesting way to look at why the human factor remains such a challenge in organizational management.

Nobody Invented This Last Week

The receptacle idea, I learned, has a long history of being described and applied.

In 1968, the Brazilian educator Paulo Freire described what he called the banking concept of education. Teaching, he wrote, turns students into containers, into receptacles to be filled by the teacher. The more completely the teacher fills the receptacles, the better a teacher she is, and the more meekly the receptacles permit themselves to be filled, the better students they are. That sums up, uncomfortably well, how much of society has understood educating its young.

Freire was writing about schools and about power, but anyone who has sat through a corporate change communication, a slide deck of talking points delivered to a room that wasn’t asked anything, has been in that classroom. The content was poured. The receptacles were filled. The scope of action allowed to those in the room extended only as far as receiving, filing and storing.

Business got to the customer version of this a generation later. In 2004, C.K. Prahalad and Venkat Ramaswamy argued in Strategy and Leadership that the meaning of value was shifting from a product-centered, firm-centered view to one in which the customer helps create it.

The traditional view had the organization deciding what value was, producing it, and delivering it to a passive recipient at the end of the line. That is a receptacle. Their alternative, which they called co-creation, treated the customer as a participant with judgment, preferences and the ability to shape the thing being made. That is a receptor. Put this in today’s terms of customer experience, where the stated intent is to change the organizational system to be more responsive to customers and to bring them into it. The approach is rational and offers a way to create a better, or at least a different, structure. But very few organizations have actually made the shift. Much gets communicated, and in reality the structure keeps its systems oriented toward what was. So the relationship remains, in many regards, one way.

And there is an employee version as well. In 2001, Jon Pierce, Tatiana Kostova and Kurt Dirks set out a theory of psychological ownership in organizations, the state in which a person feels that something- a task, a team, a project, a product, is theirs, whether or not they actually own it. They identified three routes that create this feeling of ownership, which no doubt many of you recognize from your own work as leaders and managers. Having control over the thing. Coming to know it deeply. Investing your full self in it. Every one of those is a receptor attribute. Control, knowledge and self-investment are what a person with autonomy, purpose and direction brings to a connection. A receptacle has none of them.

So the concept I am putting forward isn’t new. What David did was put it in plain enough language that a leadership team, a working group or an individual can pick it up and use it, and I am expanding it.

The Tonnage Mentality

David raised another idea in our conversation that belongs beside this one. He called it the tonnage mentality. The organization is designed to make stuff, a widget, a legal brief, a journal issue, a member benefit, and the plan is written around how much stuff can be produced at what cost. Then leadership keeps heaping more tonnage onto people rather than expecting more of them. More of them means more of their judgment, their ideas, their creativity. More tonnage means more output through the same funnel. One treats a person as a receptor. The other treats them as a receptacle.

The tonnage mentality, to me, isn’t a character flaw. It’s what happens when the easy thing to measure becomes the thing that gets managed, which readers of the last three issues will recognize as the measurement problem again, from another angle. Production can be counted. Creativity can’t, or not easily, so it gets minimized until the growth conversation arrives and someone discovers there is no place for the product to go.

David’s client found, through a talent audit across the whole organization, that the creativity and problem-solving it needed for growth were already inside the building, underused, sitting in people who had been managed as production capacity. The transformation that followed was not a hiring spree. Two senior people were added. Everything else was a shift in conditions. Who had decision-making authority. Whether it was safe to speak, which is the psychological safety Amy Edmondson defined back in 1999 as a shared belief that a team is safe for interpersonal risk-taking, and which I come back to often because so much of change depends on it. Whether people were expected to bring something to the table rather than merely absorb something. The talent had been receptors all along. The organization had been treating them as receptacles.

Where the Receptacle Habit Does Its Damage

Here is where this connects to the human factor, and to what I have been writing about for the past year on how organizations handle people during change and, lately, during AI adoption.

Think about how a typical transformation program treats its own workforce. Leadership decides. Communications are drafted. The town hall is scheduled. Training is assigned. Adoption is measured by licenses issued, modules completed and tools logged into. Every one of those can be considered a filling operation. The organization has an amount of change to pour; the people are the containers it is being poured into, and the program is judged a success when the containers are full. It is Freire’s classroom with much better slides.

Now think about what a person experiences on the receiving end of that, particularly a person with twenty years of expertise, judgment and standing. They aren’t being asked what fits. They aren’t being asked what they know, what they would do with the new capacity, where the plan is wrong. They are being filled. And a person who has spent a career as a receptor, with control over their work, intimate knowledge of it, and a great deal of themselves invested in it, doesn’t experience being treated as a receptacle as neutral. They experience it as a message about who they now are. I wrote in August about the identity renegotiation that every AI initiative conducts with its people, whether or not anyone admits it. The receptacle habit is how that negotiation gets conducted with the most collateral damage. Nobody has to say your judgment no longer matters, because pouring change into someone says it for you.

And the response is predictable. Receptors that are treated as receptacles do one of two things. They stop being receptors; they comply, absorb, store and bring nothing, and the organization wonders where the initiative and the energy went. Or they stay receptors and reject what doesn’t fit, which the organization sees as resistance. Both outcomes look like a people problem. Both are really an organizational design problem. The connection was never going to be made, because nobody checked whether the thing being poured fit the shape of the person receiving it.

The same pattern runs outward as well. The member who is treated as a renewal to be secured rather than a professional with a purpose the association could serve. The donor who is treated as a capacity to be filled with appeals rather than a person with a direction of their own. The customer who receives the product and the survey and the loyalty program and is never once asked what they were trying to do. Each is a receptor being handled as a receptacle, and each eventually behaves like one, passively, until they leave.

There is a practical side to this. A receptor connects only when the signal fits its shape, which means the work of connection is mostly the work of learning the shape before you send anything. That is what asking does. Not the survey that arrives after the product, or the pulse check that arrives after the reorganization, but the question asked early enough that the answer can still change what gets built. David’s discussion guide for this episode, Do We See the Same Business, which he offered to our listeners and which is on the episode page, is built around exactly this: asking different kinds of questions inside a leadership meeting so that the room arrives at a shared picture of what is actually going on before anyone starts pouring. We have practiced a version of this with clients for years under a different name, the psychographic layer, learning what a member or customer is trying to do in the moment rather than what benefit we would like to hand them. The more time an organization invests in that layer, the better the connection, and the reason isn’t mysterious or complex. You are finding the shape of the receptor instead of measuring how much the receptacle can hold. We default to the latter time and again, unfortunately, because it feels easier and takes less effort.

Receptors Are Not a Type of Person

When David and I first talked, I was thinking of receptors and receptacles as types of people. The more I sat with that, the more wrong it felt. Find the receptors, promote them, manage the receptacles. That isn’t what David described and it isn’t what the research supports.

Receptor and receptacle aren’t types of people. They’re conditions the organization creates. The same employee is a receptor in a meeting where her judgment is asked for and a receptacle in the one where it is not. The same customer is a receptor in a relationship where his purpose is understood and a receptacle in one where he is simply a delivery address. David’s client didn’t go out and find receptors. It changed the conditions, authority, psychological safety, expectation, and the people who had been managed as containers turned out to have been receptors the whole time. It’s a view worth considering. It doesn’t ask who your good people are. It asks what your systems are turning your people into because of how you see them.

Which brings me back to change and transformation, and to why I think this distinction belongs in the vocabulary of anyone leading either. Change can be poured. You can fill an organization with a new process, a new system, a new org chart, and if the people hold it and comply, the change has happened, more or less, for as long as anyone is watching. Transformation can’t be poured. It requires people to do something with what they receive, to fit it to their purpose, to act in a direction, to change what happens next. Transformation is a receptor activity by definition. And an organization that has spent a decade optimizing itself to fill receptacles will find, when it reaches for transformation, that it has trained the receptors out of the very people it now critically needs.

The Question Under the Question

So a few things to look at in your own organization.

Look at the last change communication that went out. Was there a question in it, a real one, that the organization intended to act on the answers to? Or was it entirely a deposit that a leader felt needed to be communicated?

Look at how the AI program measures adoption. If every number is a count of what was issued, assigned or completed, the program is measuring how full the receptacles are. Ask what measure would tell you whether anyone did anything with it.

Look at the people who are called resistant. Ask whether they rejected the change, or whether they rejected a change that didn’t fit, that didn’t preserve or create ownership and autonomy, and whether anyone checked the fit before pouring.

And look at the customer or member or donor relationship the organization is proudest of. Ask whether it is proud of what it delivered, or of what the other side went on to do.

David’s phrase was do you see your customers as receptacles or receptors? The harder version of the question is the one an organization has to ask about itself. Are you built to fill, or are you built to fit?

Because the people you depend on, inside and out, already know which one you are, and they have been responding accordingly for some time. You just missed the memo.

Related Reading

The Identity Renegotiation (Issue 276). What AI asks people to give up before it gives anything back. Treating a receptor as a receptacle is how the renegotiation is conducted in silence.

The Same Hole, Dug Faster (Issue 282). Last week, on lateral thinking and why most AI strategies are efficiency strategies. The tonnage mentality is the same hole with a production schedule.

Who Owns That Number? (Issue 280). On measurement that accumulates. Adoption counts are measures of pouring, not of receiving.

The Permission Paradox (Issue 269). Why people hide the very things leadership says it wants. Receptors that are never asked stop offering.

Smart Marketing Is a Mirror, Not a Megaphone. The customer version of this argument, from an earlier season of the newsletter.

On The Human Factor Podcast

Performance Is Lost Before Execution, Season 3 Episode 033. The conversation with David Edward this issue comes from, on what brings out the best in people, how capability becomes performance, and what to do with the capacity AI returns. His discussion guide is on the episode page.

Research Sources

1. Edward, David (2026). The Human Factor Podcast, Season 3, Episode 033: Performance Is Lost Before Execution, recorded September 11, 2026. Direct quotations from the recording transcript.

2. Freire, Paulo (1970). Pedagogy of the Oppressed, chapter 2. Continuum. First published in Portuguese in 1968. Source of the banking concept and the receptacle passages quoted above.

3. Prahalad, C.K., and Ramaswamy, Venkat (2004). Co-Creating Unique Value with Customers. Strategy and Leadership, volume 32, number 3, pages 4 to 9.

4. Pierce, Jon L., Kostova, Tatiana, and Dirks, Kurt T. (2001). Toward a Theory of Psychological Ownership in Organizations. Academy of Management Review, volume 26, number 2, pages 298 to 310. The three routes, control, intimate knowing and self-investment, are drawn from this paper.

5. Edmondson, Amy (1999). Psychological Safety and Learning Behavior in Work Teams. Administrative Science Quarterly, volume 44, number 2, pages 350 to 383. Source of the definition of psychological safety as a shared belief that the team is safe for interpersonal risk-taking.

Issue 282, September 17, 2026

Sarah Tucker is the author of Edward de Bono: Love Laterally, a biography of the man who gave us the phrase lateral thinking, and this past weekend the Wall Street Journal ran a short piece of hers under the headline The Cost of Thinking for Free. She had been invited to speak to about thirty senior people in the financial sector who wanted to understand two things. What AI was going to do to their industry, and whether De Bono’s methods could help them find opportunity in it rather than, in her words, viewing AI solely as a highly efficient mechanism for making themselves redundant. What was meant to be a short talk became an hour and a half. The questions kept coming, about leadership, about institutions that cannot see their own blind spots, about younger people, about whether children will be able to tell what is real from what is made. A couple of people, she wrote, looked visibly relieved when she suggested the future might still need human beings.

Several days later she sent an invoice for a thousand pounds and learned there had been a misunderstanding. The session had not been considered paid work. It had been considered visibility, introductions, future opportunity. Exposure, in other words. Nobody was malicious. Nobody had discussed money beforehand, her included. And a room full of people whose profession is pricing things had left with a free education, while the person who gave it left with a lesson she summed up in five words. Value unspoken is value undefined.

There are two reasons to bring her piece into this newsletter, and this issue is about both. The first is the lesson itself, because it applies to far more than speakers and consultants. Anyone doing work that AI is changing, which is nearly everyone reading this, myself included, is going to have to say what they are worth in a way they have never had to before, and most of us aren’t ready. The second reason is De Bono. Lateral thinking is one of those phrases people use without knowing what it actually means or where it came from, and it turns out to be an unusually good way of describing what is going wrong in a great many AI programs right now and what change and transformation actually ask of us. So let’s spend some time with him and with lateral thinking. I think it is a great lens for looking at current times.

Digging in the Same Place

Edward de Bono was born in Malta in 1933, trained as a physician, went to Oxford as a Rhodes Scholar and on to Cambridge, and in 1967 published The Use of Lateral Thinking, which is where the phrase comes from. Six Thinking Hatsfollowed in 1985 (that is the one most of us met in a training room, with a facilitator holding up colored cards). He wrote more than sixty books, advised governments and corporations, and died in 2021. For some balance, based on my research, his critics were never satisfied that his training programs produced lasting improvements in how people think. His reach, though, was enormous, and the central idea has held up better than the programs. Somewhat of a case where critics think one way and the public finds something that resonates.

The idea he put forward is simple and, once you see it, it’s hard to unsee. Most of what we call thinking is what de Bono called vertical thinking. You start from where you are, you apply logic, and you move step by step toward a better version of what you already have. It is how we solve most problems, and it works most of the time. But it has a built-in limit, which he put in one sentence. You cannot dig a hole in a different place by digging the same hole deeper.

Think about that for a few moments. I surely had to when I read it, because it describes an enormous amount of what passes for strategy. The hole is the current way of doing things. Digging deeper is getting better at it, faster at it, cheaper at it, more measured at it. Every improvement makes the hole deeper and more impressive. Lateral thinking, using that analogy, is the deliberate act of climbing out and asking whether this is the right place to be digging at all. De Bono was insistent that this isn’t a talent some people are born with. It is a set of moves anyone can learn and practice on purpose, and he spent his career naming them.

A few of the moves are worth knowing, as you will likely recognize them.

The first is challenge. Not criticism, just the question of why we do it this way, asked about something everyone has stopped questioning. This correlates to what I have represented in Transformation Theater and the recent Measurement Ratchet pieces. So yes, I am in the mindset of late on why we stop questioning, when we really should be asking more and more questions.

Most of the assumptions ingrained in organizational thinking and decision-making are never stated out loud. Everyone simply seems to know them and believe them, which is precisely why nobody challenges them. Tucker’s talk to the bankers was about exactly this: institutions trapped by assumptions they no longer recognize as assumptions. And then the bank proved her point. Everyone there assumed the talk was free, a bit of networking, and nobody stopped to question that assumption, so nobody noticed it was one.

The second is provocation, which De Bono marked with a word he invented: po. Po is a deliberately unreasonable statement put on the table without any obligation to defend it. Po: the office has no meetings. Po, the customer sets the price. You don’t argue with it. You use it for movement, to see where it takes your thinking before you judge it. This is the opposite of how most of us were trained, which was to evaluate an idea the instant it appears and kill the weak ones early in order to get to the best. De Bono’s point was that judgment applied too soon kills the idea that was one step away from a good one. His point should really cause us all some reflection, because we are so well trained and default so readily to our own human nature. In this regard, we are likely limiting our ability to get to the good ideas.

The third is reversal, which is simply looking at the situation from the other side, and the fourth is what he called random entry, bringing in something unrelated to force a new connection. Tucker used reversal and po on her own dilemma, along with a third tool she calls extreme, which pushes an assumption until its weakness shows, and found they made it easier to see how two intelligent groups could sit in the same room for ninety minutes and walk out with completely different accounts of what had happened. Which is, in a way, the whole point. Lateral thinking doesn’t treat perception as something to trust. It treats it as something you can move. And in a time when so much seems out of our control, perhaps the opening is in how we are all looking at it.

AI Is the Best Vertical Thinker Ever Built

Here is where de Bono becomes useful for today, and why Tucker’s bankers were right to want him, via Tucker, in the room.

What AI does, and does extraordinarily well, is dig the hole deeper. It works inside the frame it is given. It processes, drafts, summarizes, classifies, and predicts at a speed no person will ever match, and it does all of that without once asking whether the frame is the right one. That is vertical thinking, mechanized. And it is worth being honest that vertical thinking was the thing most of us were paid for. It is the layer of professional life we credentialed, promoted, and built careers on. The good analyst, the fast writer, the detail-oriented inspector, the thorough reviewer. That layer is getting cheaper by the quarter, and no amount of wishing will change it.

Now look at how most organizations are approaching AI, and you will see the same hole being dug faster. The AI strategy, if you read it closely, is nearly always an efficiency strategy.

Do what we already do, with fewer people, in less time. That is a perfectly vertical response to a lateral opportunity, and it is the response Tucker’s audience had come to hear an alternative to. It is also why so many AI programs feel to the people inside them like a countdown rather than an opening. If the only question being asked is how much of the current work the machine can absorb, then the honest answer is a great deal of it, and everyone in the building can do that math.

The lateral question is different, and almost nobody that I know of is asking it. Not what can the machine do that we used to do, but what could we do now that we could not do before?

Reverse it. If the machine handles the digging, what does the organization do with the people who are no longer digging, and does anyone have a plan for that beyond a smaller payroll? I recorded a conversation last week with David Edward for this season of the podcast that came to rest on exactly this: what to do with the capacity AI returns, and the striking thing about the question is how few leadership teams have an answer that is not a euphemism. The episode is available today wherever you get your podcasts.

This is also, for readers who have followed us for a while, the difference between change and transformation, put in De Bono’s terms. Change management is the discipline of digging the current hole better. It is valuable, it is necessary, and Kotter and Prosci and ADKAR have taught a generation of us how to do it. Transformation is a different hole. It begins with a lateral move, a challenge to the dominant idea of what the organization is for, and no amount of excellent change management will produce it, because change management is designed to make a chosen destination arrive smoothly, not to question the destination. We see this constantly. An organization announces a transformation, funds a transformation program, executes it well, and arrives two years later at a more efficient version of exactly what it was. The hole is deeper, and the hole hasn’t moved. Grant Van Ulbrich, an upcoming guest on the podcast, framed it to me in these terms. Change means an organization, and even a person, can return to the prior state. Transformation is like the caterpillar. It doesn’t stay a caterpillar; it becomes a butterfly, and it has become something else entirely. It is worth pondering whether everything we have been trained on in change management comes with the built-in option to simply revert. That is De Bono’s approach at work, seeing the same thing differently.

Why Intelligent People Keep Digging

De Bono had a name for why this happens, and it is uncomfortable. He called it the intelligence trap. In I Am Right, You Are Wrong he wrote that a highly intelligent person can take up a view and then defend it so ably, through choice of premises and perception, that the skill of defending crowds out any inclination to explore. The better you are at being right, the less you look around. Intelligence, in his account, is horsepower. Thinking is where you point the car, and a powerful engine pointed at the wrong destination just gets there faster.

I have watched this in more than forty organizations over the years, me included on more than one occasion, and it’s rarely a failure of ability. The people running the AI program are usually the smartest people in the building. They are extremely efficient at operating inside the existing system, which is how they got the job, and that efficiency is precisely what makes the dominant idea invisible to them. The publisher that knows it is a publisher. The association that knows members join for the journal. The bank that knows a speaker who talks about ideas is a networking event, and a vendor who delivers a service is a purchase order, and files a biographer under the first heading without anyone deciding to.

And here the measurement thread from the last two issues comes back in, from a new side. Everything an organization measures, it measures inside the hole. Licenses, hours saved, tickets closed, headcount reduced, adoption rates. Those are all measures of how well the digging is going. There is no number anywhere in the system for whether the hole is in the right place, and there is no line in the budget for the person who climbs out to look. So the lateral move, the one contribution that could actually change the outcome, is the one that is never counted, and what is not counted is not paid for. That is what happened to Tucker, and it’s what happens every week to the person in your organization who saw the problem coming and said so in a meeting and was thanked for their perspective.

What Is Still Worth Paying For

So what expertise remains marketable as the digging gets automated? The market has begun to answer, and the answer is consistent with everything above.

In 2023, researchers at Washington University in St. Louis looked at what happened on the freelance platform Upwork in the months after ChatGPT arrived. Writers, proofreaders and editors saw monthly jobs fall by about two percent and monthly earnings by a little over five. After the image generators arrived, designers and illustrators lost more. And the finding that should stop you is that the more highly rated freelancers were not protected. They lost the most, because the tools helped weaker performers most and narrowed the gap the stronger ones had been paid for. When the digging gets cheap, the premium for good digging goes with it. Upwork’s own workforce index this July found the other half of the sort. Freelancers using AI earn about a third more per hour than those who do not; earnings for complex judgment work rose forty-five percent in a year, and earnings per contract for simple generative production fell thirteen. Upwork sells the thing it measures, so treat those as a company’s view of its own marketplace, published for its own purposes. And relate this to last week’s issue, Small Waves, as you will likely be able to draw the connections on your own. Execution is being priced down. Judgment is being priced up. In many ways, a path forward.

Judgment, in De Bono’s terms, is the lateral move. It is knowing which hole. It is the challenge nobody else will voice, the reversal that shows the buyer what the situation looks like from the other side, the provocation that shakes a planning session loose from the answer it walked in with. And this kind of work has a peculiar and inconvenient property. It doesn’t look like work. It looks like a conversation. It looks like an hour and a half of questions after a talk that was supposed to be short. It looks like exposure, unless someone says otherwise, and the person who says otherwise first is the person who defines the value.

I will add our own recent experience with this value-defining challenge. Not long ago we lost a strategic planning bid for a small nonprofit, and as best we can tell, the reason was price. We could have cut the fee, and we chose not to, despite knowing we would really enjoy the work and the organization’s subject matter. What a plan from us buys isn’t the plan. Anyone can produce a handsome document, and the machines now produce handsome documents from a slew of notes, recordings, and more in an afternoon. What the fee buys is having been inside more than forty organizations and watched what actually happens after a plan is approved, which is the part almost no plan accounts for, and which is what makes the outcomes workable, realistic and achievable instead of admired and shelved. I can’t fully explain that in a proposal any more than Tucker could explain to the bankers what nine years of conversations with De Bono were worth. So we said the number that reflects it, and we lost, and losing on price is acceptable. Doing the thinking for free isn’t.

I share that story because the boundary took me years to learn, and I suspect most readers are still learning it. Tucker is honest that creative and intellectual people avoid the money conversation because it feels awkward, transactional, faintly vulgar. I can attest that it does, and it is something I still struggle with even after running a company for over twelve years.

We prefer the ideas, the immersion, and the work. So do I. And that preference is exactly what the market will exploit, not out of malice, but because ambiguity always resolves against the person with the least leverage, and in any room the person with the least leverage is nearly always the one who did the thinking. Ponder that one for a bit and I am sure you can align it to your own experience.

Climbing Out

De Bono’s whole career rested on one claim: that thinking is a skill and not a trait, and that the skill can be practiced on purpose. Tucker’s afternoon adds a corollary de Bono would probably have enjoyed. Knowing what your own thinking is worth is a skill too, and as AI takes over the digging, it is about to become one of the most important skills a working person can have.

So, two lateral moves to try this week, one for the organization and one for yourself.

For the organization, take the AI plan, whatever form it is in, and run a challenge on it. Why is every objective on this page a version of digging the current hole faster? Then run a reversal. If the machine handles the execution, what would we do with the capacity, and can we say it in a sentence a reasonable employee would understand and believe? If the honest answer is a smaller payroll, at least the plan is now telling the truth, and the people in the building already knew that. That outcome, however, isn’t helping your culture, building the capability of your people, or creating a shared purpose.

For yourself, run a provocation. Po, the thing I do that matters most is the thing that never appears on my job description. Don’t argue with it. See where it goes. What do you see, or decide, or change, that the machine working inside the frame doesn’t? Say it straight up to yourself first, and the next time a room asks for it, say it to them, and let the number follow.

Because value unspoken is value undefined. De Bono would have said the same thing about a hole nobody has looked at in years. The only person who can climb out and name it is you.

Related Reading

The Identity Renegotiation (Issue 276). What AI asks people to give up before it gives anything back. This issue is what happens when the renegotiation reaches the invoice.

Small Waves (Issue 281). Last week, on the millions of small decisions reshaping work without anyone announcing them. A declined invoice is one of those waves.

Who Owns That Number? (Issue 280). On measurement that accumulates and never gets removed. Here, the problem is the measure that never gets written down at all, whether the hole is in the right place.

Transformation Theater (Issue 275). When the change program performs transformation without producing it. Digging the same hole faster is the theater’s favorite act.

The Truth About Transformation. The book behind the method, on why most transformations fail and what changes when psychology comes first.

On The Human Factor Podcast

The Measurement Ratchet, Season 3 Episode 032. Why nothing you measure ever leaves, and why the measures we keep are all measures of the digging.

Research Sources

1. Tucker, Sarah (2026). The Cost of Thinking for Free. The Wall Street Journal, print edition, September 12, 2026. First published as Sarah Tucker: The Risk of Free Speaking, Finito World, May 22, 2026.

2. Tucker, Sarah (2024). Edward de Bono: Love Laterally. Aurora Metro Books, April 2024. Foreword by Baroness Helena Kennedy.

3. De Bono, Edward (1967). The Use of Lateral Thinking. Jonathan Cape. De Bono, Edward (1972). Po: A Device for Successful Thinking. Simon and Schuster. De Bono, Edward (1985). Six Thinking Hats. Little, Brown.

4. De Bono, Edward (1990). I Am Right, You Are Wrong: From This to the New Renaissance. Viking. Source of the intelligence trap passage.

5. De Bono, Edward. “You cannot dig a hole in a different place by digging the same hole deeper.” Quotation as published in the official Edward de Bono quotations collection, debono.com. Biographical dates confirmed against Thinkers50 and contemporaneous obituaries, June 2021. Descriptions of the challenge, provocation, random entry and movement tools drawn from the de Bono Group’s published lateral thinking curriculum.

6. Hui, Xiang, Reshef, Oren, and Zhou, Luofeng (2023). The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market. Working paper, Washington University in St. Louis, Olin Business School, August 2023; later published in Organization Science.

7. Upwork Research Institute (2026). The Future Workforce Index 2026: AI, Freelancing, and the New Value of Work. Survey of 2,400 U.S. skilled knowledge workers, March to April 2026, with platform data. Published July 14, 2026.

Issue 281, September 10, 2026

A few weeks ago at the beach, I fell into a long conversation with a man who runs a real estate firm. A successful outfit, the kind of place that has been a fixture in a small market for years, the name on the signs you drive past on the way in. We got to talking the way two people who run businesses do, and before long we were comparing notes on what each of us had done with AI.

He is up until two in the morning most nights. Not because anything is broken and needs fixing in the moment. He is building. He has been assembling his own systems, databases, and agents in Claude Code, one function at a time. He was as animated, excited, and satisfied describing what he has been doing as anyone I have talked to all year. Document prep. Marketing. The listing workflow. Sales funnels. New services for lead capture. All of the things he used to pay somebody else to handle. He walked me through what each piece does and what it replaced, and he was relishing every bit of it.

Then he said the other half, in the same breath and the same tone, because to him it was one coherent sentence of thought. He no longer needs to employ the people he used to employ. He no longer needs to find himself frustrated by what he got in return; he no longer needs to contend with the rising service rates, nor the constant coordination and follow-up. All he needed was to continue to replenish his tokens. A single need after years of managing contractors and employees to maintain and grow the business.

He’s not the villain of this or any similar story as some might suggest. He saw an opportunity. He built a lot. It all works with much less coordination and overhead. It saves him substantial money and energy every month. And most importantly, he has an outlet with the only expense of his time, to continue to leverage his creativity and bring his ideas into reality. Anyone who has ever run a small business knows exactly what that feels like, me included.

But I have thought about the conversation a lot since, given there is a pattern in his story that is being overlooked. It’s about the handful of people who used to have that income and now don’t. Maybe a few hundred dollars to a few thousand dollars a month that now needs to be replaced in some way. The someone who prepared the contractual documents. The someone who kept the listings current and the marketing going out. The someone who made sure the computers worked.

And nothing about the shift will ever show up anywhere where someone is paying attention. There was no announcement. No restructuring. No press release, no consultant, no formal change or transformation program with a name and a steering committee and a slide that says which quarter it begins. A man stayed up late for a few months, and an office quietly stopped needing five or six people it used to need. Think about that for a few moments.

That is one small wave. It rose, it came down on five or six companies or individuals, and the water went flat again before anyone outside the building had any reason to notice. Which is the whole problem, because a wave that size is easy to miss. It doesn’t draw one’s attention. It likely does not become part of any business or economic survey or report. But it is consequential nonetheless. So, what is the impact of this when there are a few million of these small waves, all rising at once in different cities and towns across a country, none of them big enough to report on or make the news?

Millions of the Same Decision

Let’s start with how many small companies like his there are.

The Small Business Administration publishes these numbers every year, and the February 2026 edition, working from 2022 Census data, counts 36.2 million small businesses in the United States. That is 99.9 percent of all American firms. They employ 62.3 million people, close to half of everyone who works for a private employer. They account for 43.5 percent of the economy. And from 1995 through 2024, small business created 20.7 million new jobs against large business’s 13.2 million, which means roughly six of every ten jobs added over thirty years came out of a small firm.

Now put the beach office back in the frame with those numbers around it. Almost half the people who work for a private employer in this country work somewhere like it. These are not places with a change management office and a pilot cohort and a communications plan. They are places where the owner decides on a Tuesday and it is done by Friday, and where the only approval required is his or her own. Every one of those 36.2 million owners can make the same decision he made, in the same week, without telling anybody, and most of them are hearing about and considering the use of the same tools he leveraged.

One more thing about what I think is the shape of that decision, because it explains why these waves become more numerous and none of them recede. Gartner published research in June 2026 arguing that AI is not reducing workforce costs so much as reshaping them. Jan Bansch and Joe Coyle found that up to thirty percent of AI-displaced roles will be rehired by 2029, often at a higher cost than before. That is a real and useful finding about companies that have hiring cycles, pay bands, and an HR department. But none of it describes the office at the beach or the millions of small businesses that are serving customers in a variety of ways. There are no rehires coming in 2029 or in any future year, because there is no budget line waiting for one and no plan that is in development or crafted just in case. Whatever he saved, he simply saved, and it stays saved. Gone from his operating expenses and from the daily and weekly task list. In a large company, the water tends to wash back. In a small one it doesn’t. It becomes the new normal, in reality and in perception, and the business becomes more efficient.

Waves That Leave No Mark

So if millions of these are happening, why does it feel like nothing is happening?

Part of the answer is that our ways of assessing employment, the economy and more are pointed at the wrong thing. A Federal Reserve note published in April 2026 lined up the available surveys on AI adoption and the answers don’t agree with each other. Count firms, and about eighteen percent had adopted AI by the end of 2025. Count the labor force by where people work, and roughly seventy-eight percent are employed at a firm that has. Ask individuals what they actually do at their desks, and about twelve percent use it daily. Same economy, same points in time, three defensible numbers, and three completely different stories about how far along we are. This is the challenge I talked about last week surrounding measurement. Despite the gaps, despite the omissions, despite the limited or even biased output, the numbers are something that we look to in defining the current conditions. We use that output to make decisions. To inform our strategies. To rank against others.

But notice what none of those surveys ask. Every one of those surveys wants to know whether a firm uses AI, which is a question about tools. A firm that bought licenses and changed nothing counts as an adopter. A firm that quietly went from nine people to four counts exactly the same way. We are measuring who bought a boat, not whose office emptied out.

The second part of the challenge comes from the Stanford Digital Economy Lab, where Erik Brynjolfsson, Bharat Chandar and Ruyu Chen have been working through payroll records from ADP. Their August 2026 update cuts both ways. Workers between twenty-two and twenty-five in the jobs most exposed to AI are now roughly nineteen percent below where they would be if they had kept pace with people the same age in less exposed jobs, and a year earlier that gap was fifteen percent. At the same time, the researchers say plainly that they do not see broad job loss across the economy from AI. What they see is concentrated rather than general, and where AI helps people do their work instead of doing it for them, employment held steady or grew. Anyone telling you the job market is being gutted wholesale, either right now or in a near-term projection, is embracing the media hype and not the evidence.

But between those two findings sits the line that I think matters most here. The change, they write, is happening mainly through hiring fewer young workers rather than through letting people go. There are near-term and longer-term consequences to that finding that surround the quantity of jobs available to those who are seeking to be employed. We are feeling the current, but it seems smaller than it is.

Their sentence explains why these waves don’t leave any mark in the sand. A layoff is an event. It has a date, a number, a memo, and sometimes water cooler gossip about people carrying boxes out to their cars. It’s recorded in the firm’s human resources system. A job that is never posted doesn’t exist. There is no announcement that a firm decided, quietly and reasonably, not to replace the person who left in March. There is no coverage of the coordinator role that used to exist at four hundred small firms and now exists at three hundred and forty. Those seeking employment spend their days searching and searching. For what they do find, they send more and more applications into a market that feels inexplicably harder than the one they walked into three years ago. Think about this for a few moments. If I have more than a handful of stories of this happening to those I know, you surely do as well.

There is something that may be even harder to see. Not everyone the real estate owner stopped paying was an employee. Some of that work was contracted out: the marketing and the document preparation and the fellow who kept the computers running, and in a firm that size a good deal of the work usually is. When an employee is let go, a record exists somewhere. There is a separation, usually an unemployment claim, a number that eventually reaches a statistical agency at the city, state, or federal level. When a contract simply is not renewed, there isn’t anything at all. No separation, no claim, no notice, no filing. The invoice stops arriving and both parties move on.

The Bureau of Labor Statistics does count independent contractors, and as of July 2023, there were 11.9 million of them, 7.4 percent of everyone working, up from 6.9 percent in 2017. But look at how that gets counted. It is an add-on to a household survey, and the Bureau ran it in 2017 and then again in 2023. Twice in six years. The one exercise that might catch what is going on only opens its eyes about once every six years. I have written often about the speed at which everything moves in today’s marketplace. Whether it be AI, consumer spending or other, six years to learn what is happening results in a lifetime of no information.

We have failed at this before, time and time again. When a plant closed, we counted it, wrote about it, and argued about it for decades, because a plant closing is an event. It has a gate, a last shift, a parking lot that empties out. Some reflect on this as the natural cycle of a capitalist economy. Others recognize the span of impact this has across more than the firm.

What happened to small downtowns across those same decades was bigger, and almost nobody noticed it while it was happening, because it showed up as a local hardware store that did not reopen after the owner retired and a local card shop that turned into a vacant window. Nobody held a press conference for that. There was no last shift. The whole thing was made out of things that stopped, and we have never built mechanisms that help us count what stopped and why.

What the Water Looks Like From Shore

Here is the part that I think matters and what has been on my mind the past few weeks. A few million small waves don’t stay small. Individually, they are invisible and they leave no mark in the sand. But collectively they stop being small events at all and become a water level, and the water level is measurable even when none of the waves were.

So look at the water level.

Markets have been strong. The aggregate numbers suggest we are in a really good period that is defying what we thought we previously understood about how the economy works. And yet almost nobody I talk to describes their own current life in that way. So which is it? Are we in a strong economy that people are somehow failing to appreciate or that they don’t see? Or a weak one where the indicators we use to assess the current state are somehow missing it? I think the Federal Reserve Bank of New York informs part of the why.

In a series published in May 2026, Rajashri Chakrabarti, Thu Pham, Beck Pierce and Maxim Pinkovskiy tracked spending by income group and found the growth is coming almost entirely from households earning more than one hundred twenty-five thousand dollars a year. They were the only group whose spending consistently rose. Lower-income households actually spent less for part of that stretch and did not get back to where they started until the middle of 2024. Their companion piece asked why, and found that wealth has split the same way since 2023, the top group up more than twenty-five percent while the middle forty percent gained less than ten, and that lower-income households have been paying higher inflation than everyone else since late 2022. The same year produced a genuinely good number and a genuinely worse life, depending on which house you walked into. This describes what different segments of the working world are experiencing, and it really depends on who you are and what your income level is.

Let’s explore some additional information. In July 2026, one point nine percent of workers quit their job in a month, according to the Bureau of Labor Statistics. In 2021 and 2022, we wrote issue after issue about people leaving jobs in record numbers and what it meant that they felt free to do so. Now they are staying. And not because the jobs got better, but because they feel they have no choice.

Another New York Fed team expanded on what seems to be really happening. In work published in May 2026, Gizem Kosar, Ishva Mehta and Wilbert van der Klaauw looked at households that had skipped meals or run short on food. Among those households, the share who believed they could find a job fell from just under forty-nine percent in the spring of 2020 to a little over forty-one percent in February 2026, and it sat lower still, at just over thirty-seven percent, as recently as last October. And as of February 2026, far more of them expected to be worse off a year from now than better, by a margin of about thirty-two points. That isn’t a mood, as some describe it, and it’s not pessimism as a personality trait. Those are households behind on something that is not optional, who have concluded there is nothing better to move to.

None of those numbers have AI written on it, at least not directly. Thoughtful people will read the same figures and land somewhere different on what is causing them. Everyone has a viewpoint, biases, and an opinion. But this is what it looks like from the shore when a great many small waves come in at once. Not a headline. But a water level that rose.

The Tide Is Already In

There is a version of the AI conversation that treats all of this as deterministic and as a forecast. Something coming, on a schedule, that we will have time to prepare for once the large organizations finish their pilots and publish their lessons learned.

But it isn’t ahead of us. It’s running now at a pace set by thousands of people like the man I met at the beach, each making a sensible decision at two in the morning, none of them coordinating with anybody, none of them announcing anything, and every one of them creating their own small wave and adding to the water level.

Which is exactly why it is so easy to not see. There isn’t anyone to be angry at, no decision to overturn, no announcement to answer. A tide gives you nothing to push against. It is just going to rise, or fall, and most people feel there isn’t anything they can do about it.

A couple of weeks ago I put together a sampler called The Conditions Nobody Chose. The thread running through it was that most of what shapes working life shows up as a condition rather than a decision. There isn’t anyone who votes on it, nobody announces it, and by the time it is clear enough to argue about, it’s already the ground everyone is standing on. This is one of those times, with one difference.

The difference is that we are not even looking away. We keep watching the harbor for a big wave. It’s in the media; it’s in countless social posts, blog posts and recaps from conferences. Many are expecting the large swell to come tomorrow, next month or in a few years. Many are feeling anxiety, economic pressure and/or identity threat as they wait. But the tide came in while we were looking.

I keep coming back to something simple. You already know some of these stories. Somebody’s hours that got cut and never came back. Somebody who has been looking since spring and cannot explain why it is so much harder than it was. A vendor who stopped invoicing because there was nothing left to invoice. We hear each one on its own and file it as that person’s bad luck.

That is how a tide stays invisible. It arrives as a few million separate pieces of bad luck, every one of them explainable by itself.

So before the next forecast about what AI is going to do to work in five years, there is a smaller question worth asking. How many of these stories do you already know? And what changes if they are not separate stories at all?

Related Reading

The Identity Renegotiation (Issue 276). What AI asks people to give up before it gives anything back. This issue is what that renegotiation looks like from the outside, in a market where everyone knows everyone.

The Permission Paradox (Issue 269). The workforce had already transformed itself in private while the instruments showed people waiting. Small firms are doing the same thing, at the level of the whole business.

Who Owns That Number? (Issue 280). Last week, on measurement that accumulates and never gets removed. A companion problem, which is what happens when the thing that matters most produces no measure at all.

The Proxy Collapse (Issue 278). On what happens when the evidence we relied on stops working. Employment statistics are a proxy too.

The Conditions Nobody Chose (Issue 279). A late summer sampler on conditions that arrive without anyone choosing them, which is the frame this issue is built on.

Measuring What Matters. The four questions, including the one this issue keeps running into. What decision or action does this measure inform.

The Transformation Readiness Assessment. A baseline on whether your organization is prepared for what is already underway.

On The Human Factor Podcast

The Measurement Ratchet, Season 3 Episode 1. Why nothing you measure ever leaves, and what it takes to retire a number. Out now.

The Long Game, Season 2 Episode 030. Tom Serena on pace and patience as the denominator of change. The pace problem is the whole subject of this issue, seen from the other end.

Research Sources

1. Bansch, Jan, and Coyle, Joe (2026). AI Isn’t Reducing Workforce Costs — It’s Reshaping Them. Gartner, June 1, 2026.

2. United States Small Business Administration, Office of Advocacy (2026). Frequently Asked Questions About Small Business, February 2026, using 2022 Census Bureau data.

3. Brynjolfsson, Erik, Chandar, Bharat, and Chen, Ruyu (2026). Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence, August 2026 update. Stanford Digital Economy Lab, using ADP payroll data through mid-2026.

4. United States Bureau of Labor Statistics (2023). Contingent and Alternative Employment Arrangements, July 2023, Current Population Survey supplement.

5. Chakrabarti, Rajashri, Pham, Thu, Pierce, Beck, and Pinkovskiy, Maxim L. (2026). Tracking the K-Shaped Economy: Who’s Driving Spending? and Explaining the K-Shaped Economy: What’s Behind the Divide? Liberty Street Economics, Federal Reserve Bank of New York, May 1, 2026.

6. United States Bureau of Labor Statistics (2026). Job Openings and Labor Turnover Survey, quits rate, July 2026.

7. Kosar, Gizem, Mehta, Ishva, and van der Klaauw, Wilbert (2026). Food Insecurity and Consumer Pessimism. Liberty Street Economics, Federal Reserve Bank of New York, May 27, 2026.

8. Allen, Jeffrey S. (2026). Monitoring AI Adoption in the U.S. Economy. FEDS Notes, April 3, 2026. Figures drawn from the Business Trends and Outlook Survey, the Real-Time Population Survey, and the Survey of Business Uncertainty.

Issue 280, September 3, 2026

Which of these measures would you remove?

I’ve started asking that in every room where a dashboard is on screen, and the answer is almost always the same silence. Not defensiveness. Not annoyance. But puzzled looks, as if I’d asked which of their body parts they’d like to give up. Which for me is always a fun visualization as I wonder what they might say.

Then, as one might expect, the reasons start to arrive like clockwork. This one feeds the board report, the CEO says, so it has to stay. That one’s wired into the compensation formula, the Chief People Officer adds. This one’s pulled by an integration somebody in finance built two years ago for a CFO who has since left, and nobody in the room can fully explain it. And when I ask what they actually and truly inform, or what anyone in the building does differently because of them, nobody seems to know. In one session, a finance director finally said the quiet part out loud. “I assumed somebody needed it.”

But they have always existed, or they exist because...fill in the blank here.

That is the reason for this issue, because what I keep watching is not a failure of analytics, and it isn’t a lack of discipline. It’s something stranger and far more common. These organizations have built a measurement system they know how to add to and have no way to subtract from, and the pile has started making decisions nobody signed off on. I have come to think of it as a measurement ratchet, and once you can see it, you will see it in every organization you walk into, including your own.

The Initiative Dies. The Reporting Lives On.

A few weeks ago, in After the Theater (Issue 277), I laid out a ninety-day sequence for organizations that have recognized Transformation Theater in themselves and want to do something other than launch another working group at it. That issue was about stopping things and being honest about how much change a group of humans can absorb at one time. It ended with a question. Which initiative would your organization kill first, and what has stopped you from ending it already?

This issue points that question somewhere harder.

Here’s what I’ve watched happen over and over. An organization does the difficult work. It runs a portfolio audit, it names the initiatives that have quietly died, it stops three of them, and it earns back some trust from the workforce. Real and substantive progress. No question.

And then, a year later, dashboards in use across the organization still carry the measures those initiatives installed. Nobody noticed where they came from, only that they were there and should remain there.

The initiative ended. The reporting obviously didn’t.

That’s the engine underneath a great deal of what we call change and transformation fatigue. Initiatives have sponsors, which means ending one is a conversation with a human being or group of human beings who will be disappointed. Those conversations are hard but at least they are possible. A measure frequently has no sponsor at all. Which sounds like it should make removal easier but does exactly the opposite, because there’s nobody willing to take a stand to end it as it is so ingrained in so many different ways across the organization, the board, the market, and yes, even the public.

We Reach for Numbers Where Trust Is Thin

To understand why, it helps to go back a bit over thirty years.

In 1995, John Kotter published Leading Change: Why Transformation Efforts Fail in Harvard Business Review, and the diagnosis he offered has been quoted in nearly every change management deck produced since. That same year, the historian Theodore Porter published Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Porter set out to explain why modern institutions are so committed to counting things. The conventional answer is that numbers spread because they work. Porter argues that this is close to exactly backwards, and he builds the case through the history of actuaries, engineers, and the rise of the accounting profession.

His conclusion is that quantification is what he calls a technology of distance. Reliance on numbers, in his words, minimizes the need for intimate knowledge and personal trust, which is what suits it to communication that travels beyond a community that knows one another.

Numbers do their heaviest work where authority and trust are thinner than they should be. Humans love numbers; we love counting, we generally like positive numbers over negative numbers, we rely upon numbers to generate the feeling of accomplishment or completion. We love numbers to measure performance across nearly everything in life and business. (Ask anyone wearing a step counter how they feel at 9,400, and here I admit I am very much one of those people.)

Writing about the engineers who built cost-benefit analysis, Porter concluded that the regime of calculation was imposed not by all-powerful experts, but by relatively weak and divided ones. The appeal of numbers, he wrote, is especially compelling to officials who lack the mandate of a popular election, or divine right.

Which inverts the framing most of us carry. Organizations don’t adopt elaborate measurement because their judgment is strong. They adopt it because their judgment is exposed.

Two diagnoses published the same year, thirty-one years ago. One of them we quote constantly and haven’t absorbed. The other we never read at all. I’m not sure which is the more reflective of the challenges our very humanness creates in today’s environment.

There is, however, a distinction underneath this that I use constantly with clients. Picture a veteran surgeon asked why she chose a procedure, then a new hire asked to produce the protocol she followed. Same question, two very different relationships to trust. Some groups get to make a call and explain it afterward. Others have to show their work up front, in a form anyone can check, because nobody will take their word for it. Lorraine Daston and Peter Galison named that second thing in 1992, mechanical objectivity, and Porter picks it up and runs with it. Rules pile up where the standing is thin.

Put that into a conference room you’ve actually sat in. A leadership team that trusts its own judgment, and is trusted by its board, argues and then decides. A team that isn’t trusted builds a scorecard. The scorecard is a way of asking permission without having to ask anyone for it. None of which is a criticism of the people involved. Building it is a rational response to the conditions they believe they are in.

When Numbers Got Cheap

Here’s where Porter’s world and ours part company.

The institutions he studied operated under very different conditions. Numbers were scarce, slow, expensive to compile, and hard to argue with, and that scarcity was itself the source of their authority. We went the other way entirely. Every system in your organization now produces data whether anyone asked it to or not, and a dashboard that would have taken a department a quarter to assemble in 1975 assembles itself before you finish your morning coffee.

Think about what happened to photographs. When the film was expensive, and a roll gave you maybe twenty-four shots, you thought before you took the shot, and you didn’t know what you shot until it got developed. Now we take four hundred shots on a Saturday and look at none of them. The pictures got cheaper, and they got less interesting to us at the same time. Numbers went the same way, and we never really adjusted. The conditions have changed completely, but our behavior at its core didn’t change at all.

One more difference is worth putting out there. Porter’s engineers and actuaries built their own instruments. Ours increasingly arrive from vendors, with a maturity model attached and a certification available. Trust comes bundled with them. The data is assumed good, and whatever it produces is assumed authoritative.

Let me give you an example that should rattle that default. The Blackbaud Institute’s June 2026 report Bridging the AI Effectiveness Gap, based on March 2026 surveys run with Edge Research, reports that the average organization saves roughly $503 per employee per week using AI. The number looks authoritative. It came from a named institute, it has a research partner attached, and it is precise enough to benchmark against. And to be fair to the report, it is transparent about how the figure was built in a way most vendor research is not. Respondents were asked what an hour of their time was worth and how many hours a week they saved, and the two were multiplied.

Now look at what the arithmetic does. Two guesses, one of them a guess about your own hourly worth, become a dollar figure carried out to the single digit. The calculation is real. It is also entirely a property of the multiplication and has nothing to do with the world. That’s mechanical objectivity showing up in 2026 exactly as Porter described it in 1995. You’ll find the same construction in every industry’s productivity claim, including the ones your organization has made and has continued to take actions upon or leverage for decision-making.

I fell into the same trap myself. For years I cited a figure on the cost of unproductive meetings that turned out to have no locatable primary source at all. I’d seen it everywhere; it lodged in my mind, and I fell back on it again and again. When I went looking for more on the topic, I found it traced to a magazine item from 1989. It circulated for decades because it was specific, and specificity reads as verification. I pulled it, which was uncomfortable, and which is the smallest possible version of the thing this issue is about.

The Ratchet, Not the Pendulum

When I started drafting this issue, I saw the situation as a pendulum. Organizations swing from too little measurement to too much, never finding the middle. It was a comfortable framing, and the longer I sat with it, the more clearly I think it’s wrong.

A pendulum comes back. Measurement infrastructure doesn’t. In more than twenty years of this work, I’ve watched hundreds of measures get installed and can count on one hand the ones I’ve watched get deliberately retired, despite advice, counsel, very direct statements, and even lengthy assessments that should have changed minds. What we contend with is a ratchet. It turns one way, holds where it stops, and every turn adds to the load.

And the weight isn’t the whole problem. This is the part leaders consistently underestimate. Every measure that survives its first year gets wired into something else. A monthly report. A compensation formula. A cell on a board slide the board has come to expect. An integration built to feed it. Somewhere along the way it turns up in a grant commitment, a client agreement, or a regulatory filing, and at that moment it stops being a measure and becomes an obligation.

Each of those attachments turns removal into a negotiation with people who aren’t in the room. So by the time anyone notices a measure has outlived its usefulness, removing it is no longer an analytical act. It’s a political one. And so it stays, steering decisions from inside the plumbing, long after anyone could tell you what it was for.

Which raises a question worth sitting with. Who in your organization is allowed to remove a number? Not question it in a meeting. Not footnote it. End it. Can you name the person? Would they know it was their job?

A measurement system without a mechanism for retiring measures is not a measurement system. It is sediment.

What the Sediment Costs Now

For most of the time I’ve been doing this work, the cost of the ratchet was attention. Ninety minutes of a review covering eight of forty measures. Executives reading dashboards the way the rest of us read terms of service. That cost is real, and it’s the one most leaders would name if you asked them.

Two others matter more, and the second is newer than people think.

The first is integrity. A pile that grows without anyone tending and curating it eventually degrades, and it degrades quietly. In the Blackbaud study I mentioned earlier, fewer than twenty percent of professionals rated their organization’s data health as excellent, and the rate was roughly double that at the organizations the study called AI-Adaptive. I wouldn’t read that gap as a story about AI maturity so much as a story about data and measurement housekeeping. Organizations that only ever add have worse data, for the same reason a house where nobody takes out the trash has a horrible smell.

The second changes the stakes, and it’s why this argument opens a season of The Human Factor Podcast rather than sitting in the middle of one.

A neglected pile of measures used to just sit there. Wasteful, but inert. It isn’t inert anymore. Your AI systems read it, summarize it, and act on it. Every abandoned metric, every stand-in that stopped tracking what it was built to track, every field somebody quit maintaining in 2021, now feeds a system that hands it back in fluent, confident prose with no hint of how old it is or where it came from. And we act on it.

I wrote in The Confidence Transfer (Issue 271) about how quickly people defer to a confident answer. Put that on top of a sedimented set of measures, and you get something worse than either problem alone. The machine doesn’t know which of your numbers are load-bearing and which are fossils. It treats them identically. And your people, reading the output, are two steps away from the judgment that produced the measure underneath, if any judgment ever did.

Two issues ago, in The Proxy Collapse (Issue 278), I made the case that when the real thing is hard to measure, we settle for a stand-in, and then forget it was ever a stand-in. A fossil metric is that forgetting, left to age. The pile we can count keeps growing. The judgment that would tell us which part of it matters hasn’t grown at all.

The Retirement Practice

Let me be clear about where I stand. I’m a measurement believer. I write and talk a great deal about measuring what matters. Measurement is how we assess progress, function, and impact, and an organization that can’t tell whether it’s getting better isn’t humble; it’s blind. The problem was never that organizations have too many numbers. It’s that they have numbers nobody owns. That is a solvable problem, and the rest of this issue is how.

Tom Serena, closing out last season of the podcast, gave a new leader the best advice on this I’ve heard from anyone. Don’t come in to transform. Come in to find out what needs to be transformed.

I want to apply that literally, because it’s the whole practice in one sentence. A leader who arrives and redesigns the measurement system is coming in to transform. A leader who inventories what the organization actually produces, and asks what each item is for, is coming in to find out. The second takes longer and it’s the only one that works. You don’t have the standing to retire a measure until you understand what it does. Standing isn’t conferred by title. It’s earned by having looked.

Every measurement conversation I’ve been part of eventually arrives at whether a measure is still relevant. It’s the fourth of the four questions in the Measuring What Matters work, and by far the hardest, because answering it honestly requires a human being to stand behind a removal and say why. Adding a measure spreads the responsibility around. Removing one puts it on a single desk. And nobody has ever been fired for adding a metric.

So the practice has to make removal survivable rather than heroic. Here’s how I run it.

Inventory what is actually produced, not what is on the dashboard. The dashboard is the visible layer. Underneath it sit the standing reports, the scheduled exports, the board attachments, and the spreadsheet somebody keeps by hand every Thursday because a director asked for it in 2019. Expect the real list to run two or three times the size everyone believed. That surprise is itself the finding.

Put each item through the four questions. What is the purpose of this measure? What decision or action does it inform? How does it connect to other measures? Is it still relevant? If the answer to the second one is that it informs nothing, you’ve found sediment, and you should say so out loud in the room.

Trace the attachments before you touch anything. For every candidate, establish who uses it, what formula depends on it, which outside commitment references it, and what breaks if it disappears. This is what separates a retirement practice from a spring cleaning, and skipping it is how good intentions blow up a compensation cycle.

Name the person who owns the removal, by name, in the minutes. Not the committee. Not the function. A person who says on the record that this measure has served its purpose and here is why it ends. Organizations will try hardest to skip this one, because it’s the only one that costs anybody anything.

Put it on a cadence and let the cadence carry the weight. An annual review that is expected, scheduled, and normal is a completely different social event from a purge somebody has to champion. The cadence is what makes the previous step survivable, because the person doing it is following a standing practice rather than attacking a colleague’s work.

Measure the first cycle by what stopped. Nothing else. Longtime readers will recognize that line from Issue 277. The repetition is deliberate. It’s the same test applied one layer down.

Two cautions before you run this, because both have bitten people I work with.

The first is continuity. A metric with eleven years behind it can answer questions a new one can’t touch for another decade, and the person proposing its removal is usually the person least able to see who depends on it downstream. Retire carelessly and you destroy the comparability that made the measurement worth having in the first place.

The second is what comes back. In Noise, Daniel Kahneman, Olivier Sibony and Cass Sunstein show how much human judgment varies on identical cases, and every measure you retire is a place where that variation returns. Some of it will be wisdom. Some of it will be mood, fatigue, favoritism, and the last conversation someone had in the hallway. That is an argument for retiring on a cadence, with the attachments traced and a name in the minutes. It is not an argument for leaving the pile alone.

And one more, because I’ve watched this get misused. Coming in to find out has no natural end point. Discovery is comfortable in exactly the way a pilot is comfortable. It threatens no one, it produces something to report, and it can be stretched out forever by anyone who would rather not decide. If finding out isn’t time-boxed, and doesn’t end in a decision a named person owns, it isn’t patience. It’s theater at a slower tempo, which is harder to spot and worse.

The Cost of the Middle

Both ends of this are attractive for the same reason. Scarce measurement lets authority rest on status and intuition. Abundant measurement lets it rest on the dashboard. The balanced position, the one we all say we want, is the only one where a person has to stand behind a call and explain it. The swing isn’t a failure to find the middle. It’s a long flight from what the middle costs.

So the real test of a measurement system isn’t what it captures. It’s whether anyone inside the organization could survive taking something out of it.

Season 3 Begins in September

The Human Factor Podcast returns next Thursday, September 10, and this issue is the argument that opens it. Season 2 ended on a sentence I keep coming back to. Transformation isn’t an event; it’s a practice, and the shorthand for it is patience, repeated. Season 3 takes that as a theme, and the ratchet is that problem wearing different clothes. Adding happens at machine speed. Subtracting happens at human speed, when it happens at all.

Subscribe wherever you listen, on Apple Podcasts, Spotify, or YouTube.

And as the season gets underway, I’d like to hear from you. Tell me about a measure your organization actually retired. Who owned the removal, what broke, and what it cost the person who put their name on it. I’m collecting these across the season, and I’ll bring back what I learn.

Related Reading

After the Theater (Issue 277). The ninety-day sequence for organizations that want to stop things rather than launch something at the problem. This issue is what happens to the instruments those stopped initiatives leave behind.

The Proxy Collapse (Issue 278). A fossil metric is what this one looks like after it has aged.

The Confidence Transfer (Issue 271). Why deference to a confident machine answer happens by default rather than by discernment. Read it alongside this issue and a sedimented data estate stops looking untidy and starts looking dangerous.

Transformation Theater (Issue 275). How the performance of change substitutes for its substance. Elaborate apparatus is the tell.

The Permission Paradox (Issue 269). An organization’s instruments show a workforce waiting to be transformed. The workforce had already transformed itself in private.

Measuring What Matters. The four questions used in the retirement practice above, in their original form.

The Transformation Psychology Series. The research underneath the identity threat that shows up whenever someone defends a measure they built.

The Transformation Readiness Assessment. Worth a baseline before you pull on any of this.

On The Human Factor Podcast

From Diagnosis to Practice, Season 2 Episode 031. The Season 2 finale, tracing the arc of eighteen episodes and previewing where Season 3 goes.

The Long Game, Season 2 Episode 030. Tom Serena on pace and patience as the denominator of change, which is the argument standing behind this issue.

The Measurement Ratchet: Why Nothing You Measure Ever Leaves. Season 3, Episode 1 (Coming September 10, 2026). The companion episode, out September 10, going back seventy years to the research that called all of this in 1956, and further into what the measurement retirement conversation actually sounds like in the room.

Research Sources

1. Porter, Theodore M. (1995). Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton, NJ: Princeton University Press.

2. Daston, Lorraine and Galison, Peter (1992). The Image of Objectivity. Representations, No. 40, Autumn 1992, pages 81 to 128.

3. Blackbaud Institute and Edge Research (2026). Bridging the AI Effectiveness Gap: New Research on What Drives AI Impact and Trust in the Social Sector. Published June 24, 2026. Parallel surveys of 1,389 social impact professionals and 1,034 donors, United States, fielded March 2026.

4. Kahneman, Daniel, Sibony, Olivier, and Sunstein, Cass R. (2021). Noise: A Flaw in Human Judgment. New York: Little, Brown Spark.

5. Kotter, John P. (1995). Leading Change: Why Transformation Efforts Fail. Harvard Business Review, March/April 1995.

Join the Conversation

LinkedIn: https://www.linkedin.com/company/2040-digital

20Forty’s Newsletter

My weekly newsletter focuses on organizational change and transformation majorly representing the criticality of the human factor and behavior in any initiative.

By Kevin Novak

The Truth About Transformation

The Truth About Transformation: Leading in the Age of AI, Uncertainty and Human Complexity. The book behind the method, on why most transformations fail and what changes when psychology comes first.

Issue 279, August 27, 2026

A client asked me a question earlier this year. It wasn’t about strategy. It wasn’t about technology, nor about any of the things we are usually brought in for. She asked how she was supposed to plan anything at all.

Her material and operational costs had increased three times in eighteen months. Her pricing had to follow suit as there was little wiggle room in her internal finances. The market answered her by buying less. It is a situation that has affected many organizations of late as financial pressures exist upstream and downstream with seemingly no sign of abating.

Her people were asking for raises because their cost of living was becoming higher and higher. She understood completely as she was feeling her own personal financial pressure, but there wasn’t anything she could do to accommodate their requests.

Two roles she had approved in the spring were still unfilled, and she was no longer sure she would fill them, partly because of budget and partly because of a quieter question nobody in her leadership team wanted to say out loud, which was whether those jobs would exist in the same shape in two years.

She wasn’t panicking. That’s what surprised me. She was competent, clear-eyed, and completely without a stable set of assumptions to build on. She knew it and simply didn’t have the tools or approaches to navigate forward and generate a plan.

That’s the condition I want to dive into this week, because I think it’s the actual environment most of you and your organizations are operating in right now, and because almost none of the current conversation about change and transformation acknowledges it. We talk about change as if organizations always choose it. We talk about transformation as if an organization always chooses it. Increasingly, they don’t. The catalysts for change and transformation are arriving from outside, through prices, through policy, through labor markets, through a technology that’s simultaneously a genuine capability and a convenient reason to stop hiring people.

What This Issue Is

So this week I’m doing something I do a few times a year by drawing from the archive, now past 278 issues, to trace a thread that runs through a great many of them and that has moved from background to foreground.

That thread is conditions. Not strategy, not capability, not intent. The environment surrounding the work, and what impact it has on how people decide, commit, and change.

The pieces offered below were written across five years and several very different societal and business moments. Some were written during a period of optimism that now reads like a foreign country. That’s part of why I’m pulling them forward. Reading what we believed in 2022, 2023, 2024 and even last year against what we know in 2026 is itself a lesson in how quickly the ground moves, and in how confidently we all stood on it at the time.

Four movements, ten pieces.

One. The Conditions Are the Story

Start where most analysis doesn’t, which is with the environment rather than the organization.

Unintended Consequences: Decision-Making and Economic Interconnectedness is the anchor piece for everything that follows. Written in March 2024, it argued that linear problem-solving fails because we don’t trace how a decision moves through an interconnected system. Fix a price, a wage, a border, a supply chain in isolation, and the second and third-order effects arrive later wearing a different face. It walked through PepsiCo’s shrinkflation and Carrefour pulling its products across five European countries, California’s minimum wage and the restaurant prices that followed, Wendy’s short-lived experiment with dynamic pricing. The line I’d point you to is this one. We simply don’t see what we don’t see.

What’s changed is where the consequences originate. That piece assumed disruption accumulating from many independent actors: corporate pricing here, state wage law there, demographics underneath. Much of what’s arriving now comes from fewer sources, moves faster, and reverses. The method holds. The map of where to look for the next effect doesn’t.

Read this issue

Navigating Whiplash: Effective Leadership in the Era of Volatility is the most recent piece in this collection, and at fifteen months old, the oldest thing about it is that it treated whiplash as news. It examined what reversal does to an organization as distinct from what difficulty does, and the distinction still matters. Organizations are far more capable of absorbing bad news than repeated changes of direction. Bad news lets you plan. Reversal makes planning feel foolish, and people stop doing it.

Melanie Vargas put it in a sentence I’ve quoted since: leadership isn’t just about setting a vision and executing a plan, it’s about navigating an unpredictable sea of shifting priorities, conflicting values, rising emotional intensity, and, quite frankly, collective burnout. What I’d add now is that the piece framed adaptive leadership as the response to a period. It’s no longer a period. It’s the operating environment, and the response has to become a practice rather than a posture.

Read this issue

Inflation + True Value was written in February 2022, when 7 percent inflation was the first of its kind since 1982 and food staples had moved 12 percent in a year. The argument was that the reflex to raise prices has to be preceded by a hard look at what value you actually deliver, using outside data rather than internal assumptions, because a price increase unbacked by value gets read as opportunism and remembered as such.

I want to flag one line, because I wrote it as an observation and it has aged into something closer to a forecast. A perfect stormfront of sorts where increased costs are coming from multiple avenues and where there is no point in the future time where those increased costs may decrease. Four years on, that has largely held. What the piece did not anticipate is how many organizations would spend those four years raising prices without ever doing the value work, and how thin the trust account is now with customers and stakeholders as a result.

Read this issue

Workforce Reset, from later that same month, asked whether the workforce was overdue for a fundamental renegotiation. It assembled the evidence of one already underway: the anti-ambition reporting, median job tenure running under three years for workers in their late twenties, pay transparency narrowing the gender gap by as much as 45 percent in the organizations that adopted it, portfolio careers averaging something like twelve jobs across a working life. The through line was leverage. The employee is in the driver’s seat when it comes to reshaping the workplace.

The renegotiation did happen. The leverage moved, and then it moved back. What makes the piece worth rereading in 2026 is what’s missing from it entirely. There is no AI in that picture. The threat to workers in early 2022 was bad management, not automation, and the remedy was for employers to listen. Four years is a very short time for the terms of a negotiation and the psychological contract to reverse that completely.

Read this issue

Two. What Scarcity Does to Decisions

Here’s the part that gets underestimated, and it’s the human factor at the center of this issue. Constrained conditions don’t simply limit what an organization can do. They change how it thinks.

Survival Mode Leadership: The Hidden Costs of Managing by Fear is the most directly applicable piece in this collection. Its argument was that fear-based leadership isn’t strength; it’s insecurity wearing confidence, and that it degrades the organization it’s meant to control. When a leader operates from fear the time horizon collapses, discretion narrows, and the behaviors that produce long-term value are the first things cut. It carried the neurobiology, the ten consequences, and a statistic that did a lot of work: three-quarters of workers have left a job specifically to escape a toxic boss.

That statistic was also the piece’s safety net. It assumed a labor market where mistreated people can walk, which is what made fear-based leadership self-limiting. Take that away and the check disappears. Fear-based leadership under scarcity isn’t just more damaging; it’s more durable, because the people best positioned to name it are the ones least able to leave.

Read this issue

The Risk of Certainty matters more in volatile conditions than in stable ones, for a reason that runs against intuition. Uncertainty is uncomfortable, so under pressure leaders reach for conviction rather than accuracy, and the organization rewards them for it. The piece drew on Zakary Tormala’s work on attitude certainty at Stanford, and on the finding that people grow more confident in a position after defending it, not more correct. It also carried Scott Galloway’s reminder that the species that survive are not the smartest or the strongest but the most adaptable.

Here’s what I’d revise. In January 2024, I framed false certainty as something leaders choose because it’s comfortable. That was too generous to the conditions and too hard on the person. Conditions like these manufacture the demand for certainty. The board wants a number, the staff want a plan, the funder wants a projection, and the leader who says honestly that they don’t know is the one who looks unprepared.

Read this issue

The Misfortunes of Uninformed Urgency carries what’s become one of our longest-standing mantras at 2040. Speed kills with uninformed urgency. The piece built a case study of a media organization in panic, where the CEO hired a friend from a university, who hired a circulation consultant found through a Google search, whose credentials were a subscription model at a company barely holding above the water line. Every department got a contradictory mandate. Sales was told to produce $350,000 however they possibly could. Nobody surveyed the existing subscribers. And no one was talking to each other.

What that piece assumed was self-inflicted panic inside an otherwise functioning organization, with time still available to be deliberate. The urgency now is frequently imposed from outside and the timeline isn’t yours. That doesn’t retire the argument; it sharpens it. Acting quickly is often correct. Acting quickly on an unexamined premise is how you spend scarce resources confirming something that was never true.

Read this issue

Three. The Generational Stake

There’s a dimension of this moment that most business coverage treats as a side issue, and I don’t think it’s one.

Why You Need to Listen to Gen Z was written in spring 2023, and it lands differently now. Its argument was that a generation entering the workforce wanting meaningful work, mental health support, and mentorship rather than lecturing would leave the organizations that dismissed those expectations. The mentorship data was the strongest part: workers with mentors report satisfaction at far higher rates, and retention improves by half.

What dates the piece is the frame. Retention was the employer’s problem. The assumption was that this generation held leverage and organizations needed to adapt or lose them. It predates AI as a factor in entry-level hiring entirely, and entry-level is precisely where AI has landed hardest. A generation that was told to be patient, build skills, and wait its turn is now watching the first rung of the ladder get automated while being asked, by the same institutions, to remain optimistic. Read the piece with that in mind and the reasonable doubts stop looking generational.

Read this issue

The Future of Personal Agency goes underneath the generational framing to the thing actually at stake, which is whether individual effort still reliably connects to individual outcome. When that link weakens, engagement doesn’t decline because people became lazy. It declines because the arrangement stopped being true, and people are good at noticing when it does.

It was written in April 2023, a few months after ChatGPT arrived in public, and it treated 2035 as the horizon. The Pew canvassing at its center found 56 percent of experts expecting that smart systems would not be designed to let humans easily control tech-aided decision-making. Three years in, a good deal of that has arrived early. The line I’d carry forward is the one that reads as a warning and a design principle at once. Whoever writes the code controls the decision-making and its effects on society. Thriving through this requires more agency, not less, and agency is the thing conditions like these quietly withdraw.

Read this issue

Four. Measuring What Matters When the Variables Won’t Sit Still

Which brings us to the question of measurement. If the factors and variables are changing this quickly, how do you measure anything at all?

Measuring What Matters: Navigating the KPI Labyrinth in an Era of Information Overload contains the four questions I still use with every client. What is the purpose of this measure? What will this measure inform? How does this measure connect to other measures? Is this measure still relevant? The piece argued for curation over collection, asking why before what, and building measures that connect rather than sit as isolated tiles. The metrics that got you to here won’t get you there.

In stable conditions, that fourth question is an annual housekeeping exercise. In these conditions, it’s the whole discipline, and it’s the hardest of the four because answering it honestly requires a person to stand behind a removal. There’s also something the piece didn’t contemplate, written as it was for a human reading a dashboard. Your measures are now being read, summarized, and acted on by systems that can’t tell a live measure from a fossil. I’m taking that up properly next week.

Read this issue

And here’s the answer I’d offer to the question directly, because I don’t think it’s the one people expect.

When conditions move quickly, the instinct is to measure more frequently and more broadly, on the theory that a faster-moving world requires faster-moving instrumentation. I think that’s backwards. Faster measurement of unstable quantities produces noise that feels like information, and the organizations I watch struggle most aren’t the ones measuring too little. They’re the ones responding to every fluctuation as though it were a trend.

What holds up when the variables won’t sit still is measurement of the things that change slowly. Trust. Whether people say what they know. Whether decisions close and stay closed. Whether capability actually converts into work. Those are not soft measures. They’re the ones with the longest half-life, and in a volatile environment the long half-life is precisely the point. Anything you can measure this week that will be irrelevant next quarter was never worth building a dashboard around.

What Holds Up

I’ve watched a few things hold when conditions didn’t.

Organizations that stay honest with their people about what’s actually happening retain them at rates the compensation data alone can’t explain. Organizations that keep one commitment slowly, rather than announcing five, rebuild credibility that later becomes the capacity to change. Organizations that treat resistance as information rather than obstruction find out what’s wrong early enough for the knowledge to be worth something.

None of that is a strategy for good conditions. All of it is a practice for bad ones, and the difference between a strategy and a practice is exactly what the next several weeks here will be all about.

Now, The Question

We spend most of our attention on what an organization intends. Strategy, vision, roadmaps, the language of choice and direction.

Almost none of it on what surrounds the organization while it tries to act, even though the conditions decide how much of any intention survives contact with reality.

So rather than end on a question I know you can answer, let me ask one I can’t.

What conditions is your organization actually operating in right now, and how honestly have you named them out loud to the people who have to live inside them?

Write back and tell me. I want to know whether what I’m seeing across client work matches what you’re living through, and I’ll share what I learn in a future issue.

Leave a comment

Related Reading

The Case for Strategic Patience (Issue 243). Why knowing when to hold back is a discipline rather than a failure of nerve, and why volatile conditions make it harder and more necessary at once.

The Consequences of Unbounded Optimism. What happens when hope substitutes for planning, and how to hold both without letting one dissolve the other.

Where Has Our Optimism Gone?. Written in a different mood, and worth reading now for what it reveals about how fast a collective outlook can turn.

How Drift Can Derail an Organization. What happens when nobody decides to change direction and the organization changes direction anyway.

Reactive Decision-Making, Social, and Youth. The reactive patterns that pressure produces, and where they show up first.

The Distortion of Time. Why organizational time and human time run at different speeds, and what breaks at the seam.

On The Human Factor Podcast

The Long Game, Season 2 Episode 030. Tom Serena on pace and patience as the denominator of change, and on why you rarely control the pace.

From Diagnosis to Practice, Season 2 Episode 031. The season finale and the map of where Season 3 goes.

Season 3 begins Thursday, September 10. The opening episode takes up the measurement question directly, and the season is built around practice: what it takes to build the human factor into how an organization works when nobody controls the conditions. Subscribe on Apple Podcasts, Spotify, or YouTube.

Issue 278, August 20, 2026

In the last days of July, a debut crime novelist named Jerry Falade lost just about everything a writer can win. His first book had sold in a fourteen-publisher auction, a two-book deal reported at $2 million. Film interest was circling. Then his own agents withdrew it.

Not because a detection tool flagged it. His agency said it could not authenticate how the manuscript had gone from an idea to a finished book. That was enough. The book was simply doubted.

Falade denies writing it with AI, says he used an open-source model running locally for research only, and points to time-stamped drafts, notes and dated messages documenting how the book came together. He has also said he believes the scrutiny came for him because he is Black. That claim runs through much of the coverage, I cannot settle it from the outside, and I raise it because leaving it out would be its own kind of editing.

He also ran an experiment worth holding in your mind as you read this issue. He told The Bookseller he fed passages from books published in 2012 and 2017 into detection software, and it classified them as 98 and 99 percent AI-generated. Text written years before these tools existed.

It didn’t matter, of course. Within days, deals that would look too good to be true to any aspiring writer were coming apart.

Whether Falade used AI, we may never know, and that’s exactly the point. He was asked to prove a negative. Once the doubt was loose, there was nothing he could say to it.

That’s the world every writer works in now, and increasingly every professional.

A word before you go further. This one runs long. The topic is tangled, and there’s more of the human factor packed into it than I can do justice to at my usual length, and I’m trying not to leave pieces of it on the floor. I think it’s worth your time. We are all walking into something none of us understands yet, personally, professionally, organizationally and as a society.

This is not an AI newsletter. The subject is, as always, the human factor, but of course the growing role AI is playing in our society is creating a complex case study in real time.

The Scanner Arrives

A week before the Falade story broke, Substack handed every reader a button.

Scan any post on the platform and a company called Pangram returns a percentage. This much human, this much AI. Writers can scan their own drafts, dispute the result, add a statement describing how they work, or switch detection off entirely.

Credit where it’s due to Substack. As these systems go, it’s a thoughtful design. It favors disclosure over punishment, and it quietly concedes something much of the industry still won’t say out loud, which is that AI involvement is now a given across nearly every sector of the economy, publishing included, and that it runs on a spectrum rather than a yes or a no.

But one omission shapes everything. Nowhere does the reader see the tool’s error rate.

A percentage looks like a fact, and there’s no way around that perception. It is a guess dressed up as one, produced by tools that are nowhere near as good as the number makes them look. I’ll come back to exactly how far off they can be.

I turned the scanner off for my own publication, and I’m well aware of how that looks.

So let me say what regular readers already know, because I’ve written about it at length in the three-part Artificial Understanding series this spring, on the comprehension gap, the human cost of it, and what feeds the machine (Issues 260 to 262). I work with AI in producing this newsletter. It’s my research assistant and my production support. It is an efficiency that has become very helpful.

This newsletter has never been made alone. For years, it was made with an editorial partner whose voice is still in my head for nearly everything I write, whether that’s an email, a report, a proposal or this newsletter. I learned that much from her. My only regret is that I didn’t have her as an editor earlier in my career, despite my own confidence in my writing, and despite how often other people asked me to write for them.

Today AI sits in that same chair. And here’s the funny thing.

The same things she would delete, my AI editor deletes. A passage that’s off voice. An analogy that only connected inside my own head. The informal word I dropped in to loosen up a serious subject. Same places, same reasons.

I used to push back on her. She would explain. I would lose what I thought of at the time as a battle. I have writing blind spots, and she helped me enormously with them. Now I mount the same battle with a machine, and yes, I still lose. Then and now, for all the right reasons.

What has matured in this phase, and you may catch it week to week, is more formality, more research, and a denser exploration than I used to write. Hold onto that. It comes back later in this issue in a way I did not expect when I started writing it.

The archive behind this newsletter runs to 277 issues. Two hundred and forty of them were written before I brought AI anywhere near this work, which I did not do until November of 2025. I had the same hesitancies most people did, and I held onto them longer than most. My books predate the tools entirely. The ideas, the positions, the arguments, the insistence that something needs saying, the judgment and the final word are always mine, and always have been.

What I declined wasn’t transparency. I’ve offered more of that than any scanner will ever produce. What I declined was being held up against something that can’t tell the difference between a writer who thinks with a machine, a writer who simply writes formally and technically by training and habit, and a machine with no writer behind it at all.

The rest of this issue is about why that difference is the whole question. Not just for publishers, not just for authors, not just for writers. For anyone, in any capacity, personally and professionally.

The Marks Are Coming Everywhere

Substack’s scanner turned out to be the opening act.

On August 11, Anthropic announced that its Claude models will embed an invisible watermark in the text they generate. The idea is clever, and we have been here before with watermarks, tags, files locked against editing, and the rest of it.

The mark doesn’t live in the file, where copying and pasting would strip it out. It lives in the words themselves, in a hidden pattern woven through the model’s choices among equally good phrasings. Copy it, paste it, change the format, and the mark travels along. Rewrite it heavily, and the mark dissolves, because it only exists in the words that survive intact. Break the phrasing, and you break the pattern.

Anthropic concedes the limits, and states them more plainly than I expected. The absence of a mark doesn’t mean a human wrote it, because editing removes the signal. And a mark doesn’t mean a machine authored it. What the mark establishes is that text was processed by Claude, which is not the same claim at all. The model may have only helped edit.

Touched by a machine and thought by a machine are different things, of course. A watermark can only ever speak to the first.

What it can’t speak to, at least not yet, is the consequence. The label, the perception that follows the label, what a reader does with it. What a superior may do with it, or what a superior may think. In these early days it seems premature, and closer to reflex than to sound judgment.

And what strikes me is how much of this is driven by discomfort, anxiety and the identity threat I write about so often. Even if the hype never fully proves out, AI will have significant impact for one reason alone. It put capability in the hands of people who did not have it before.

The direction of this path runs one way, and it has already arrived. The EU’s AI Act rules on this took effect on August 2 of this year. Anything a model produces has to carry a mark a computer can read. Systems already on the market have until December 2 to comply. Anthropic’s announcement came nine days after that deadline landed, which tells you what it was responding to. China got there first, and its labeling rules took effect last September. LinkedIn is testing reader-facing detection. Marks are being built on three continents, with deadlines and budgets behind them.

What is being built nowhere, with no deadline and no budget, is any shared understanding of what a mark actually means. What it should mean. What questions it ought to raise. Whether it means anything at all in our race to leverage AI for all things.

The technology moves, and the literacy doesn’t, and the gap keeps widening. I have written about that pattern more times than I can count, and I watch it happen in person every semester. Students arrive not knowing they live inside a filter bubble. They trust search results with no sense of how ranking decided what they saw. Over ten years of teaching, I have watched that awareness build across a few weeks, and every so often a student walks out of it wanting to go tell everybody else. That is what the gap looks like when somebody closes it.

Now many people trust what AI tells them without question. With our dwindling attention spans, the consequence of that is simply scary.

So, hold onto that gap I mentioned. It’s where the real damage, and the collateral damage, gets done.

The Proxy Collapse

For most of history, good writing was reliable evidence of thinking, and for a simple reason. Producing it was hard work. I can attest to that. Edit after edit. Confirming sources. Making sure a quote is used the way its author meant it. Matching the language to whoever was reading it, because legal does not talk like procurement and neither of them talks like engineering. Checking that the research and references underneath would hold up.

A clear, sustained, well-sourced piece of thinking could only come from a mind that had done that work. Readers never had to check this consciously. The difficulty was the proof. Publishers and universities built their gatekeeping on it, through peer review and official journals and portals. Writers built something more personal on it. An identity, a readership, a following, and most importantly, trust.

Generative AI didn’t break the thinking. It broke the proof. Fluent text can now be produced at almost no cost by anyone, with any amount of thought behind it, including none at all.

That last part is the scariest of them all.

I call this the Proxy Collapse, and I want to be precise about what collapsed. Original judgment, tested expertise and accountable analysis are as scarce and as valuable as they have ever been, arguably more so as the flood of fluent text keeps rising. I raised a version of this years ago, as these tools were climbing, when I asked whether critical thinking was at risk of extinction.

What collapsed isn’t the thinking. It’s our ability to judge the thinking from the surface of the text.

The written word no longer certifies anything.

Seen that way, the detection industry comes into focus. These tools are an attempt to restore the old proof by force. To restore what was comfortable, what was understood, what was valued. Certify that the written word was made by hand and we can go back to trusting it as evidence of thought.

I understand the impulse. It won’t work. Hand-typed text is not evidence of thinking. Machine-assisted text is not evidence of its absence. The thing readers actually care about was never in the keystrokes.

Longtime readers will recognize the shape of this from the Measuring What Matters series. When the real thing is hard to measure, we measure a stand-in. Then we completely forget it’s a stand-in.

And then something worse. The stand-in becomes what people know, and what they base their thinking on. Whether it was ever right stops mattering, because by then it is simply accepted as the way things are. Norms get set and stop being questioned. Think of how often a child asks a parent why, and the answer comes back as well, that is just the way it is.

We Have Been Here Before

Before we accept that assisted writing is some new deception requiring new policing, we should be honest about the practices we already have, and have had for a century, though I would suggest they have existed as long as we have had spoken and written communication.

There has always been something one could call deception in these arrangements. There has also always been an accepted set of practices that nobody gave a second thought to, and probably never did.

Carolyn Keene, author of the Nancy Drew mysteries that three generations of readers grew up on, never existed. A publishing syndicate invented her in 1929, outlined the plots, and hired writers to produce the books. Mildred Wirt Benson wrote twenty-three of the first thirty. She was paid $125 for the first manuscript, under a contract that handed over every right to the text and to the name and required her silence. Her role stayed hidden until she testified in a court case in 1980.

When the novelist V.C. Andrews died in 1986, her estate hired a ghostwriter, Andrew Neiderman, to keep producing books under her name. He has been writing them ever since, for close to forty years now.

I read a lot of those books. They passed around my extended family and became the subject of conversation whenever we got together. I just assumed V.C. Andrews wrote them. I didn’t even know she had died. Who the author was didn’t seem to matter nearly as much as the complicated stories those books told and the shared experience of reading them with family members.

Below the level of books, the practice is so ordinary that we stopped seeing it. The executive’s op-ed drafted by an agency. The CEO’s letter assembled by staff. The leadership memo polished by a chief of staff. We have done that work for clients over the years, and you can add press releases, scripts and speeches to the list.

Nobody scanned those. The industry didn’t call it fraud then and doesn’t now. It was understood as collaboration and as putting honed expertise to work.

I call this the Ghostwriter’s Exemption, and its unwritten terms are simple. Writing by proxy is acceptable as long as it stays expensive and invisible.

What AI did was hand a version of that ghostwriter to anyone with a monthly subscription. The norm didn’t change. Access did. And some of the alarm right now, including the sudden appetite for detection, is the discomfort of a gatekeeping economy watching its private privilege go retail.

One distinction between the human and the machine is worth keeping in front of you. A human ghostwriter exercises judgment and can refuse to make things up when they have the editorial standing to do so. A language model will invent a source with exactly the same fluency it uses to report a real one, which is why machine assistance demands more verification from the named author, not less.

But notice where that lands. It puts the full weight of trust on the named human’s diligence, which is exactly where it has quietly rested all along. Nobody who admired a ghostwritten memoir believed the celebrity typed it. They believed the celebrity stood behind it. They understood that somebody had helped tell the story, and they lost themselves in it without a second thought.

The typing was never the trust. The name was.

There is one more thing sitting under all of this that I’m not going to resolve here, because it deserves its own issue and I intend to give it one. A model can hand you an invented source. It can also hand you words that belong to a real person, or an idea with its author quietly removed, and you would have no way of knowing. A detection score will catch none of the three.

What the Tools Actually Measure

To understand who these tools misjudge, you need to know how they work, and the plain version is this. They don’t know where your words came from. They guess, based on patterns. Text that’s smooth, conventional, and even-keeled resembles machine output. Text that’s uneven, surprising, and idiosyncratic reads as human.

Now think about what that means for careful writing.

Citations follow standard formats. Phrases like “the study found” are formulaic because they have to be. The measured tone of evidence-based work deliberately files off the quirks these tools read as human. And the models were trained heavily on exactly this kind of writing, so executing it well makes your work resemble theirs.

The result is backwards. Disciplined, sourced writing scores as machine-like. Loose, unsupported opinion scores as human. The tools systematically penalize the writers doing the most careful work.

This isn’t theory. OpenAI, which understands its own models better than anyone, pulled its AI text classifier in July 2023, seven months after launching it, citing low accuracy. In its own disclosed testing, the tool caught 26 percent of AI-written text while labeling human writing as AI 9 percent of the time. A Stanford analysis published that same year ran ninety-one English proficiency exam essays, all written by students for whom English is a second language, through seven detectors. On average, the detectors called 61 percent of them AI-generated. Almost every essay in the batch was flagged by at least one. Also that year, Vanderbilt turned off Turnitin’s AI detector entirely. Its reasoning was simple arithmetic. Vanderbilt had run 75,000 papers through Turnitin the previous year. At the 1 percent error rate the vendor itself claimed, roughly 750 of those students could have been wrongly flagged.

Read that again. The people most likely to be flagged for not writing their own work are the ones writing in a second language.

I know this territory from both sides.

As an adjunct professor, I’ve used Turnitin on student work, and I know its usefulness and its blind spots. A decade taught me one rule I’ve never regretted: the tool informs the judgment, it should never be the verdict, and the diligence is still yours to do.

You could fairly ask how that sits with turning the scanner off on my own work, and it deserves a straight answer. A student is being assessed on whether they can do the thinking. That is the entire point of the assignment. You are not assessing me. You are deciding whether to trust me, and you have an archive, a podcast and two books to decide from. Different questions, different tools. What I will not do in either case is put a number in front of a person and call it a verdict.

And I’ve had this happen to me. Some months back I published an issue of this newsletter with Grammarly running in the browser, and its new AI detector announced that entire sections were AI-written. I had written every one of those words myself. There was no appeal. Just an immature tool’s verdict on one side and my own memory of writing the piece a few days earlier on the other.

To be fair, Pangram is better than the earlier generation. It claims it wrongly flags human writing about once in every ten thousand times, and it admits its accuracy drops on short, formulaic text. Stay on that last part for a second, because formulaic text is very often academic and business writing. The same writing my two editors have slowly trained me to produce.

That one in ten thousand is the company’s own number, from the company’s own testing, and researchers have already published cases where it flagged human writing. Even if you accept it completely, the math at scale doesn’t change. Across millions of posts and millions of scans, a steady population of honest writers gets wrongly flagged, each one facing the same impossible task Falade faced, in front of an audience shown a number with no uncertainty attached to it.

It’s worth being clear about what these tools reward, because once you see it you can’t unsee it.

The first generation worked on statistics. How predictable is each word given the ones before it, and how much does that predictability vary across a document. Formal writing systematically strips out both kinds of variation.

Predictable vocabulary. Professional writing leans on precise terminology, clear transitions, and established phrasing. Those pairings are logical and common, so a detector reads them as highly predictable and therefore machine-like.

Uniform rhythm. Formal prose favors well-constructed, medium-to-long sentences with parallel grammar. You rarely swing from a thirty-word analytical sentence to a three-word fragment. That balanced pacing registers as low variation.

No noise. Good writing strips out the tangents, the emotional outbursts, the slang, the typos, the rambling. Detectors treat those exact flaws as the proof that a person was there.

Read that list again and notice what it is actually describing. It’s describing an editor’s job.

I should be fair about where the technology has gone since. The newer tools, Pangram included, threw that statistical method out and trained systems to recognize machine text directly instead. Pangram has published its own argument for why the old approach fails. But every piece of evidence I laid out above comes from the years when the old method was the method. And the newer tools are answering the same question the old ones asked. Only the machinery underneath it changed.

I have worked on this issue since mid-July, for more hours than I can count and through twenty-five drafts, using AI and my own fingers for the research. I keep running aground on the same thing. What these tools call unevenness is precisely what I was taught to remove, so much so that as sentences form in my head, I stop myself from writing the slang, the rambling and the tangents.

The analogies I like. The informal word I drop in to loosen up a serious subject. The tangent that entertained me and would have entertained nobody else. My editorial partner cut those for years. My AI editor cuts them now, in the same places. Both of them were right.

So the machine reads my writing as machine-like because I was trained, patiently and correctly, by people who made it that way.

Funny thing. The quirks that would certify me as human are the ones I have spent a career learning to delete.

The Removal Industry

There’s another half to the detector’s predicament. These tools are losing an arms race, and this isn’t a prediction of mine. It’s already underway, and has been for a while.

An industry of “humanizer” tools has been operating since 2023, rewriting machine text to put back the unevenness that classifiers read as human. Research published this June in the International Journal for Educational Integrity measured the outcome across forty fully AI-generated papers and forty humanized ones. Turnitin scored every fully AI-generated paper below the study’s strict threshold, catching none of them. GPTZero caught one humanized paper out of forty. Pangram held at 92.5 percent on the humanized set, which is presumably a large part of why Substack hired it.

But the rewriting tools will now be tuned against the last detector standing, because that is what always happens next.

The same ready-made industry greeted Anthropic’s watermark. Within a day of the announcement, before Anthropic had even published a way to check for the mark, watermark-remover sites and bypass guides were live.

Sit with what was actually for sale that week. Removal of a mark nobody can yet detect, sold by vendors who can’t prove their product works, to buyers who can’t verify either. A fear market, open for business before the thing it feared had even been documented and put into practice.

Yikes.

I’ve watched this movie for thirty years in digital, and the ending doesn’t change. Digital rights management didn’t end music piracy. It lost outright, while a public ravenous for digital music on brand new devices did what it was always going to do. What ended piracy at scale was Spotify, Apple Music, Amazon Music, and the rest making the honest path easier and far more valuable than the dishonest one.

That’s the lesson the detection era keeps refusing to learn, at least so far. Punish the mark, and you industrialize the scrubbing. Make the mark unremarkable and the workaround economy never grows, because workarounds only spread when the honest path is expensive.

If you want one indicator to watch, watch the removal market. If it stays a fringe of relabeled paraphrasing tools, the honest path is winning. If it attracts real funding and advertises openly, the design has failed, and the fear has won.

The Platform’s Problem Is Real

It would be easy, and wrong, to treat all of this as an unforced error by Substack. The platform has a real problem.

Since AI became widely available, Substack has watched a share of new newsletters arrive that appear to be entirely machine-generated. The platform is a revenue generator for some of its authors (2040 Digital publishes to Substack but not to generate revenue), and a subscription business rests completely on the reader’s belief that a person is on the other end. Readers who find out that a voice they paid for was automated feel deceived, and as I wrote in The Empathy Outsource (Issue 270), that feeling runs backward through everything the voice ever sent them.

Pangram’s own measurements make the scale concrete. Across a million social posts sampled this spring and summer, more than 40 percent of the longer LinkedIn posts came back as fully AI-generated. Longer articles on X came to almost half once AI-assisted writing was counted. Substack, the lowest of the platforms measured, still ran over a fifth once you count both. Worth saying out loud that these numbers come from the same company Substack hired, measuring the problem its product is sold to fix.

Now let me argue against myself, because I am in that data.

My social posts are machine-generated, and while I often edit them, I won’t pretend the drafting is mine. What I will say is what they are. Every one is distilled from an issue I wrote or an episode I recorded. They carry the points of something I actually wrote to another place people are looking. They are not posts with nobody behind them. It saves me time, it keeps the posts on point with what I actually want to say, and it helps me get the newsletter and the podcast in front of people who read, listen or watch.

Somewhere past that line sit the small companies now handing AI their entire social presence, their marketing and parts of their sales, with no source work underneath any of it. The distance between extending your own work and outsourcing your voice entirely is one of the truly unsettled questions of this moment.

The scanner is blind to it. My derived posts and a fully automated feed flag identically.

Substack’s own chief executive, Chris Best, conceded the deeper limit the day before launch. Pangram, he wrote, can only detect whether AI was used to make the text, not whether great human care went into creating it. Those are different claims, and only the second one has ever mattered to a reader.

The problem is very real. The tool counts the wrong thing.

And we do what people always do when something is wrong. We need to act, to put something in place, to be seen doing something about it. The subject I keep coming back to, year after year, is what that impulse costs us later. We are almost always blind to the consequences we did not intend.

The Question That Matters

In June, in The Empathy Outsource (Issue 270), I argued that for personal messages- the condolence note, the recognition, the hard piece of feedback- the authorship is the message. The whole point is that a specific person spent something of themselves on you, and the research shows the value collapses the moment the author becomes uncertain.

Analytical writing runs on a different currency.

When you read this newsletter, or any analysis, you aren’t really asking whether my fingers produced each keystroke. You’re asking whether what I put in front of you is worth your time. Whether it speaks to real issues and real challenges in transformation and organizational management. Whether the sources are real. Whether the judgment behind the argument has been tested and stays consistent. Whether someone stands behind it who will own the errors when they come.

In personal writing, authorship is the message. In analytical writing, accountability is.

A detection score measures the first and cannot see the second. By the scanner’s math, a scholar who spent thirty years building judgment and drafted with a machine scores exactly like a content farm that spent thirty seconds. Meanwhile, a fabricated statistic, typed slowly by hand, scores as perfectly human.

The number isn’t wrong, exactly. It’s answering a question that was never the important one.

The Fear Underneath

I watched what this moment is doing to people at close range this summer.

I’ve written twice now about my session at this year’s Bridge Conference and the conversations that followed it, in Transformation Theater and The Identity Renegotiation. There’s one detail from that room I haven’t put on the page until now.

When the subject turned to AI, and the subject always turns to AI now, I watched the faces change. Not skepticism. I know skepticism well and I can work with it. This was fear. The quickly composed kind, smoothed over within a second because showing it feels unprofessional. It’s the face of someone calculating, in real time, what a new reality means for their own worth.

I did my best with it in the room, and the room met me halfway. People began sharing what had already happened to them and how little idea they had of how to navigate it.

Here’s what makes the fear rational and the scanning ritual strange at the same time.

AI has become part of the basic equipment of office work. Employers aren’t just allowing it; many are actively pushing adoption. Gallup’s most recent numbers, published in July, put AI use at 52 percent of American workers, up six points in a single quarter, with about two-thirds among people whose jobs can be done from a desk anywhere. It has climbed every quarter they have measured it. And the study I mentioned earlier ran more than eleven hundred master’s theses through a detector. Nearly half got flagged. Among those, the middle case showed about 30 percent AI involvement. Help, in other words, not replacement.

Mixed work, some of it yours and some of it the machine’s, is now the ordinary case. A test that nearly everyone fails doesn’t separate anybody from anybody. It just reports that the water is wet.

So why does the appetite for these tools keep growing precisely as they stop being informative?

Try this with me. Imagine a perfect detector. No mistakes, and a complete record of where every sentence on earth came from. Would the unease go away?

My opinion is that it would sharpen. The honest number would be enormous, and every reader would discover that the colleagues they respect, the newsletters they trust, and their own last three drafts all carry the same signature.

A tool that feels exactly the same to you whether it is right or wrong is not measuring anything. It is a ritual.

Which means the worry was never really about where the words came from. Underneath “was this written by AI” sit two questions no detector can reach, and both of them are deeply human.

The first is about the reader. Am I making a decision, forming a belief, arriving at a conclusion, because a machine put something in front of me and I took it?

The second is about the writer, and it is the one I hear most. Am I still necessary? Is the thing I spent years learning to do still worth doing?

I’m not dismissing either one. The first I come back to at the end. The second is the one I want to sit with here, and I think it’s the most legitimate question of this decade. I write about it, I talk about it with clients, and I talk about it with anyone willing to listen. It hurts precisely because it isn’t irrational.

The displacement is real, and I know it from the inside. The editorial and production services I once paid people for, I now perform with a machine. The honest version of that is more tangled than the headline version. I made some personal and professional decisions. As a result, recurring costs had to come down. Several decisions sat behind the one, and the tool was a factor among them rather than the cause of them. Multiply that across every publisher and every marketing department, and the people who feel threatened are not imagining things.

And I should admit the part that had nothing to do with money, because it is the part that implicates me. I saw freedom in it. Freedom to choose subjects that I am passionate about and nobody would have talked me out of, to run long when something needed the room, to pile on sources. My editorial partner held a construct she believed served readers better, and she was right more often than she was wrong. When she was no longer holding it, I stopped being held.

Which brings me back to what I asked you to hold onto earlier. The formality, the extra research, the density you may have felt building in these issues. That is not a machine writing like me. That is me writing without the person who used to tell me when to stop.

And it is precisely what a detector reads as machine-like.

But look at what we’ve done with the question.

Faced with something too large to hold in our heads, we did what we always do. We swapped an unmeasurable question for a measurable stand-in, and then we started arguing about the stand-in. It offers a way to focus when you are facing ambiguity and uncertainty. Transformation dashboards do this. Engagement scores do this. Authenticity percentages do this.

The Human Factor Method treats resistance as signal, and the signal here is clear. An entire profession is being asked to move its identity from producing prose to exercising judgment, with no map and no timeline. It is uncomfortable, it raises more questions than it answers, and it produces the kind of anxiety that turns into identity threat.

The detector is a checkpoint. Almost a way of slowing how fast things are moving by creating a pause. The people putting it up are being human, in the oldest way there is, at a moment that produces fear.

Nobody Is Teaching Us to Read

If the marks can’t answer the question that matters, what we need alongside them is obvious, and it is also the thing that always gets built last. We need a shared understanding of what they do and don’t mean, held widely enough that a score informs judgment instead of replacing it. In every other domain we call that literacy.

And here is the least optimistic paragraph in this issue, because I’ve spent enough years inside standards and policy bodies to know how this work gets done, and what infuses the end result despite anyone’s best efforts and intent.

It won’t come from Congress. I’ve watched privacy bills die in committee for two decades while Section 230 sits untouched, despite every reason to change it to protect the public. The responsibility falls instead to platforms, vendors and industry groups, and my standards and policy years taught me a lesson I’ve never been able to unlearn. Industry builds what the people paying for it need, and often builds their advantage quietly into the result.

Detection tools serve platforms, so they get built. Provenance standards serve enterprise sales, so they get built. Compliance paperwork serves legal departments, so it gets built. Public understanding of what a mark means serves everyone and pays no one.

Detection has a sponsor. Provenance has a sponsor. Compliance has a sponsor. Literacy does not.

History offers one comfort with a warning tucked inside it. Tool stigmas do fade. But they fade when somebody takes on the teaching, when what good use looks like becomes clear, and when enough people accept it. The calculator stopped being a scandal because schools decided what calculator use meant and then taught it. No institution has picked up that job for AI authorship, and until one does, fear fills the space where understanding should be.

Expect the norm, when it finally settles, to settle the way ghostwriting did. By domain. Unremarkable where writing is just a tool. A stigma where authorship itself is the product, which is exactly the territory where every writer reading this lives.

What This Asks of Us

So what does a writer do, standing in the middle of something that used to work and now doesn’t, with nothing settled in its place?

I can only report my own answer. I turned the scanner off, and in its place I point to the record no detector can produce. An archive that predates the tools. Books that predate the tools. Sources you can check in every issue. And a name that answers for all of it.

For writers, the counsel is short. Let the machine carry the load it’s good at. Keep the judgment and the accountability unambiguously yours. Say so specifically, rather than hoping the question never comes up. And when an idea arrives inside a draft rather than out of your own head, turn the tool around and ask it directly what the prior art is and who has written about this. The burden of verification went up. So did the means.

For readers, the counsel is older still. Interrogate the sources, not the writing style. A bibliography you can check will tell you more about the human behind a text than any statistical reading of its rhythm ever will.

And for leaders, there’s a version of this work your organization needs now. An AI policy tells people what is forbidden. A literacy practice tells them what things mean. What a mark does and doesn’t prove. What honest use looks like. What will never be punished if it’s disclosed. If your people believe a flagged document is evidence against their own worth, that belief, and not the technology, is what your next set of results will measure.

The scanner answers whether a machine touched the text. The question that matters is whether a human stands behind it. Those are not the same question, and no percentage will ever make them the same.

The first has a number. The second has a name.

Mine is on every issue, including this one. And here is what the name actually promises, because it isn’t perfection. I get things wrong. I misread a study now and then, and I carry biases I can’t fully see. The promise is that when you tell me, I will look at it, admit it, correct it, and apologize.

That is a promise a person can make and a percentage cannot. A detector will never issue a correction. It has nothing at stake and no one to answer to.

I have both. And that difference, not the keystroke count, is the only thing that has ever made a byline worth anything.

Related Reading

Artificial Understanding, Parts One through Three (Issues 260 to 262). The comprehension gap. What these systems actually do, what it costs the people using them, and what feeds the machine. The fullest account of how I work with AI and where it fails.

The Empathy Outsource (Issue 270). Why authorship is the message in personal writing, and what happens to trust when the author becomes uncertain.

The Confidence Transfer (Issue 271). What happens to expertise when we trust the machine’s answer over the person’s, which is what a detection score asks readers to do.

Transformation Theater (Issue 275). The Bridge Conference session behind the room I describe in this issue.

The Identity Renegotiation (Issue 276). AI adoption as an identity renegotiation before it is a skills transaction. The fear in this issue is that renegotiation made visible.

Is Critical Thinking at Risk of Extinction? The question I raised as these tools were rising, and the one sitting underneath this entire issue.

Why Transformation Dashboards Lie. The proxy problem in its organizational form. Measuring the stand-in and forgetting it is one. Part of the Measuring What Matters series.

The Human Factor Podcast, Season 2, Episode 019: Structural Silence. On why people conceal rather than disclose, which is the dynamic now playing out with AI use across every workplace.

What I Read

Everything below is checkable. That is rather the point of this issue.

The OpenAI classifier. OpenAI launched an AI text detector in January 2023 and pulled it that July for low accuracy. On its own testing it caught 26 percent of AI text and wrongly flagged human writing 9 percent of the time. Both figures are OpenAI’s, published on its own site.

The second-language study. Liang and colleagues, “GPT detectors are biased against non-native English writers,” in the journal Patterns, July 2023. The 61 percent average and the seven-detector test come from here.

Vanderbilt. “Guidance on AI Detection and Why We’re Disabling Turnitin’s AI Detector,” Vanderbilt University, August 2023. The 75,000 papers and the arithmetic are theirs.

The Markup. Tara García Mathewson, “AI Detection Tools Falsely Accuse International Students of Cheating,” August 2023.

The humanizer study. Van Vlasselaer, Van Droogenbroeck and Spruyt, “Who wrote this? Evaluating the reliability of AI detection tools in higher education,” International Journal for Educational Integrity, June 2026. Every figure I quote on Turnitin, GPTZero, Pangram and the master’s theses comes from this paper.

The humanizer industry. Lars Daniel, “Students Use ‘AI Humanizer’ Apps To Make ChatGPT Essays Undetectable,” Forbes, October 2025.

Substack and Pangram. TechCrunch and Axios covered the launch on July 22 and 23, 2026. Chris Best’s post “Against Claudefishing” went up July 21. Substack’s support documentation describes how the feature works. The platform percentages come from Pangram’s own study of just over a million social posts, published July 9, 2026. Company research about the problem the company sells against, which is why I said so in the text.

Jerry Falade. The Wall Street Journal broke it, and Publishers Weekly followed, August 2026. Deal figures, the agency’s statement and Falade’s response as reported there. His account of the 2012 and 2017 test comes from The Bookseller.

Anthropic’s watermark. Announced August 11, 2026, and covered by TechCrunch. Anthropic’s own wording is that a watermark only helps test whether Claude might have produced or processed the content, and cannot separate Claude writing something from Claude heavily editing it.

The rules. The EU AI Act’s transparency requirements, in force August 2, 2026, with existing systems given until December 2 to comply. China’s labeling measures were issued in March 2025 and took effect September 1 that year.

How detection works. Gehrmann, Strobelt and Rush, “GLTR,” 2019, and Mitchell and colleagues, “DetectGPT,” 2023, for the statistical approach. GPTZero has said publicly that it stopped using that approach in autumn 2023.

Workplace numbers. Gallup, “Organizational AI Adoption Jumps Six Points,” July 2026, surveying 22,573 employed American adults. The 52 percent and the six-point quarterly jump come from there.

Nancy Drew. The University of Iowa Women’s Archives on Mildred Wirt Benson, and JSTOR Daily on the Stratemeyer Syndicate. Her authorship became public when she testified in the Grosset & Dunlap litigation in 1980.

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The Truth About Transformation

The Truth About Transformation: Leading in the Age of AI, Uncertainty and Human Complexity (2025) explores why transformation fails at a 70% rate and what it takes to lead change as the human problem it actually is.

Issue 277, August 13, 2026

Two weeks ago I presented Transformation Theater at the Bridge Conference in National Harbor. It was a small but full room, and that turned out to matter. Partway through the session I asked the leaders in the room to score six statements about their own organizations, the same six that now anchor the Transformation Theater Diagnostic. Most scores landed between six and twelve, which is where most organizations land, and the room understood what that meant before I said anything. What stayed with me came afterward, in the conversations a small room makes possible. Nobody argued with the diagnosis. Many shared examples with me and those in the room where they have noted struggles, where realignments have resulted in more stress and high ambiguity, where they have seen the signs and pondered why leadership wasn’t seeing the same things they were. They expressed relief as I put a name to what they had or are feeling, a name to what they have seen and they each repeatedly nodded their heads as I talked through the signs, through the behaviors and the elements of performance. They asked about approaches and strategies that could help. Every conversation, in the end, was a version of the same question: what do we do on Monday morning?

It was the right question then, and it still is, and it deserves more than a brief answer. Readers of this newsletter have now seen the Transformation Theater argument in full in Issue 275, and podcast listeners heard it develop in a solo episode earlier this season: the initiative fatigue that masquerades as progress, the activity and institutional metrics that replace outcome measurement, the change launched because a peer organization launched first. At 2040, we have spent years writing about why change fails, and we have learned to be suspicious of our own industry’s reflex, which is to answer every diagnosis with a new framework and a new launch. You may be seeing this in your own social media feeds where many posts are suggesting a new framework, a new approach, a new way in. Well, sometimes it isn’t a new framework that helps, but a new way of looking at a situation with the recognition of the human factor and how we humans operate.

So this issue doesn’t seek to introduce a new framework. It offers an exercise that is, at its core, that shifted human viewpoint put to work. It is the working companion to Issue 275, a ninety-day sequence you can actually run, with the exercises I use with clients spelled out along the way.

One promise before we begin: ninety days doesn’t transform an organization, and anyone who tells you otherwise is selling you more theater. What ninety days can do is re-establish the credibility without which no genuine change is possible. Think of it as the first honest quarter your organization has had in years. Honesty, it turns out, has a sequence too.

The Temptation to Fix Theater with More Theater

First, a warning, the same kind I raised in our discussion of unlearning last year. When leaders recognize a dysfunction, the familiar knee-jerk response is to launch something at it. A task force on initiative overload. A working group on meeting culture. A new dashboard to track the proliferation of dashboards. I have watched organizations do each of these, sincerely and with real energy, and every one of them is the disease presenting as the cure. You can’t launch your way out of a pattern that launching created. But alas, it always seems to be our very deeply inherent default reaction: that we are taking action, not likely the right action, but we feel we have moved the ball forward.

Why is the trap so consistent? Because theater is comfortable, and it’s comfortable because it’s visible and it appears action-oriented. A launch has a date, a name, a sponsor, and a communications plan. It gives everyone something to report. What I describe in this issue is the opposite: stopping things, listening carefully, and keeping one commitment slowly. It produces very little worth announcing for weeks at a time, and you should expect that silence to feel uncomfortable. The discomfort is itself information. An organization that can’t tolerate ninety days without an announcement has been measuring itself by its announcements rather than its actions, and that is precisely the habit we are here to try to break.

The evidence for patience has not changed via any recent research. John Kotter documented the patterned failure of transformation efforts in Harvard Business Review back in 1995. Thirty years of methodology later, McKinsey’s 70 percent failure rate has not moved. If frameworks alone could fix this, they would have fixed it by now. What remains unaddressed is the psychological infrastructure of the people being asked to change, and the first ninety days after the theater stops have exactly one purpose: rebuilding that infrastructure, in many ways from the ground up.

Start With the Instrument You Already Have

If you were in the room at Bridge, you left with a number between six and thirty. If you weren’t, the Transformation Theater Diagnostic is waiting for you, one page containing the six statements we scored together and the twelve early warning signs of transformation fatigue. Complete it before you read further if you have the time. The ninety days that follow work differently when they begin from a measurement rather than a mood.

Your score isn’t a grade, and I want to be clear about that. Knowing where you stand is the starting point, not the failure. What the number gives you is three things the coming quarter requires. A baseline, because you will score the same six statements again at day ninety, and the comparison will tell you whether the quarter produced change or performed it. Specificity, because the statements you scored highest tell you which of the three theater patterns has the deepest hold on your organization, and you should lean the sequence hardest where your score leans. And a shared language, because a leadership team that has scored the diagnostic together can name the theater without anyone having to accuse anyone. Ask yourself how long your leadership team has needed a way to have that conversation, removing the barriers of accomplishment, removing the barriers of competitiveness, and removing the barriers to openness to criticism.

For the fuller picture beneath the score, the Transformation Readiness Assessment takes a few minutes and examines whether your organization is positioned to change or positioned to perform. Either instrument will serve you well. They are both purposely simple, but so very revealing. The discipline is what matters: measure what really matters before you move, and measure what really matters again when the quarter ends.

Days One Through Thirty: Stop, Visibly

The first month has one job, and it is subtraction. Not strategy, not vision, not the future state. Subtraction. It is hard and will be challenging for those who must decide.

Recall the arithmetic from Issue 275. Most organizations are carrying somewhere between eight and fifteen active change initiatives, and the same people across the organization are carrying the load. The sector research reveals the condition of the workforce underneath all that leadership ambition: 95 percent of nonprofit leaders express concern about staff burnout to the Center for Effective Philanthropy, 45 percent of nonprofit employees have told Nonprofit HR they expect to seek other employment within five years, and Gallup’s persistent finding is that employees living through disruptive change report frequent burnout at nearly twice the rate of everyone else, 42 percent against 24. An organization in that condition doesn’t need its next initiative on top of what it’s already carrying. It needs an honest accounting of the ones it already has, their value and whether they will generate true outcomes.

Here is the exercise as I run it with clients. I call it the Portfolio Audit, and it requires nothing more sophisticated than a conference room or a virtual meeting room, and the openness to be candid.

First, list every active change initiative. Expect the list itself to surprise everyone in the room. Leadership teams routinely discover initiatives running in departments and teams that half the room was unaware of, and many also learn about active initiatives they believed had already ended. That learning tells you something about how initiatives end, and how they are communicated, in your organization: not by decision, not by awareness, but by neglect, protectionism, and isolation.

Second, sort each initiative with one question, the question I asked the nonprofit executive whose story opened Issue 275. Does this require your people to think differently about themselves and their work, or does it require them to use a different tool? Longtime readers will recognize Ron Heifetz’s line between adaptive and technical challenges, a distinction we return to constantly because organizations keep blurring it. Sort openly and honestly. There should be no sacred cows that get protected.

Third, count what the sorting reveals. In my experience, the portfolio nearly always contains more adaptive challenges than any organization can handle at once, most of them are routinely disguised as technical projects and resourced accordingly. They have a technical project plan, a task list, and nothing more.

Fourth, the step that separates recovery from theater: eliminate initiatives, and do it publicly. Don’t pause them. Don’t put them lower on the priority list. Don’t keep them on the game board as a phase two. Just end them, name them, and explain why they are no longer of value.

Why publicly? Because of what Issue 275 said about your people’s cynicism. It’s not attitude. It is evidence-based reasoning from a sample of abandoned initiatives their leadership would prefer to forget. You can’t argue anyone out of their own evidence. You can only add new evidence, and the first piece can’t be another promise, because promises are a currency that has been spent far too freely, and every unkept one fed the performance. The communication has to be an admission with consequences attached. We started too many things. These are ending. Here is what we are protecting instead.

Download the companion. The full ninety-day sequence, the Portfolio Audit, the Intelligence Session questions, and the commitment criteria are on one page, in the same format as the Transformation Theater Diagnostic, with a single personal follow-up from me rather than a sales sequence. Download The First Ninety Days companion

The remainder of the first month is a moratorium. No new initiatives, no new committees, no new measurement systems, for thirty days. At 2040, one of our longstanding mantras is that speed kills with uninformed urgency, and the moratorium is that mantra applied as policy. This is not inaction. Regular readers will recognize why from the Perpetual Thaw discussion in Issue 275: an organization in constant destabilization, overloaded with change, has lost the experience of anything ending, and with it the ability to tell motion from progress. The moratorium gives your people the first stable ground many of them have stood on in years; it allows them to take a breath and process, and that stability is what makes the listening that comes next believable rather than suspicious.

Measure the first month by what stopped. Nothing else.

Days Thirty-One Through Sixty: Listen Like It Is Intelligence

The second month is diagnosis, and the diagnostic instrument is structured listening. Not a survey with a five-point scale. Not a town hall where leadership speaks for fifty minutes and takes questions for ten. Small groups, real questions, and leaders who have decided in advance that nothing they hear will be argued with in the moment.

Issue 275 made the case that resistance is organizational intelligence, that it tells you where psychological safety is low, where trust is insufficient, and where identity threats far too often sit deeply unaddressed. The second month is when you collect that intelligence deliberately. The five psychological factors from that issue convert directly into the questions to ask, and I offer them here as the working agenda for what I call Intelligence Sessions. And remember, be humble, be open, and listen. Create a psychologically safe environment to ensure you hear what you need to hear.

Where did we assume you were ready when you were not?

Where did we explain the strategy but never connect it to your actual day and your actual role?

Where did leadership demonstrate command of the new system but no understanding of what the change felt like from inside your job?

Where did we treat a team as a collection of individuals rather than a social system?

Where do you want to do the right thing but feel unsafe or unsupported doing it?

Two disciplines determine whether these sessions produce intelligence or produce Listening Theater, because that variant will result if your motives are different from what they should be. Don’t do this as a token exercise to simply go through the motions hoping people feel better, become better supporters, feel less burnt out. The first comes from Chris Argyris and Donald Schön, whose distinction between espoused theory and theory-in-use has run underneath this newsletter’s and podcast’s arguments for the past couple of years. Don’t ask people what they think of the strategy. Ask them what they actually do on a Tuesday when the new process collides with getting their work done, and treat the gap between the official answer and the Tuesday answer as the revealing finding. The workarounds are the map. Every spreadsheet that survived the CRM migration marks a place where the official system fails a real person, and each one is a data point no dashboard will surface.

The second discipline concerns what leaders do in the room, and it returns us to Amy Edmondson’s territory, familiar ground for readers of this newsletter and listeners of the podcast. The first time someone says something genuinely uncomfortable, everyone present will watch what happens next. That moment will set the ceiling on honesty more decisively than any value statement your organization has published. A leader who explains, defends, or corrects in that moment has taught the room what candor costs. So decide, before the first session, what you will do with the first piece of unwelcome truth. If you can’t answer that question, you aren’t ready to hold the session, and it is better to know that now.

Month two also includes an audit most organizations have never conducted: an inventory of the unwritten promises that recent years of change have broken over and over again, to the point that trust simply doesn’t exist in the organization. This is the psychological contract we examined at length in Issue 275 and in Podcast Episode 20. Somewhere in your organization is a person whose decade of expertise was made worthless overnight by a system migration, and no one ever acknowledged it. Put yourself in her seat for a moment. What would it take for you to extend trust again? Research by Denise Rousseau and her colleagues on what happens after a violation is clear on this point: a breached contract is not restored by the next promise. It is restored by acknowledgment, and then by evidence of responsiveness.

Measure the second month by what surfaced. Count the things leadership learned that it did not know and could not have learned from its reporting systems. If the count is low, the listening was theater, and your organization knew it before you did.

Days Sixty-One Through Ninety: Prove One Thing Slowly

The third month is where recovering organizations most often relapse. The listening has produced a list, and organizational instinct converts lists into initiative portfolios. Please resist the instinct. The discipline of the third month is to choose one commitment. Not the easiest one, and not the most strategic one. Choose the commitment that most directly tests whether leadership means it this time, because that is the question your people are actually asking and really it’s the only way to start to restore the trust reserves. Call it common sense, call it recognizing people’s cognitive and energy limitations, call it being rational and realistic.

Three criteria govern the selection. Adaptive, not technical, because a technical win proves only that the organization can still install things, which nobody doubted. Visible in the daily experience of ordinary staff, rather than inside a steering committee’s charter. Falsifiable, meaning anyone in the organization can tell whether it happened. If the Intelligence Sessions revealed that people hide problems because messengers fare poorly, get criticized, get publicly ostracized, the commitment might be what happens to the next three messengers, publicly and unmistakably. If the sessions revealed decision paralysis, the commitment might be the one Decision Theater in Issue 254 pointed toward: a single consequential decision with a named owner who stands in front of the organization and says, I made that call and here is why, and then lives with the consequences.

Whatever the commitment, the third month keeps it at a cadence people can watch, and this is where measurement finally enters the sequence. Notice how late it arrives, and recall why from Why Transformation Dashboards Lie: dashboards built early become instruments of the performance. The measure comes after the commitment, is behavioral rather than attitudinal, and is reported honestly even when it is unflattering, especially then, because one honestly reported shortfall builds more trust than a quarter’s worth of green dashboard indicators. What we measure is what changed in what people do. Decisions traceable to owners. Problems surfacing earlier. The meeting after the meeting growing quieter, because the meeting itself became a place where truth is put on the table without consequence.

Watch for one marker in particular, because it tells you whether the quarter is working. Somewhere in the third month, someone will test the new reality with something real: a genuine objection, a report of something broken, an admission of being lost. The test will be small and easy to miss. What happens in the following forty-eight hours, whether that person is thanked, criticized, or managed, determines whether the first honest quarter becomes the first honest year, or becomes the story people tell about the time leadership seemed different for a while. Regression is seen far too often in organizations. It becomes part of the culture, part of the story, part of the perception of leadership, and it’s so very hard to recover from.

Next, at day ninety, return to the diagnostic you scored at the beginning. Score the six statements again, with the same people. The comparison is the quarter’s real report, and you should share it either way. Sharing an unimproved score honestly is itself the kind of evidence this whole sequence exists to produce.

Measure the third month by what one commitment proved.

What Ninety Days Cannot Do

Now I want to be honest, because everything above could be mistaken for a program, and programs are how this disease spreads deeper and deeper in most organizations.

Ninety days does not transform an organization. It does not resolve the identity threats that AI and restructuring have placed on your most experienced people, work that is measured in months of doing rather than weeks of messaging. It does not refill a trust account that took years to drain. It does not make an adaptive challenge technical, however well the quarter goes. William Bridges, whose thinking on endings and the neutral zone has shaped my own thoughts and this newsletter for years, would say ninety days barely carries an organization through its endings, and he would be so very right.

What the first honest quarter can do is narrower and more important. It changes what your people believe about whether leadership is serious, and that belief is the exchange rate on everything that follows. The same announcement, the same investment, the same strategy lands entirely differently in an organization that has watched its leaders stop things, hear things, and keep one commitment slowly. The quarter doesn’t deliver the transformation. It delivers the conditions under which transformation stops being a performance, and three decades of unmoved failure rates suggest those conditions were the missing variable all along. Why do most find it so hard to see?

The Question Underneath

The leaders in that room at Bridge scored their organizations honestly and then asked what they should do next. The sequence above is my answer. Underneath it sits a harder question, and I will leave you with it, because it is the one that determines whether any of this actually happens.

Your organization will spend the next ninety days doing something. It will either spend them producing more theater, with launches and dashboards and announcements, or it will spend them producing evidence. Your people can tell the difference within the first week, because they have been reading that difference so very accurately for years. They talk about it in the hallways, at the water cooler, and in the quiet spaces before a meeting begins or ends. So the question is not whether you accept the diagnosis. Most leaders do, quickly and privately. The question is what you are prepared to stop, acknowledge, and prove before you ask your people to believe anything again.

What would your answer be, and would your people recognize it as yours?

Related Reading

Transformation Theater: The Elaborate Rituals of Change That Produce Everything Except Change (Issue 275)

The diagnosis this issue answers: why 70 percent of transformations fail, the five psychological factors that predict failure, and the patterns of performative change.

The Transformation Theater Diagnostic

The one-page instrument this sequence begins and ends with: six statements to score and twelve early warning signs of transformation fatigue.

Decision Theater: When People and Organizations Mistake Motion for Commitment (Issue 254)

The pattern the third month’s commitment most often has to answer: rituals of deciding without the exercise of authority.

Why Transformation Dashboards Lie

Why measurement arrives last in this sequence: dashboards built early become instruments of the performance.

Unlearning: The Hidden Key to Organizational Transformation (Issue 206)

The earlier warning about answering every dysfunction with a launch, and what stopping actually requires.

Transformation Readiness Assessment

The fuller instrument beneath the diagnostic score: whether your organization is positioned to change or positioned to perform.

The Trust Gap: A Midsummer Sampler on the Asset Every Organization Is Spending and Almost None Are Measuring (Issue 273)

The trust account this sequence exists to refill: why organizations spend trust freely, measure it almost never, and pay the exchange rate on every change that follows.

The Identity Renegotiation: What AI Asks People to Give Up Before It Gives Anything Back (Issue 276)

The identity threats this quarter cannot resolve but must stop ignoring: what your most experienced people are being asked to surrender, and what leaders owe them in return.

The Meeting After the Meeting: Why Your Strategies and Plans Keep Losing Support

Where the real conversation happens when truth is not sayable in the room, and the corridor dynamic the third month’s commitment should quiet.

Building Psychological Safety During Transformation

The working conditions the Intelligence Sessions require: how leaders create rooms where the first piece of unwelcome truth is thanked rather than managed.

Human Factor Podcast

Season 2, Episode 027: Transformation Theater, Why Organizations Perform Change Instead of Making It

Season 2, Episode 020: The Broken Contract, The Unwritten Promises Your Transformation Just Violated

Research Sources

1. McKinsey & Company. (2021). Losing from Day One: Why Even Successful Transformations Fall Short. McKinsey Global Survey.

2. BCG. (2020). Flipping the Odds of Digital Transformation Success. Boston Consulting Group.

3. Kotter, J. P. (1995). Leading Change: Why Transformation Efforts Fail. Harvard Business Review, May–June 1995.

4. Gallup. (2024). Disruptive Change Is Hitting Leaders and Managers Hardest.

5. Center for Effective Philanthropy. (2024). State of Nonprofits 2024: What Funders Need to Know.

6. Nonprofit HR. (2020). Nonprofit Talent Retention Practices Survey.

7. Heifetz, R. A. (1994). Leadership Without Easy Answers. The Belknap Press of Harvard University Press.

8. Argyris, C., & Schön, D. A. (1974). Theory in Practice: Increasing Professional Effectiveness. Jossey-Bass.

9. Edmondson, A. C. (1999). Psychological Safety and Learning Behavior in Work Teams. Administrative Science Quarterly, 44(2), 350–383.

10. Tomprou, M., Rousseau, D. M., & Hansen, S. D. (2015). The Psychological Contracts of Violation Victims: A Post-Violation Model. Journal of Organizational Behavior, 36(4), 561–581.

11. Rousseau, D. M. (1995). Psychological Contracts in Organizations: Understanding Written and Unwritten Agreements. Sage Publications.

12. Bridges, W. (1991). Managing Transitions: Making the Most of Change. Addison-Wesley.

Join the Conversation

Which initiative would your organization kill first, and what has stopped you from ending it already? I would love to hear your perspective. Share your thoughts with me on LinkedIn or subscribe to Ideas and Innovations on Substack for weekly insights on leadership, transformation, and the human side of organizational change.

The Truth About Transformation

If this issue resonated with you, my book The Truth About Transformation explores why most transformation efforts fail and what leaders can do differently. Available on Amazon.

Issue 276, August 6, 2026

Regular readers of Ideas and Innovations may have noticed that this newsletter and The Human Factor Podcast have been circling around something for months. In The Algorithmic Mirror, and its sister podcast episode, we examined what AI reveals about how we actually think and decide and found that the discomfort was never really about the technology. It was about the reflection. In The Readiness Illusion, we tested the AI agent era’s claims against the evidence and found a large gap between what organizations communicate and what their people are prepared to absorb. In The Empathy Outsource, we asked what happens when the care in a message was real, but the author wasn’t, and discovered that the question destabilized something deeper than the practice of effective communication. In The Confidence Transfer, we followed organizational expertise as it migrated from people to systems and watched what happened to the people when their judgment wasn’t the first thing consulted. And in Transformation Theater, we named the elaborate rituals of change that produce everything except change, and identified professional identity as the terrain where the performance most reliably collapses into failure.

Each of those issues examined one side of the same object. Every one of those patterns- the mirror we avoid, the readiness we overstate, the empathy we delegate, the confidence we transfer, the theater we perform- converges on a single unacknowledged transaction that sits underneath AI adoption in every organization I work with or talk to. I am going to call it the Identity Renegotiation: the process of renegotiating what a person’s expertise, judgment, and standing mean when intelligent systems begin doing work that used to define them.

Every AI initiative conducts this negotiation whether its leaders and those across the workforce know it or not. The only real choice leadership has is whether to conduct it openly, with both parties at the table, or silently, with one party dictating terms and the other party expressing its position through what we too casually label resistance.

This issue takes the thread further than any of the previous pieces could individually, because the argument only becomes visible when the pieces are assembled. So let’s assemble them.

The Transaction Nobody Names

We talk about AI adoption as if it were a skills transaction. The organization provides tools and training, the employee provides effort and attention, and competence is the exchange. The framing is clean, procedural, and measurable, which is precisely why adoption programs look the way they do: license counts, training completion rates, usage dashboards, prompt libraries. Readers of Transformation Theater will recognize these instantly as activity metrics. They measure the performance of adoption rather than the fact of it, and they allow everyone involved to report progress while the actual transaction goes unexamined.

The actual transaction most are unaware of is an identity transaction, and its terms are considerably less comfortable. We are asking people to surrender three things they spent their careers acquiring.

The first is mastery. The expertise that took a decade or two to build, the pattern recognition, the synthesis, the ability to look at a situation and know, is precisely what current AI systems replicate most visibly. This means the people with the most invested face the largest write-down, an inversion of every assumption our organizations make about seniority, expertise, and security. We have spent generations telling people that deep expertise is the safest asset they can hold. The systems now showing up in their workflows revalue that asset in an afternoon.

The second is the currency of judgment. In most professional settings, compensation operates on two ledgers. The first is the salary. The second is the standing that comes from being the person whose judgment gets consulted, and it is the ledger people actually organize their working lives around: evidence that they contribute, that they are productive, that their work has meaning. When an algorithm’s recommendation arrives before anyone asks the veteran what she thinks, that currency devalues quietly and without announcement. The Confidence Transfer traced the organizational half of this problem, the atrophy of institutional expertise when the machine is trusted first. The personal half is sharper. The person still holds the judgment. The organization has simply stopped asking for it, and everyone in the room can see that it has.

The third is standing itself. Expertise confers a place in the room, a reason to be included, a basis for belonging. If the expertise is now ambient and instant, available to anyone with a login and the right prompt, the place in the room becomes negotiable. This is the existential question sitting underneath every adoption plan: if the system can do what took a decade to master, what is that person’s value now? No amount of training answers that question, because it is not a question about capability. It is a question about worth.

Those in attendance at my Bridge 2026 session last week expressed this new, multifaceted and deepening uncertainty in their responses to the questions I asked them to discuss with their neighbors and their need to find answers continued individually with me once the session was over. Middle managers struggling to find the new path, marketers struggling with the change in role and scope, others contending with organizational downsizing and having to take on more responsibilities as overseers of AI outputs. And all of them unsure and unsettled about where this is all heading.

Here is the challenge that makes this new negotiation so fraught. The surrender is demanded up front, and it is concrete. The compensation, the promised elevation to higher-order work, the partnership with the machine, the redeployment of judgment to more consequential terrain, is delivered later, if at all, and it is and will remain far too abstract for the foreseeable future. AI adoption isn’t a technology rollout with a training and acceptance problem attached. It is a negotiation in which one party is asked to pay first, to trust, to accept ambiguity, and to bring a significant amount of patience. That patience can really be described as surrendering to the unknown and unknowable.

The Write-Down of Mastery

Consider what the surrender actually feels like from inside, because today’s adoption plans are written without any consideration for what individuals are going through.

Moving from expert to novice in public is one of the most psychologically expensive transitions a professional can make. Adults will actively avoid situations that expose incompetence, even when they intellectually accept that the new approach is better. This is why your most experienced people are so often your most resistant, a pattern I have now watched across dozens of client organizations and which I described through the three senior pharmaceutical scientists in Season 2 of the Human Factor Podcast. Their resistance to an AI-powered clinical trials initiative had nothing to do with doubting the models. The models were good, and they knew it. Their resistance was the entirely rational response of people who understood, faster than their leadership did, that the initiative was conducting a negotiation over who they were, and that nobody had invited them to it.

The write-down operates on three dimensions, and each compounds the others. Professional identity fusion means that years of expertise have become inseparable from self-concept. I am the person who knows how to do this. When a system makes the knowing obsolete, it doesn’t merely change a process. It invalidates accumulated mastery, and it says so in front of colleagues. Competence threat means the daily experience of being visibly worse at the new way than one was at the old way, with no guarantee the gap will close. And status and belonging risk means that the social architecture of the team, who defers to whom, who is consulted, who teaches, is suddenly under revision, with no stated rules for the revision. We are in a period of atrophy. As people adopt AI, their critical thinking, their expertise, and their other skills, even the interpersonal, are atrophying and will continue to atrophy.

Gallup finds that employees experiencing disruptive organizational change report frequent burnout at nearly twice the rate of everyone else, 42 percent against 24 percent, and that 64 percent of them are watching for or actively seeking another job. We read those figures as change fatigue, and some of it is. But a meaningful share is something more specific: the exhaustion of conducting an identity renegotiation alone, in private, against a leadership that won’t admit the negotiation actually exists. Grief without a venue is expensive, and the expense shows up on exactly the lines our dashboards track last, if at all.

Why Training Cannot Fix This

The organizational psychology literature has been circling this problem for decades, well before AI gave it urgency and a real-time case study. The research explains why the standard adoption playbook in use keeps failing and will continue to fail.

Jennifer Petriglieri, writing in the Academy of Management Review in 2011, described what people do when an identity they value comes under threat. They don’t simply comply or refuse. They engage in identity work: protecting the threatened identity, restructuring it, or in some cases shedding it. The critical insight for leaders is that this work happens whether or not the organization creates space for it. Deny it a legitimate channel and it goes underground, surfacing as workarounds, quiet noncompliance, selective enthusiasm in meetings, and hallway skepticism. All feeding into an organization’s culture that is being changed in very unintended ways. Leadership often misdiagnoses these symptoms as a change management problem and responds with more communication and more training, which addresses none of it, because none of it was ever about information.

Herminia Ibarra’s research on professional transitions, developed in Working Identity, supplies the second piece. Identity does not change by decision or in response to an announcement. People act their way into new professional identities through experiments, provisional selves, and new relationships, and the process is iterative, social, and slow. The change of identity doesn’t respect quarterly adoption targets, because it can’t. There is no communications plan that installs a new sense of self.

William Bridges made the companion point a generation earlier, and readers of this newsletter will remember the frame from our discussions of the neutral zone. Every transition begins with an ending. The psychological work of the ending, the letting go of who we were, is the phase organizations most reliably skip, because endings are uncomfortable to sponsor and impossible to celebrate at a kickoff. We schedule the launch. We don’t schedule the loss. And then we are surprised when people seem stuck, when the actual situation is that we asked them to begin something new without ever permitting them to finish something old.

Assemble those three bodies of work and the conclusion is difficult to avoid. Training addresses capability. The renegotiation is about identity. Applying training to an identity problem is a category error, a technical solution applied to an adaptive challenge, and it produces the same result: the appearance of adoption while monitored, and reversion the moment attention moves elsewhere. The spreadsheets come back. The old workflow returns. Not because people are stubborn, but because the negotiation was never concluded, and an unconcluded negotiation defaults to the status quo.

The Contract Beneath the Contract

Every organization holds two contracts with its people: the written one covering compensation and role, and the unwritten psychological contract covering expectations, norms, and implied promises. Denise Rousseau’s research established that the unwritten contract is the more powerful predictor of behavior, and that its violation is experienced not as change but as breach. Breach is the right word, because the felt experience is betrayal.

The Identity Renegotiation is, at bottom, a forced amendment of the psychological contract, executed without an opportunity to object. The written contract said you will use the new system. The unwritten contract said your expertise is valued here, your judgment matters, your standing is secure. AI adoption amends the second contract while pretending only to amend the first. The major gifts officer whose relationship instincts are displaced by donor scoring, the analyst whose synthesis arrives after the model’s summary, the editor whose judgment is now a review step on machine-generated copy: each of them is holding an amended contract they never signed, and each of them knows it, whether or not they can articulate it.

This is why so much of what we call resistance carries a moral tone that surprises leaders. People are not merely inconvenienced. They feel wronged. They feel betrayed. And here is the connection to the trust work from our midsummer sampler: every unilateral amendment of the psychological contract is a withdrawal from the trust account, and organizations spend that account freely while measuring it almost never. When the next initiative arrives, the account is empty, and leaders wonder why the announcement lands on folded arms.

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The Three Clauses of the Renegotiation

If we take the negotiation frame seriously, then like any renegotiated contract it has clauses, and the organizations that are or will navigate this well are the ones that make each clause explicit rather than leaving it to be inferred from a slide deck.

The first clause concerns what is genuinely being surrendered. Leaders are understandably tempted to soften this in an attempt to gain acceptance. Nobody is being replaced, we say, the AI is just a tool, your expertise still matters. People know better, whether they are consciously recognizing this in the present or they are unconsciously accepting the present while worrying about the future and their role change, and the gap between what they are told and what they observe is measured in trust. The alternative is uncomfortable candor: this system will do a substantial part of what you currently do and doing that part of your work isn’t any longer how your value will be measured. That sentence costs a leader something to say. It also treats the listener as an adult party to a negotiation rather than an audience for a communications plan, and in my experience the candor is received better than the euphemism, because it matches what people have already concluded on their own. The leader is confirming, being honest, and vulnerable, which is what the individual is hoping for and what any rational person would expect.

The second clause concerns what is explicitly retained. Not retained by default, retained by declaration with all the necessary detail, at least as much as is known and can be communicated. Accountability is the clearest example. The renegotiation requires leaders to say, specifically and in writing where it counts: the model recommends, you decide, and when your judgment and the model disagree, here is who owns the call and here is how we will learn from the disagreement. Organizations that can’t complete that sentence have not adopted AI. They have abdicated to it as a result of market pressure, urgency for productivity gains, or the ambition to be seen as the innovator. The Confidence Transfershowed what follows: verification capacity atrophies, institutional expertise thins, and the organization discovers, usually during an incident, that it has automated its judgment without ever deciding to. Retention clauses are how that outcome is prevented, and they only work when they are explicit enough that a person can quote them clearly and in plain language.

The third clause concerns what is gained, and when, and how the gain becomes real rather than rhetorical. Ibarra’s research is practical here. New professional identity forms through doing, not through messaging, which means the compensation side of the renegotiation must take the form of actual work: the harder cases the machine can’t handle, the oversight and validation roles that require exactly the pattern recognition the veteran carries, the teaching of judgment to junior staff and to the systems themselves, the edge cases where twenty years of context is the difference between a defensible decision and a plausible-sounding error. If the elevated work doesn’t materialize on a visible timeline, people conclude the negotiation was conducted in bad faith and their deep, overthought suspicions have been validated.

What the Organization Loses When It Skips the Negotiation

Skipping the renegotiation carries organizational costs that compound quietly, and they connect directly to patterns this newsletter and the podcast have documented all year.

The first cost is the one The Confidence Transfer named: when the people who could verify the machine’s output no longer feel their verification is wanted, they stop offering it, and the organization loses its error-catching capacity precisely when it is deploying systems that fail in confident and plausible ways. The second cost is organizational memory. The veterans most affected by the write-down are the same people carrying the undocumented context, the institutional why behind the how, and Gallup’s 64 percent figure tells us where they are heading if the negotiation is botched. When they leave, the knowledge leaves, and as we explored in Organizational Memory Loss, almost no organization has any mechanism for noticing the departure until it needs what has already departed. The third cost is the trust account already discussed, which determines the exchange rate on every future change the organization attempts.

Add these together and the conclusion inverts the usual framing. The Identity Renegotiation is not a soft-side accommodation that slows down the real work of AI adoption. It’s the real work of AI adoption. The technology installation, the model refinements, the teaching of prompts are the easy parts, the technical challenges in Heifetz’s terms. The renegotiation is the adaptive challenge that determines whether the installation produces capability or theater.

What This Asks of Leaders

None of this yields a checklist, and I will not pretend it does. It yields a posture, and the posture begins with the diagnostic honesty most adoption programs are designed to avoid. The renegotiation lives in people’s values, habits, and self-concept. The Human Factor. You can’t project-manage your way through it. You can only lead people through it, which means creating the conditions Amy Edmondson’s work on psychological safety describes, where a twenty-year veteran can say I don’t know what I am now without that admission becoming evidence against them. It means sponsoring endings as deliberately as launches, giving the loss a venue instead of a euphemism. And it means accepting that the timeline for identity work is measured in months of doing, not weeks of messaging, and budgeting for that reality instead of being perpetually surprised by it.

So, the diagnostic questions, uncomfortable by design. Has anyone in your organization named, out loud, what your most experienced people are being asked to give up? Can each of them state which of their judgments still govern and which now belong to the system? Does the elevated work they were promised exist yet, and can you point to it, with names and dates rather than intentions? When your best people go quiet in the adoption meetings, are you reading compliance, or are you reading a negotiation you did not know you were losing? And if the renegotiation in your organization is currently being conducted in silence, who do you suppose is winning it?

The question for the rest of us is not whether our people will renegotiate who they are when the machines arrive. They already are, in every hallway conversation we are not part of. Lying in bed at night pondering their futures. Sitting in meetings drifting off in thought about what their role becomes.

The question we should all ask is whether we have the courage to join a negotiation that is happening with or without us, and to pay what candor costs before we collect what the technology promises.

Join the Conversation

How is the Identity Renegotiation being conducted in your organization, openly or in silence? I would love to hear your perspective. Share your thoughts with me on LinkedIn or subscribe to Ideas and Innovations on Substack for weekly insights on leadership, transformation, and the human side of organizational change.

The Truth About Transformation

If this issue resonated with you, my book The Truth About Transformation explores why most transformation efforts fail and what leaders can do differently. Available on Amazon.

Research Sources

1. Petriglieri, J. L. (2011). Under Threat: Responses to and the Consequences of Threats to Individuals’ Identities. Academy of Management Review, 36(4).

2. Ibarra, H. (2003). Working Identity: Unconventional Strategies for Reinventing Your Career. Harvard Business School Press.

3. Bridges, W. (1991). Managing Transitions: Making the Most of Change. Da Capo Press.

4. Rousseau, D. M. (1995). Psychological Contracts in Organizations: Understanding Written and Unwritten Agreements. Sage Publications.

5. Robinson, S. L., & Rousseau, D. M. (1994). Violating the Psychological Contract: Not the Exception but the Norm. Journal of Organizational Behavior, 15(3).

6. Heifetz, R. (1994). Leadership Without Easy Answers. Harvard University Press.

7. Edmondson, A. (1999). Psychological Safety and Learning Behavior in Work Teams. Administrative Science Quarterly, 44(2).

8. Gallup. (2024). Disruptive Change Is Hitting Leaders and Managers Hardest.

9. Novak, K. (2025). The Truth About Transformation (Revised and Expanded). 2040 Digital.

Issue 275, July 30, 2026

A nonprofit executive called me recently, frustrated and exhausted. Her organization had launched eleven change initiatives in the past two years. A new CRM. A strategic plan refresh. A culture transformation. A digital fundraising overhaul. A board restructuring. And enough others that she had stopped counting them. Then she said something that has stayed with me. “We are doing everything right. We hired the consultants. We bought the software. We trained the staff. We built the dashboards. We have steering committees and weekly check-ins. And nothing has changed.”

Eleven initiatives. Nothing changed. I asked her one question. Of those eleven, how many required your people to think differently about themselves or their work, versus just using a different tool? She went quiet for a long time. Then she said, “All of them. We treated all of them like they were about tools.”

That conversation is the subject of this issue. Her organization is not unusual, and her honesty is rarer than the pattern she described. Most organizations, nonprofit and commercial alike, have launched somewhere between eight and fifteen change initiatives in the past three years. Staff are exhausted. Constituents are confused. And the metrics have not budged. Meanwhile the board keeps asking for more innovation, because the board attended a conference where someone talked about innovation.

I have a name for what is happening in these organizations, and it is the title of the session I am presenting tomorrow morning at the Bridge Conference 2026 in National Harbor. I call it Transformation Theater: the organizational habit of performing elaborate change rituals without achieving actual change. The form is present. The substance is not. Theater implies performance. It implies an audience watching something that looks real but isn’t. And that is exactly what is happening across the majority of change initiatives right now, at organizations that are spending real money, consuming real institutional energy, and burning out real people to produce it.

The Number That Refuses to Move

McKinsey’s Global Survey reports that 70 percent of transformation initiatives fail to achieve their stated objectives. BCG’s survey of more than 800 senior executives confirms the same. Globally, organizations are spending an estimated 2.5 trillion dollars a year on digital transformation efforts, according to IDC. Two and a half trillion dollars, against a 70 percent failure rate. In the nonprofit sector, my own assessment is harsher still: when you measure sustained impact beyond two years rather than launch-day adoption, the effective failure rate approaches 85 percent.

But here is what should alarm us more than the percentages on their own. The percentages have not improved. Despite decades of accumulated transformation experience, despite billions spent on change management consulting, despite every methodology from Kotter to ADKAR to Agile, the failure rate has remained stubbornly, persistently at 70 percent. John Kotter himself documented the pattern in Harvard Business Review in 1995. Thirty years of frameworks later, we are where he found us.

Consider what we would do with that failure rate anywhere else. If 70 percent of bridges collapsed, we would stop building bridges the same way. If 70 percent of surgical procedures failed, we would shut down the operating room and rethink the entire approach. In organizational change, we do the opposite. We launch more initiatives. We hire more consultants. We buy more elaborate software. We build more detailed project plans. And we get the same result, over and over and over again.

A failure rate that survives every methodology designed to fix it is telling us something. It’s telling us we are solving for the wrong problem. The problem isn’t your strategy. It’s not your consultants. It’s not your technology. The problem is that we keep treating the symptoms while we ignore the psychological infrastructure of us humans that genuine change requires. The 70 percent failure rate is not a strategy problem. It is a psychology problem, and we keep applying strategy solutions to it.

What Transformation Theater Looks Like

Transformation Theater has three hallmark patterns, and once you can name them and recognize them, you will see them everywhere, possibly including your own calendar.

The first is initiative fatigue that masquerades as progress. When an organization is running fifteen change programs simultaneously and treats that count as a badge of honor rather than a warning sign, it’s performing transformation, not actually doing it. Volume of change for many has become a proxy for seriousness about change, when the research suggests the relationship runs in the completely opposite direction.

The second is activity metrics that replace outcome measurement. Training completion rates. Dashboard views. Meeting attendance. Task force convenings. These aren’t outcomes. They are inputs and outputs. And one of the greatest harms we can do is to measure inputs and outputs as opposed to outcomes. When your reports focus on how many people attended the workshop instead of what changed as a result of the workshop, you are measuring the performance, not the impact. Regular readers will recognize this as the same gap I examined in Why Transformation Dashboards Lie: the dashboard can be green while the transformation is failing, because the dashboard was built to measure the motion of the inputs and outputs.

The third is change for change’s sake, driven by what I call sector and peer FOMO. Your peer organizations launched a digital transformation. Your board heard a keynote about AI. Suddenly you need to innovate too. But nobody has defined the problem you are actually solving or what success would look like. You are performing innovation because you are afraid of being left behind, which is a fear response, not a strategy.

Why the Nonprofit Sector Is Especially Exposed

Tomorrow’s session is for a nonprofit audience, and there is a reason I built it for that room and its attendees. Every organization is susceptible to Transformation Theater. Nonprofits are especially exposed, and the sector data explains why.

Ninety-five percent of nonprofit leaders express concern about staff burnout, according to the Center for Effective Philanthropy’s State of Nonprofits study. In Nonprofit HR’s talent retention research, 45 percent of nonprofit employees said they would seek new or different employment within five years. Thirty-six percent of nonprofits ended 2024 with an operating deficit, the highest rate in a decade according to the Nonprofit Finance Fund’s State of the Nonprofit Sector survey. And 59 percent of organizations report that filling open positions with the right skills and experience is significantly harder than in prior years, according to PNP Staffing Group’s Nonprofit Salaries and Staffing Trends report.

Assemble those four facts and look at what they describe: organizations that are financially strained, short-staffed, hemorrhaging institutional knowledge, stressed out and running on fumes. Now consider that these are the organizations launching eight to fifteen change initiatives in three years. When an organization in that condition announces another transformation, that isn’t leadership. It’s theater, and the audience in the seats, meaning the staff, the constituents, the donors, and the members, already knows how the play ends.

The Psychology of Failure

Why do smart, well-intentioned leaders keep making the same mistakes? The answer isn’t incompetence. The answer is psychology. In my Transformation Psychology research series, I identified five psychological factors that predict transformation failure, and organizations almost never assess any of them before launching.

The first is psychological readiness neglect. Organizations launch assuming people are ready. They aren’t, particularly when there are already other change initiatives in process. People comply while monitored and revert when oversight relaxes. You can watch this in any CRM or ERP migration: staff use the new system while the project manager is watching, and the moment attention moves elsewhere, the spreadsheets come back. Those spreadsheets are comfortable, they are known to help, and they are understood. After the meetings, the training, the communications and more, the desired change was never really psychologically accepted. It was simply performed when being watched. Months later, the system isn’t achieving its desired outcomes, because many have not committed to the change.

The second is understanding depth misjudgment. Leaders explain the rationale once, perhaps twice, and assume everyone understands. But what people heard were strategic organization-wide benefits. They couldn’t connect the change to their own personal or professional day to day and role success, and without that personal and professional relevance, motivation is superficial and doesn’t survive the first month or quarter.

The third is leadership credibility erosion. Senior and middle management leaders demonstrate technical competence. They know the new system. They understand the strategy. What they fail to demonstrate is knowing how to manage the human side of change. The staff lose confidence not in the leader’s technical ability but in the leader’s understanding of what the change actually feels like to them from the inside.

The fourth is social dynamics mismanagement. Transformation is treated as individual behavior change (a position, a role, a single process change) when it’s actually social system evolution. Early adopters become isolated from their peers. Resistant influencers shape group opinion. Peer support networks that could accelerate adoption never develop, because nobody designed for them.

The fifth is environmental factor blindness. Psychological safety levels, resource adequacy perceptions, and cultural alignment operate as invisible barriers. People genuinely want to embrace the change but feel unsafe or unsupported doing so. Nobody measures this, because nobody thinks to ask. There is a persistent perception that people are at work to do the job as defined, regardless of what the job is or how it changes. There is a logic to that which goes back to industrial-age command-and-control leadership mantras. But we have evolved, work environments have evolved, and people are humans. Humans are complicated. They need to be nurtured, they need to be supported, and they need to understand.

Notice what all five factors have in common. None of them appears on a project plan. Few are ever discussed in a leadership meeting. But all of them determine whether the project plan actually matters.

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Decision Theater, Revisited

There is a specific pattern within Transformation Theater that longtime readers have seen me name before, in Issue 254. I call it Decision Theater: the organizational habit of performing the rituals of decision-making without actually exercising authority. Meetings are held. Decks are polished. Governance forums are convened. Leaders debate, refine language, and align on the set narratives. From the outside it looks like progress. Inside, process has replaced authority, and motion has been substituted for commitment.

The cost of this performance is becoming more and more measurable. Asana’s State of Work Innovation research found that individual contributors lose 3.7 hours per week to unproductive meetings, an increase of 118 percent since 2019.

The diagnostic questions to determine if you are in a performance are uncomfortable by design.

How many meetings in the past month ended with a concrete, irreversible decision and a named person accountable for its execution?

Are your meetings producing decisions, or producing follow-up meetings?

How often do leaders say “we need more data” when the real deficit is not data but courage?

Can you trace a strategic initiative to a single decision maker who owns its success or failure?

Are your best people spending more time preparing to decide than executing what was decided?

If any or all of those questions produced discomfort, the discomfort is data that you can use.

Resistance Is Organizational Intelligence

Now let me challenge a sacred cow: the belief that resistance to change is always bad. It’s really not. Resistance is and always will be organizational intelligence. It is telling you something.

When staff say “this too shall pass,” they aren’t being difficult. They are pattern-recognizing. They have watched their organization launch initiative after initiative after initiative, most of which were quietly abandoned within eighteen months. I have no doubt whatsoever that you can count at least ten across your own experience.

Their cynicism is not attitude. It is evidence-based reasoning from a sample size their leadership would prefer to forget or have long ago forgotten.

Think about it from the other side of the table. They invested emotional energy in the last series of change initiatives. They learned a new system. They rebuilt their workflows. Then the leaders changed direction, or the funding ran out, or the consultant contract ended, and everything reverted back to the way it was. Why would they invest that energy again? Any rational individual wouldn’t, and your staff are of course rational people.

Resistance is your organization telling you something. It tells you where psychological safety is low, where trust is insufficient, and where identity threats remain completely unaddressed. The question shouldn’t be how to overcome the resistance. The question should be whether the leadership has the humility to listen to it and take action on it before pushing harder and faster.

Two Kinds of Change

Everything above leads to a question most organizations never ask before launching: what kind of change are we actually attempting? The answer determines whether your change or transformation needs a project plan or a psychologist.

Ron Heifetz at Harvard drew a distinction decades ago that most organizations still haven’t absorbed. Technical challenges have a clearly defined problem and a known or knowable solution. An expert can solve them. Implementation is procedural. Migrate the CRM. Update the website. Install the new accounting software. These are hard, they take time, and they consume resources, but the path from problem to solution is knowable. You can project-manage your way through them.

Adaptive challenges are different in kind, not degree. The problem is embedded in people’s values, habits, and beliefs. There is no known solution; it has to be discovered. And the critical difference is this: adaptive challenges require people to change themselves. Not their tools. Not their processes. Themselves. Shifting from a scarcity mindset to an abundance mindset. Building a real culture of stewardship instead of talking about one. Moving from siloed departments to collaborative teams. No new system, org chart, or strategic plan solves these, because they don’t live in systems we so often focus on. They live in people, the people who have responsibility for implementation, the people who have responsibility to manage, and the people who do the ongoing work.

The biggest mistake most organizations make is treating adaptive challenges with technical solutions. We implement a new CRM expecting it to create a culture of data-driven decision-making. It won’t. The CRM is a tool. The culture shift is an adaptive challenge that lives in people’s heads and hearts. And when the technical solution fails to produce the adaptive outcome, leaders blame the tool, the staff, and the vendor. They blame everyone except the diagnosis.

The Identity Threat

In Season 2 of the Human Factor Podcast, I told the story of three senior pharmaceutical scientists who became the biggest obstacles to an AI-powered clinical trials initiative. Not because they opposed the technology. It was because the change threatened their professional identity. When expertise and identity become fused, and over time they always do, any change that threatens the expertise threatens the person. People are not resisting the change. They are protecting who they are.

There are three dimensions to this threat that leaders need to understand. The first is professional identity fusion. Years of expertise become inseparable from self-concept. “I am the person who knows how to do this.” When a new system makes that expertise obsolete, it doesn’t just change a process. It invalidates years of accumulated mastery. It says: the thing you spent a decade getting great at no longer matters.

The second is competence threat. Moving from expert to novice in public is psychologically devastating. Adults will actively avoid situations that expose incompetence, even when they intellectually know the new approach is better. This is why your most experienced people are often your most resistant. They have the most to lose.

The third is status and belonging risk. Expertise confers standing within a team and an organization. If AI or a new process can do what took a decade to master, what is that person’s value now? That is an existential question for an individual, a team, and an organization, and no amount of training answers it. It requires a renegotiation of professional identity, and organizations almost never create space for that renegotiation.

This is the human factor. This is what makes organizational change categorically different from installing software. Software doesn’t have an identity. Your people do.

The Contract Nobody Wrote Down

Every organization holds two contracts with its people. The written one covers compensation, benefits, and role description. The unwritten one, the psychological contract, covers expectations, norms, and implied promises. Research in organizational psychology has established that the unwritten contract is a more powerful predictor of employee behavior than the formal employment terms.

Three examples, each of which will be familiar. The written contract says “you will use Salesforce.” The unwritten contract says “your expertise with our current system is valued.” When the new CRM launches, ten years of carefully built workarounds and institutional knowledge become worthless overnight. The staff member doesn’t feel retrained. They feel betrayed.

The written contract says “your role is Major Gifts Officer.” The unwritten contract says “you are the relationship expert here, and your instincts matter.” Then AI-driven donor scoring arrives and replaces gut instinct with algorithms. The tool may be better. The psychological contract just told that person their human judgment is no longer needed.

The written contract says “we value collaboration.” The unwritten contract says “your department has autonomy.” Then a restructuring consolidates three teams into one, and the stated value of collaboration now means the practical loss of the autonomy that was the real currency of feeling as if they belonged.

This is why transformation generates feelings of betrayal rather than mere resistance. You aren’t breaking a policy. You are breaking a promise that was never written down but was deeply felt.

Perpetual Thaw

Now for what happens at the neurological level when organizations push past their people’s capacity to absorb change, because this is where the conversation shifts from organizational behavior to neuroscience.

Leaders navigating transformation carry three hidden cognitive loads. The first is the information switching burden: maintaining awareness across multiple organizational systems while constantly shifting between mental models. Strategic planning, then a donor call, then internal politics, then a technical problem. Each context switch depletes the prefrontal cortex. Not metaphorically. Physiologically. By mid-afternoon, your initiatives are not competing with resistance. They are confronting cognitive depletion.

The second is the emotional labor quotient: constantly reading and responding to emotional cues from multiple stakeholder groups while managing one’s own psychological state. This produces what psychologists call emotional dissonance, the gap between felt and expressed emotion. A leader projecting confidence while internally processing uncertainty is doing exhausting physical work that no calendar ever captures.

The third is decision complexity amplification. Today’s decisions involve exponentially more variables: multiple stakeholder impacts, regulatory considerations, cultural implications, and now AI ethics. For leaders making hundreds of consequential judgments daily, the depletion compounds through the day. And the critical insight is that these three loads don’t add. They multiply. That is why transformation fatigue feels so much worse than workload fatigue.

The costs are documented. Gallup and Workhuman estimate the global cost of employee burnout at 322 billion dollars a year in turnover and lost productivity. And research on task switching, summarized by the American Psychological Association, shows that switching between tasks can consume as much as 40 percent of productive time, a cost the researcher Sophie Leroy named attention residue. Nearly half of your people’s productive capacity can be spent on switching between contexts rather than working within one.

This brings me to a concept I call Perpetual Thaw. Traditional change models, whether Lewin’s unfreeze-change-refreeze, Kotter’s eight steps, or ADKAR, all assume episodic change: destabilize the current state, implement the change, then restabilize into a new normal. But look at what actually happens. The CRM migration starts. Before anyone stabilizes, the strategic plan refresh launches. Before that is absorbed, the culture initiative begins. Then digital fundraising. Then the training program. Nothing ever refreezes. The organization exists in a constant state of destabilization where nothing feels solid. People appear resistant and disengaged simultaneously, and leaders read this as apathy. It is not apathy. It is exhaustion. Their brains have run out of capacity to absorb one more change.

In my research on emotional exhaustion in change management, I identified three stages this exhaustion follows. Adaptation strain arrives in the first eight weeks: initial excitement mixed with underlying anxiety, the brain burning through cognitive resources at an unsustainable rate while adoption metrics look encouraging. Competence anxiety hits between weeks eight and twenty: performance plateaus despite continued effort, people avoid challenging tasks and stop asking questions because they don’t want to appear incompetent, and leadership concludes that more training is needed when what people need is psychological safety. Identity exhaustion arrives around week twenty and beyond: deep fatigue that rest doesn’t resolve, people disconnected from the professional identity they honed and curated without having developed a new one, a psychological no-man’s-land marked by cynicism, emotional numbness, and withdrawal from work relationships.

The scale of this is significant. Gallup found that 70 percent of U.S. workers experienced disruptive organizational change in the past year, and those employees report frequent burnout at nearly twice the rate of everyone else, 42 percent versus 24 percent. Gallup also found that 64 percent of employees living through disruptive change were watching for or actively seeking a new job. And Deloitte’s research with Workplace Intelligence found that nearly 70 percent of C-suite executives were seriously considering leaving for a role that better supported their own well-being. The exhaustion runs from the bottom of the org chart to the top. A nonprofit CEO put it to me this way: “I used to be decisive. Now I second-guess everything, and my brain feels like it is running a data center.” That is not a leadership failure. That is a neurological reality. You cannot motivate people out of neurological depletion. That is not an opinion. That is neuroscience.

Diagnosis Before Prescription

So what do we do about it? The first step is not a new initiative. The first step is diagnosis, and the most validated diagnostic tool comes from implementation science, a field that studies why evidence-based practices fail in real-world settings, which makes it the right field to be borrowing from.

The tool is the ORIC, the Organizational Readiness for Implementing Change instrument, developed from Bryan Weiner’s 2009 theory of organizational readiness and validated by Shea and colleagues in 2014. ORIC measures two constructs, and both must be present for a transformation to succeed. Change commitment is the shared resolve to implement the change. Not individual willingness. Shared resolve. Do your staff believe the change is necessary? Is there real psychological commitment, or performed compliance? Has the “why” been connected to personal meaning rather than organizational benefit alone? Change efficacy is the shared belief in the collective capability to implement the change. Do your people believe the organization can actually pull this off? Are resources adequate to the scope? Does your organization have a track record of completed change, or a track record of abandoned initiatives?

If either construct is low, your transformation will produce theater, not change. And here is what most organizations get wrong: they never assess either one. They launch and hope. That is not transformation. That is gambling with your mission.

Beyond the formal instrument, there are early warning signs worth watching at three levels. At the individual level: performance inconsistency, decision avoidance, learning resistance, and emotional flatness. At the team level: declining communication, the quiet cessation of innovation, rising conflict, and apathy toward milestones that once would have been celebrated. At the organizational level: metric obsession, shrinking feedback channels, initiative fatigue, and culture regression. Three or more signs at any single level indicate trouble. Signs across multiple levels indicate change and transformation fatigue, and launching another initiative into that condition will deepen it, not cure it.

If you want a faster self-diagnostic, try scoring your most recent change initiative honestly against six statements.

1. We assessed psychological readiness before launching.

2. Staff could articulate why the change mattered to them personally.

3. We sequenced changes instead of stacking them.

4. We measured emotional impact, not just adoption.

5. Leadership modeled vulnerability during the transition.

6. We built recovery time into the timeline.

Most organizations, scored honestly, fail most of these. That isn’t an indictment. It’s a starting point, and knowing where you stand is the first step toward doing this differently.

Should You Transform at All?

The hardest leadership question is not “how do we transform?” It is “should we transform at all?” And sometimes the answer is no.

Do not transform when your staff are still exhausted from the last change. Do not transform when a previous initiative has not been fully absorbed. Do not transform when you are treating a symptom instead of a root cause, or when the board wants “innovation” without a defined problem, or when incremental improvement would achieve the same result at a fraction of the disruption. Transformation is warranted when external conditions have fundamentally changed, when the current model cannot serve the mission at scale, when you have diagnosed a structural problem rather than friction, when readiness assessment shows both commitment and efficacy, and when leadership is prepared to own consequences rather than delegate blame.

When transformation is warranted, the strategies that work are grounded in the same psychology that explains the failures. Redesign the cognitive architecture: batch strategic thinking into blocks of ninety minutes or more during natural energy peaks, and convert recurring decisions into frameworks so judgment is reserved for decisions that require it. Sequence changes instead of stacking them, building recovery time into roadmaps the way athletes build recovery into training, because no serious athlete trains at maximum intensity indefinitely and no serious organization should either. Practice transformation triage: ask whether each change requires people to think differently or merely act differently, and spend hearts-and-minds energy only where it is required, handling behavioral changes through environmental design and automation. Distribute cognitive load across teams rather than concentrating it on your top performers, who are reliably the first to burn out. And make recovery visible: endorsed and modeled by leadership, because an organization’s capacity to change is limited by its capacity to recover.

None of this is a framework in the traditional change management sense. It is a different diagnosis, which is the thing most organizations have been missing. Sustainable transformation is not about moving faster. It is about moving at a pace your people can actually sustain.

The Question Underneath

Tomorrow morning I will make this argument in person at the Bridge Conference, in a room full of nonprofit practitioners who have lived every pattern in this issue. If you are attending, the session is Friday, July 31 at 9:10 AM in Prince George’s Exhibit Hall C at the Gaylord National Resort and Convention Center, and I would welcome the conversation. If you want the audio treatment, I explored this territory on the Human Factor Podcast in Season 2, Episode 27, Transformation Theater: Why Organizations Perform Change Instead of Making It, which goes deeper on several of the psychological mechanisms than one hour on a conference stage allows. And if you want to measure where your own organization stands, the Human Factor Method and the Transformation Readiness Assessment measure not just whether people support a transformation but whether they are psychologically prepared for what real change demands of them.

But I want to close with the question underneath all of it. The executive who launched eleven initiatives and could not name one change was not describing a failure of effort. Her organization had worked enormously hard. It had performed transformation with full commitment, the way a repertory company performs a demanding play. What it had not done was change, because nobody had asked whether the organization was psychologically capable of changing before the curtain went up.

So before the next initiative launches, ask the question the launch calendar never asks. Is this transformation, or is it theater? The uncomfortable truth is that from the inside, in the early months, the two look identical. The meetings are held either way. The dashboards are green either way. The difference only becomes visible two years later, in what your people are actually doing when nobody is watching. Progress does not come from looking like you are changing. It comes from accepting what real change demands.

Related Reading

Directly Referenced in This Issue

Decision Theater: When People and Organizations Mistake Motion for Commitment (Issue 254)

The pattern within the pattern. Decision Theater examines what happens when the rituals of deciding replace the exercise of authority, and it is the load-bearing wall of most Transformation Theater.

Why Transformation Dashboards Lie

The measurement half of this issue’s argument. The dashboard can be green while the transformation fails, because dashboards are built to measure activity, and activity is the thing theater produces in abundance.

Emotional Exhaustion in Change Management: Warning Signs and Solutions

The full treatment of the three stages of transformation exhaustion and the twelve early warning signs summarized here.

The Leading Indicators of Resistance

If resistance is organizational intelligence, this piece is about learning to read it early, before it hardens into the cynicism that dooms the next initiative too.

Ideas + Innovations from 2040: Innovation Theater

Where this thread began in this newsletter years ago: organizations performing innovation for audiences internal and external, long before I extended the diagnosis to transformation itself.

The Truth About Transformation: Revised and Expanded

The book-length argument that transformation is a human problem before it is a process problem, and the research foundation for the Human Factor Method.

Human Factor Podcast

Season 2, Episode 027: Transformation Theater, Why Organizations Perform Change Instead of Making It

Join the Conversation

Where is Transformation Theater running in your organization, and what would it take to name it out loud? I would love to hear your perspective. Share your thoughts with me on LinkedIn or subscribe to Ideas and Innovations on Substack for weekly insights on leadership, transformation, and the human side of organizational change.

The Truth About Transformation

If this issue resonated with you, my book The Truth About Transformation explores why most transformation efforts fail and what leaders can do differently. Available on Amazon.

Research Sources

1. McKinsey & Company. (2021). Losing from Day One: Why Even Successful Transformations Fall Short. McKinsey Global Survey.

2. BCG. (2020). Flipping the Odds of Digital Transformation Success. Boston Consulting Group.

3. IDC. (2024). Worldwide Digital Transformation Spending Guide.

4. Kotter, J. P. (1995). Leading Change: Why Transformation Efforts Fail. Harvard Business Review, May–June 1995.

5. Weiner, B. J. (2009). A Theory of Organizational Readiness for Change. Implementation Science, 4:67.

6. Shea, C. M., Jacobs, S. R., Esserman, D. A., Bruce, K., & Weiner, B. J. (2014). Organizational Readiness for Implementing Change: A Psychometric Assessment of a New Measure. Implementation Science, 9:7.

7. Heifetz, R. A. (1994). Leadership Without Easy Answers. The Belknap Press of Harvard University Press.

8. Lewin, K. (1947). Frontiers in Group Dynamics. Human Relations, 1(1), 5–41.

9. Center for Effective Philanthropy. (2024). State of Nonprofits 2024: What Funders Need to Know.

10. Nonprofit HR. (2020). Nonprofit Talent Retention Practices Survey.

11. Nonprofit Finance Fund. (2025). State of the Nonprofit Sector Survey.

12. PNP Staffing Group. (2024). Nonprofit Salaries and Staffing Trends Report.

13. Asana. (2024). State of Work Innovation Report.

14. Gallup & Workhuman. (2022). Amplifying Wellbeing at Work and Beyond Through the Power of Recognition.

15. Rubinstein, J. S., Meyer, D. E., & Evans, J. E. (2001). Executive Control of Cognitive Processes in Task Switching. Journal of Experimental Psychology: Human Perception and Performance, 27(4), 763–797.

16. Leroy, S. (2009). Why Is It So Hard to Do My Work? The Challenge of Attention Residue When Switching Between Work Tasks. Organizational Behavior and Human Decision Processes, 109(2), 168–181.

17. Gallup. (2024). Disruptive Change Is Hitting Leaders and Managers Hardest.

18. Deloitte & Workplace Intelligence. (2022). The C-suite’s Role in Well-being.

19. Novak, K. (2025). The Truth About Transformation (Revised and Expanded). 2040 Digital

20. Novak, K. (2025). Transformation Psychology Series. 2040digital.com.

Issue 274, July 23, 2026

A vice president at a financial services client described a moment to me that she had never mentioned to anyone. In a strategy session with eleven colleagues, the group converged on a market assumption she believed was wrong. She had the data. She had spent three weeks on the analysis. When the room turned to her, she heard herself say, “That works for me.” She told me she felt her face flush as the words left her, and that the discomfort had arrived before the decision to agree did. “I didn’t decide to go along,” she said. “I was already going along by the time I noticed I was doing it.”

That sequence is the subject of this issue. Not the structured silence that follows, which we have examined before in this newsletter and on the podcast. We are focusing on the thing that happens roughly a half second earlier, when the mind is still assembling the objection and something older and deeper has already cast its vote.

It deserves a name, because naming it changes what we do about it. I am going to call it the Harmony Reflex: the involuntary physiological response that registers disagreement with others as a threat to belonging to a group, team, or even an organization, before the brain has evaluated whether disagreement with others is the right thing to do. It’s not a decision. It’s not a character trait. It’s not cowardice. It’s a reflex, and like most reflexes, it fires nearly automatically whether or not the situation warrants it. And most often after the reflex fires, we question ourselves, we criticize ourselves, we might even have feelings that we violated ourselves, turned aside what we believe in, we may question our values of right and wrong or even feel stronger feelings that we let ourselves down at the felt pressure from a group of others.

Think about how many times over your career that reflex may have fired while with a group of your peers? Your organization’s leadership team? In a team meeting? It doesn’t even have to happen in person; it can happen as a result of an email chain, a series of text messages and really any situation where more than one person is involved.

What the Brain Does With Rejection

In 2003, three researchers at UCLA put people in a brain scanner and had them play a simple online game of catch. Partway through, the other two players stopped throwing them the ball.

That was the entire experiment. No insult. No criticism. No consequence of any kind. Just being left out of a meaningless game by strangers they would never meet.

The brain responded in the region that processes physical pain. Not a similar region. The same one. It is called the anterior cingulate cortex, and its job is registering how much something hurts. It became more active when people were excluded, and the more active it became, the more distress they reported feeling. Naomi Eisenberger and her colleagues titled the paper “Does Rejection Hurt?” and answered their own question. Social pain runs on the machinery built for bodily injury. Being left out registers as being hurt.

Sit with how small that experiment was. A pointless game. Anonymous partners. Nothing whatsoever at stake. And the brain reached for the same alarm it uses for a broken bone.

Now consider what that alarm does when the room is your leadership team, the stakes are your credibility, and what is on offer is not a withheld ball but the collective judgment that you are the difficult one.

Two years after “Does Rejection Hurt?”, Gregory Berns and colleagues at Emory ran a study that gets closer to what happens in a meeting. Participants judged whether two shapes were the same object rotated, a simple visual task. Before answering, they were shown what a group of peers had supposedly said. Sometimes the group was wrong.

Two findings deserve everyone’s attention.

The first is that when people went along with a wrong group answer, the change showed up in the visual parts of the brain, the machinery that handles seeing. And it happened most strongly when the wrong answer came from other people rather than from a computer. Going along was not only a decision made at the top of the mind about what to say out loud. Something was shifting further and deeper down, in how the object was perceived in the first place.

The second finding is the one I can’t stop thinking about. When people broke from the group and gave the independent answer, the brain’s fear circuitry activated. The amygdala, which handles threat detection, showed increased activity.

Read that again, because the ordinary framing has it backwards. We talk as though agreement is the easy path and disagreement is the thing that requires effort. The biology and inner workings of our brains say the reverse. Agreement is not a reward. Agreement is the absence of an alarm that standing alone sets off. Yikes.

Asch, and the Number Nobody Quotes

Most readers may remember Solomon Asch’s line experiments from 1955. Participants were shown three lines and asked which one matched a reference line. The task was so easy that people working alone got it wrong less than one percent of the time.

Then Asch seated them in a room with several other people, all secretly working for him, who confidently gave the same wrong answer. Roughly three-quarters of participants went along at least once. Across the trials, people conformed about a third of the time.

Those are the figures everyone cites as a view into how easily we seek to conform to others, doubting or ignoring what we know is right (or wrong). The figure that I think matters more is what happened when Asch broke the unanimity. He placed one other person in the room who gave the correct answer.

Conformity collapsed. Not diminished. Collapsed.

One voice. Someone else saying the right thing, and the pressure to conform lost most of its grip.

The Harmony Reflex is not triggered by the presence of disagreement. It’s triggered by the prospect of being the only one in the room, the meeting, or an entire organization. Which means the reflex is not primarily a courage problem to be solved inside individuals. It’s a structural condition of aloneness that leaders either create or dissolve, and most leaders have no idea which one they are doing.

I wrote about one half of this mechanism three years ago, in Issue 114, without recognizing that it had a second half.

That issue examined social proof, Robert Cialdini’s term for the way people copy the behavior of others when they are unsure what to do. I used the example of an audience member who wants to give a standing ovation but waits to see whether anyone else stands first. Cialdini calls the underlying response primitive automaticity, and I described it then as something the subconscious drives rather than something we decide.

What I said at the time was that social proof exerts its strongest pull under uncertainty. When we do not know enough, we look to others to tell us what to do. That is accurate, and it is the version most of us find easy to accept, because it lets the behavior look reasonable. Of course you follow the room when you do not know the answer.

The research in this issue removes that comfort. The vice president in the strategy session was not uncertain. She had the data. She had spent three weeks on the analysis. She knew the group was wrong. And she went along anyway.

Social proof explains why we follow others when we are unsure. The Harmony Reflex explains what happens when we are sure and the room is not. The first is a shortcut. The second is an alarm. Knowing the answer does not switch it off.

The Fear Is Social, Not Punitive

Here is where the research becomes uncomfortable for those of us who have built careers on the language of psychological safety, and I include myself.

In 2003, Frances Milliken, Elizabeth Morrison, and Patricia Hewlin sat down with forty employees across a range of industries and asked a simple question. What do you not say to your boss, and why?

Eighty-five percent said they had been in a situation where they felt unable to raise something they believed was important. Three-quarters of those said the colleagues who knew about the same issue felt the same way.

Then comes the part that matters. When asked why, the most common answer was not fear of being fired. Not demotion. Not retaliation of any kind. The most frequent reason people gave for staying quiet was that they would be seen as a problem, and that it would damage relationships they valued.

Read that again. People are not primarily protecting their jobs. They are protecting their belonging. The thing they won’t risk is not the paycheck. It’s their standing in the room.

That should complicate how we think about the problem, because it means an organization can be entirely safe in the formal sense, with no retaliation, no career consequence, and a leader who genuinely welcomes challenge, and people will still not speak. The threat they are managing isn’t the one the permission or the policy addresses.

John Detert and Amy Edmondson pushed this further in 2011. Across four studies, starting with 190 interviews inside a large multinational organization, they found that people held back even when the thing they were withholding was a constructive suggestion that would have helped everyone, including the boss.

What stopped them was a set of unwritten rules about when speaking up in a hierarchy is risky or out of place. People couldn’t always put those rules into words. Most had never thought about them. And most had not learned them at their current employer.

The rules arrived before the job did. Regular readers will recognize the connection to Structural Silence in Issue 256 and in an episode of The Human Factor Podcast, where I argued that organizations train people not to speak. The points I made across those pieces stand, and I will now add a qualification to both. The organization isn’t writing the curriculum from scratch. It’s reinforcing a curriculum the nervous system brought with it that is learned early and deeply by each one of us.

The Objection to Answer

Now let me argue against myself.

If we are all so conflict-averse and seeking to go along with the crowd at every turn, why is workplace conflict at a record high?

In November 2025, Acas published the largest study of its kind into conflict at work in Great Britain, surveying more than 4,500 working-age adults. Forty-four percent reported experiencing conflict at work in the previous twelve months. That is the highest level ever recorded in a British survey, well above earlier estimates that ranged from twenty-five to thirty-eight percent. Acas puts the annual cost at £28.5 billion.

That doesn’t look like a population avoiding friction. It looks like the opposite.

But look at what people did about it.

Nearly one in five did nothing at all. Among managers who had a conflict with someone who reported to them, more than a third did nothing, a rate more than double their inaction with anyone else. And fifty-seven percent of everyone reporting conflict came away with stress, anxiety, or depression.

The data does not contradict the Harmony Reflex. It locates it. Avoidance doesn’t show up in whether conflict occurs. It shows up in what happens next.

And notice which conflict managers most reliably decline to touch. The one with the person they see every day, whose regard they depend on, where the relationship is closest and the belonging is most exposed. Exactly where Milliken and her colleagues said the fear lives.

There is a second distinction worth making, and the Acas data makes it visible. The most common subjects of conflict were capability and performance, and personal friction between people. Those are conflicts about people.

What organizations get very little of is disagreement about ideas. The assumptions in the deck. The numbers in the model. The strategies nobody wants to be the one to question.

We are not short on friction. We are short on the specific kind that improves decisions. Conflict between people is abundant and mostly costly. Disagreement about the work itself is scarce and mostly valuable. The Harmony Reflex can’t tell them apart, because to the brain’s alarm system, standing alone is standing alone regardless of the subject.

What the Reflex Costs Us

The temptation is to treat all of this as a workplace pleasantness issue in that we all simply want to get along. It’s not. It’s a decision quality issue, and Charlan Nemeth’s work at Berkeley shows why.

In 1986, Nemeth drew a distinction that has held up for forty years.

When a group hears a dissenting view, people think more broadly. They consider the problem from several angles, reexamine their own assumptions, and go looking for information they hadn’t considered. When a group hears only the majority view, thinking narrows. People concentrate on the position in front of them and stop searching.

Here is the counterintuitive part, and it’s the heart of the matter. The dissenting view improves the group’s thinking whether or not the dissenter is right. What changes isn’t the answer. It is the quality of the process the group uses to reach one.

Which means the Harmony Reflex isn’t merely suppressing correct objections. It is removing the friction that makes any group think well, including friction that turns out to be mistaken. We aren’t just losing the good ideas people are afraid to say. We are losing the thinking that even wrong objections would have forced.

In 2001, Nemeth and her colleagues tested the devil’s advocate, the standard organizational workaround in which someone is assigned to argue the other side, against genuine disagreement.

Real dissent produced more original thinking, more honest consideration of the opposing position, and actual changes of individuals’ minds. The devil’s advocate did something else entirely. It left the group more firmly attached to the position it started with.

Read that carefully, because it is worse than the technique simply not working. Assigning someone to argue against the plan appears to strengthen the group’s belief in the plan. We have been running a ritual that inoculates us against the very thing it was designed to provide. A companion study that same year tested three separate versions of the devil’s advocate role and found genuine dissent superior to all of them, concluding that the value lies in authenticity and that role-playing cannot reproduce it.

The devil’s advocate is our attempt to purchase the benefits of dissent without paying its social cost. It doesn’t work, obviously, because everyone in the room knows the difference between a person who disagrees and a person who has been asked to perform the disagreement. They are simply playing a role. The group’s thinking isn’t fooled. Real dissent works because it’s real, which is to say because someone actually stood alone and their brain actually registered it. There is no version of this that is free of cost.

Where This Sits in the Larger Pattern

I have been circling this reflex behavior for years without realizing it until recently. Decision Theater in Issue 254described organizations mistaking motion for commitment. The Meeting after the Meeting described strategies that lose support in the corridors where the real conversation happens. The Permission Paradox in Issue 269 described people concealing their AI use because disclosure invited judgment. How to Have a Complicated Conversation, all the way back in Issue 47, offered techniques for conversations we now know most people are neurologically primed to exit.

Each of those issues described a different symptom. The Harmony Reflex is the mechanism underneath all of them.

It also completes a circle of thought that has run through this newsletter and through The Truth About Transformation since the beginning. Resistance is signal, not obstacle. I have made that case for years, over and over again even when I knew I wasn’t being heard.

I want to add its mirror. Agreement is also a signal, and we have almost no tools for reading it. When someone resists, we at least know something is there. When eleven people agree in a strategy session, we record alignment and move on, having measured nothing except the reliable operation of a reflex none of us controls.

What Leaders Owe Their People Here

The instinct is to tell people to be braver, have courage, speak up and be honest. The instinct misreads the reflex completely. You can’t exhort your way out of a fear response.

What Asch demonstrated is that the reflex responds to a structural condition, not a moral one, and structural conditions are exactly what leaders control. Someone has to not be alone. That is the whole finding. Whether that means assigning genuine positions rather than devil’s advocacy, gathering views before the room converges rather than after, or simply noticing when a decision has been reached with suspicious ease and refusing to bank it, the design principle is the same. The question is never whether your people are brave enough. The question is whether your meetings systematically manufacture the condition of aloneness that the brain is built to avoid.

It also requires leaders to separate the two things the reflex can’t separate. Disagreement about an assumption isn’t conflict with a person. Most organizations have never made that distinction explicit, and so people, sensibly, treat every objection as a relationship risk. If you want the first kind and not the second, you will have to say so, repeatedly, and then demonstrate it in the moment someone tests whether you meant it.

And it requires a harder acknowledgment. If the fear people are managing is social rather than punitive, then the absence of retaliation in your organization proves nothing. You can have no consequences, an open door, and a genuine appetite for challenge, and still preside over rooms where nobody says the thing. The reflex does not read your policies. It reads the room.

The Question Underneath

The vice president who told me “that works for me” was not weak although she felt like she was. She was a competent professional whose body reached a conclusion before her judgment could, in a room that gave her no reason to think anyone else saw what she saw.

Which raises the question I would leave with any leader reading this. When your last significant decision met no resistance, what did you remember? Almost certainly alignment. Almost certainly you moved on.

But the smoothness of that meeting was data, and it was telling you something. It may have been telling you the decision was sound. It may have been telling you that eleven people felt their faces flush and said it worked for them.

You cannot tell those apart from the outside. That’s the point. And it means the most confident moments in your organization are the ones you understand least.

Related Reading

Directly Referenced in This Issue

Structural Silence: Why Organizations Train People Not to Speak (Issue 256)

The system-level counterpart to this issue. Structural Silence argues that organizations teach people that quiet is safer than truth. The Harmony Reflex argues that the nervous system arrives already believing it, and that the organization is reinforcing a lesson rather than originating one.

The Permission Paradox: Why Your People Are Hiding the Very Things You Say You Want (Issue 269)

Concealment driven by fear of judgment rather than fear of punishment. The Milliken research cited here explains why that distinction holds.

Decision Theater: When People and Organizations Mistake Motion for Commitment (Issue 254)

Where this issue examines why disagreement does not surface, Decision Theater examines what organizations do with the frictionless consensus that results.

Social Proof (Issue 114)

The other half of this mechanism. That issue explained why we copy others when we are uncertain. This one explains what happens when we are certain and break from the room anyway.

How to Have a Complicated Conversation (Issue 47)

The practical companion, written before I had a name for what makes those conversations physiologically difficult in the first place.

The Truth About Transformation: Revised and Expanded

Resistance is signal, not obstacle. This issue offers the mirror argument: agreement is signal too, and we have almost no instrumentation for reading it.

Human Factor Podcast

Season 2, Episode 019: Structural Silence, Why Organizations Train People Not To Speak

Season 2, Episode 018: The Organizational Immune System, When Culture Attacks What It Doesn’t Recognize

Research Sources

1. Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003). Does Rejection Hurt? An fMRI Study of Social Exclusion. Science, 302(5643), 290--292.

2. Berns, G. S., Chappelow, J., Zink, C. F., Pagnoni, G., Martin-Skurski, M. E., & Richards, J. (2005). Neurobiological Correlates of Social Conformity and Independence During Mental Rotation. Biological Psychiatry, 58(3), 245--253.

3. Asch, S. E. (1955). Opinions and Social Pressure. Scientific American, 193(5), 31--35.

4. Milliken, F. J., Morrison, E. W., & Hewlin, P. F. (2003). An Exploratory Study of Employee Silence: Issues that Employees Don’t Communicate Upward and Why. Journal of Management Studies, 40(6), 1453--1476.

5. Detert, J. R., & Edmondson, A. C. (2011). Implicit Voice Theories: Taken-for-Granted Rules of Self-Censorship at Work. Academy of Management Journal, 54(3), 461--488.

6. Nemeth, C. J. (1986). Differential Contributions of Majority and Minority Influence. Psychological Review, 93(1), 23--32.

7. Nemeth, C. J., Connell, J. B., Rogers, J. D., & Brown, K. S. (2001). Improving Decision Making by Means of Dissent. Journal of Applied Social Psychology, 31(1), 48--58.

8. Nemeth, C., Brown, K., & Rogers, J. (2001). Devil’s advocate versus authentic dissent: stimulating quantity and quality. European Journal of Social Psychology, 31(6), 707--720.

9. Acas (2025). How prevalent is individual conflict at work in Great Britain in 2025? Published 20 November 2025. Survey conducted by the National Centre for Social Research, August to September 2025, 4,558 respondents.

Issue 273, July 16, 2026

This week I am doing something a little different. Rather than a single new examination, this issue draws from our archive of 272 issues, published weekly since May 2021, to trace one thread that runs through nearly all of them, and that has moved, in the past several months, from the background of organizational life to the center of it. That thread is trust. Who we extend it to, how we decide, what happens when we get it wrong, and what it costs us when we stop deciding at all.

The current conversation gives this thread its urgency, and the most telling numbers have nothing to do with technology. The 2025 Edelman Trust Barometer, in its twenty-fifth edition, documented institutional trust at or near historic lows, with particular weakness in employer trust during periods of organizational change and transformation. PwC’s Trust in US Business Survey found that 86 percent of executives believe employee trust in their company is high, while only 67 percent of employees say they highly trust their employer, an 18-point gap that has widened rather than narrowed, and one reason it persists is that most organizations have no structure for seeing it. Leaders are not misreading their technology. They are misreading their people.

AI has simply given the gap a louder stage. The World Economic Forum wrote just last month about a widening believability gap between what leaders say about AI and what their people are prepared to accept as true. Inside organizations, employee surveys and the workforce’s behavior are telling two different stories. Leaders report enthusiasm. Employees report suspicion, not primarily of the technology, but of the people deploying it and the intentions behind the deployment. The gap between those two stories is where change and transformation initiatives quietly stall, then collapse into failure.

Here is what the archive of this newsletter makes plain. The machine era did not create the trust problem. We wrote about it at length even before AI became part of the conversation. What it did do was expose what was already there, often unspoken, and accelerate it. We were struggling to trust institutions, leaders, information, and one another long before a model could draft a condolence note or overrule an analyst in a forecast meeting. AI simply removed the buffers that let us avoid looking at the problem directly. That has been the argument of this newsletter since its earliest issues, and it is why the eight pieces gathered here, spanning from the newsletter’s first summer in 2021 to two weeks ago, read less like a retrospective and more like a single argument built over five years. It begins with people, because that is where trust begins and where it breaks.

Each piece below includes a fresh framing for this moment, and a link to the full issue. Whether you are leading an organization through change, rebuilding confidence with a workforce that has stopped believing the town halls, or simply trying to decide what to believe yourself, the principles hold: trust is a process rather than a feeling, it is a pattern rather than a state, and it is revealed by behavior rather than declared by any survey.

Personal Trust and Loyalty are Table Stakes

June 30, 2021

From the newsletter’s first summer, this issue examined the relationship every organization depends on before any other: the one with the individual customer, member, or subscriber. Writing in the era of what we called the “it’s only about me” economy, we argued that personalization is not a marketing trend. It is the path by which an organization earns engagement, and engagement, sustained honestly over time, becomes trust, and trust becomes loyalty. The piece drew the distinction that still matters most: trust is a bond that takes authenticity to build and a promise you cannot break, and the spray-and-pray tactics that treat every individual identically tell your stakeholders precisely how little you know them.

Five years later, the argument has a sharper edge. The tools for personalization at scale are now AI systems, which means every organization can manufacture the appearance of knowing its customers without doing the work of actually knowing them. The mechanics have become effortless. The authenticity has not, and the issues later in this sampler show what happens when the two are confused.

Read this issue>

How Do You Trust?

Issue 42, February 10, 2022

In early 2022, in the middle of a different crisis of confidence, we broke trust down to its working parts. Borrowing from John Ciardi’s How Does a Poem Mean, the issue asked not whether you trust but how: the source, your history with the source, the alignment of your values with theirs, your own biases, and your yearning to want to trust in the first place. The 2022 Edelman Trust Barometer had just named the cycle of distrust, and business stood as the only institution people still trusted to lead.

Reading it now, what stands out is how little the mechanics have changed and how much the stakes have. Every question we currently ask, about a leader’s announcement, a colleague’s assurance, or a machine’s output, is the same question this issue asked about people and institutions. Trust is a process, not a feeling, and the discipline of asking how remains the best protection we have against both misplaced faith and reflexive suspicion.

Read this issue>

Breaking the Bond of Trust

Issue 97, March 2, 2023

One of the unexpected outcomes of the pandemic was a new level of trust between leaders and their workforce. Forced out of the office, both sides had to trust one another to meet deadlines and goals, and performance began to be measured by outcomes rather than presence. This issue examined what happened when leaders started to break that bond, mandating returns to the office after three years in which the work had demonstrably been done, and monitoring screen time and keystrokes in place of results. The question the issue posed to every command-and-control leader still stands: if the work was performed successfully for three years, what does the reversal say about whom you actually trust?

The reason this issue belongs in this sampler is what it reveals about how trust actually breaks inside organizations. It rarely breaks over a single betrayal. It breaks when people watch the distance grow between what leadership says and what leadership does. That is the same distance employees are measuring today when they hear reassurance about AI and headcount, and it is why the trust gap in the current moment cannot be closed with better messaging. Trust is the anchor of the relationship between managers and the people who do the work, and once strained, it converts every future announcement into a claim requiring evidence.

Read this issue>

Measuring Trust as a Behavioral Asset

Issue 266, May 28, 2026

A client published survey results showing 84 percent of customers reporting high trust. Two years and three breaches later, the customer base contracted sharply, and the interviews revealed that the trust had collapsed not because of the incidents themselves but because of how the organization communicated through them. The trust score had not predicted the behavior. The behavior revealed what the trust score failed to capture.

The issue’s central argument is the one I would put in front of every leadership team this year. Trust is not a state that can be captured by asking. It is a pattern that can only be measured by observing, across three distinct surfaces: internal trust among employees and leadership, customer trust, and partner trust. Each reveals itself through behavioral signals, from how quickly bad news travels upward to whether customers forgive errors, and none of them shows up in a sentiment survey. This is also why the executive overconfidence in the PwC findings should surprise no one. We measure revenue with precision. We measure trust, the thing every other measure depends on, with almost nothing at all.

Read this issue>

From the Measuring What Matters series: The Value of Intangibles

Being Human in the Age of AI: Trust, Adoption, and Ethical Dilemmas

Issue 233, October 9, 2025

Only after the human foundations does the machine enter the story, and it enters as a test of everything above. The issue that accompanied the launch of the Human Factor Podcast asked a deceptively simple question: why do we follow the Netflix recommendation without a second thought and hesitate when AI proposes a strategic decision? The answer is not inconsistency. It is a sophisticated risk assessment running on evolutionary programming. Cognitive Territory Theory maps where the hesitation lives: low-stakes assistance feels like enhanced information, our competence zones trigger identity threat, and high-stakes irreversible decisions activate our deepest caution.

The issue argued that our resistance to AI may be evolutionary wisdom rather than a character flaw, and it warned about delegation creep, the slow, unconscious ceding of cognitive territory to systems we have not consciously chosen to trust. Nine months later, that warning reads less like a caution and more like a description.

Read this issue>

Listen or Watch: Episode 001: Being Human in the Age of AI: Trust, Adoption, and Ethical Dilemmas

The Algorithmic Mirror: What AI Reveals About How We Actually Think and Decide

Issue 253, February 26, 2026

What makes AI unsettling is not that it changes how decisions are made, but that it exposes how decisions have always been made, without the human buffers we rely on to soften the truth. Algorithms do not invent priorities. They surface them. They do not create bias on their own. They reveal what we fed them. A University of Washington study found that when AI systems exhibited bias in hiring recommendations, human decision makers tended to mirror those biases, and without the biased input, their choices were unbiased.

The mirror reframes the trust gap in a way most commentary misses. Before we ask whether the machine deserves our trust, we have to ask whether the judgment we have been trusting all along was ever as objective as we believed. The discomfort so many leaders feel when they look at algorithmic output is not evidence of machine failure. It is evidence of exposure, and exposure, uncomfortable as it is, is the signal rather than the problem.

Read this issue>

The Empathy Outsource: What Happens When the Care Was Real but the Author Was Not

Issue 270, June 25, 2026

A manager received a warm, specific, unexpectedly moving message from her director on the morning after her father’s funeral. Then she noticed, in the cadence of the third sentence, a smoothness that did not match how her director actually spoke, and the comfort inverted. The research on this dynamic is strikingly consistent. AI-generated messages can make people feel more heard than human ones, right up until the recipient learns the source. Researchers, including Amit Goldenberg of Harvard Business School, have named the effect the AI Penalty: label a message as machine-authored, accurately or not, and its emotional worth collapses.

The lesson for the trust gap is precise, and it is a human lesson rather than a technological one. In the moments that exist to signal one human being’s investment in another, the authorship is the message. AI can replicate the prose. It cannot replicate the spending of oneself, and the recipient’s mind is built to know the difference.

Read this issue>

The Confidence Transfer: What Happens to Organizational Expertise When We Trust the Machine Over the Person

Issue 271, July 2, 2026

A senior analyst with fifteen years of pattern recognition named a specific risk in a forecast meeting. A colleague typed the parameters into an AI tool and read the output aloud. The room shifted, the questions stopped, and her judgment was quietly set aside, not because anyone argued against it, but because the model sounded certain. A month later she was proven right. By then no one remembered she had said so.

The Confidence Transfer is the quiet migration of trust from people to systems, happening by default rather than by discernment. The danger is not that machines are frequently wrong. It is that every unexamined deferral erodes something hard to reverse: expertise is a practice sustained only by use, and the judgment we stop exercising, we slowly stop having. The question is not whether to trust the machine. The question is whether your organization is still deciding that question on purpose, or whether it has already stopped deciding it at all.

Read this issue>

Listen or Watch: Episode 028: The Trust Paradox: Transforming the Institutions Built to Stay the Same

The Thread That Holds

Eight pieces, five years, one argument. Trust is a relationship earned one individual at a time (June 2021). It is a process we can examine (Issue 42). It is a bond between leaders and the people who do the work, kept or broken by behavior rather than words (Issue 97). It is a behavioral asset that can be measured honestly or reassuringly, but rarely both (Issue 266). It is territory our minds defend for good evolutionary reasons (Issue 233). It is a mirror that shows us our own judgment before it shows us the machine’s (Issue 253). It is authorship in the moments that matter (Issue 270). And it is the practice of judgment itself, which survives only as long as we keep exercising it (Issue 271).

The trust gap that dominates the current conversation is real, and it will not be closed by better tools, louder reassurance, or more confident dashboards. It will be closed, where it closes at all, by the slower work these issues describe: deciding deliberately what to trust, observing what people actually do, keeping the relational core of leadership unambiguously human, and protecting the conditions under which judgment is practiced rather than outsourced. That is human factor work, and it has been the subject of this newsletter through all 272 issues that precede this one.

Later this month, timed to my talk at the 2026 Bridge Conference, we will take up a closely related pattern that deserves an issue of its own: Transformation Theater. Keep an eye out for it. Until then, the archive is open.

Explore the full archive at:

2040digital.com/newsletter

Assess Your Organization’s Transformation Readiness:

transformationassessment.com

Connect with Us

What does trust look like in your organization right now, and how would you know? We would love to hear your insights. Share your experiences with us on our Substack or join the conversation on our LinkedIn. For more insights on navigating organizational complexity, explore our archive of Ideas and Innovations newsletters or pick up a copy of The Truth About Transformation: Leading in the Age of AI, Uncertainty and Human Complexity.

Go Deeper: Subscribe to the Human Factor Podcast, where we explore the psychology behind transformation success, including the trust dynamics sampled in this issue. Season 2 concluded this month with eighteen episodes on the human side of change.

The 2040 construct to change and transformation: What’s the biggest reason organizations fail? They don’t honor, respect, and acknowledge the human factor.

Issue 272, July 9, 2026

Before recording the final episode of the Human Factor Podcast’s second season last week, I went back and listened to the very first one. I wanted to hear whether the thesis still held. The thesis was simple. The reason roughly seventy percent of transformations fail is not the technology and not the strategy. It is the human factor, the psychology of the people who have to live the change while they keep the organization running. Eighteen episodes later, I am more convinced of that than when I started. But sitting with the whole season at once, I noticed something I had not seen while we were in the middle of it. Every conversation, whether it was about middle managers or generational conflict or an industry of thirty thousand companies, kept arriving at the same three discoveries by different roads. And each of those discoveries is something you have almost certainly felt in your own organization, even if no one around you has named it. That is what this issue is for. Not a recap of a season, but the argument the season made, because understanding the human factor, it turns out, is not the same as knowing what to do about it. Seeing the problem was Season 1. This season was about what you do once you can see it.

The Thread We Have Been Following

Readers of the last several issues will recognize the territory. In The Permission Paradox (Issue 269), we examined why depleted employees adopt AI in the shadows. In The Empathy Outsource (Issue 270), we asked what happens when leaders hand the language of care to a machine. In The Confidence Transfer (Issue 271), we followed the quiet migration of trust from human judgment to machine output. Underneath all three sat the same gap this podcast has been probing since its first episode: the gap between what organizations say and what their behavior reveals. The season and the newsletter have been running on the same spine. So consider what follows the hinge between them: three discoveries, each grounded in decades of research, each tested this season against people who do the work in the field.

Change Is Felt Before It Is Managed

Think about the last major change your organization attempted. Somewhere in that effort there was a person, probably one of your best, who resisted in a way that surprised you. The conventional reading is stubbornness, or politics, or fear of technology. The season kept returning to a harder and more useful reading. When years of mastery fuse with a person’s sense of self, any change that threatens the expertise threatens the person. Your most capable people are often your most resistant precisely because they have the most identity invested in the old way. That is not a character flaw. It is a threat response, and it behaves like one.

And it does not stay contained in the person experiencing it. Nicholas Christakis and James Fowler’s research on social contagion demonstrated that emotional states move through human networks the way infection moves through a population, and Elizabeth Stewart and I spent an episode this season tracing exactly that dynamic inside organizations under change. Anxiety and trust are both contagious. They spread through teams faster than any memo, and leaders are the most contagious carriers of all, whether they intend to be or not. Richard Lazarus and Susan Folkman’s transactional model of stress explains why: people do not respond to change itself; they respond to their appraisal of what the change means for them, and they read that meaning largely off the faces and behavior of the people above them. Which means the emotional weather of your transformation was set long before the kickoff meeting, by signals you did not know you were sending.

This is the first discovery, and it is worth sitting with because it inverts where most change effort goes. We spend our budgets on process and communication plans, on the assumption that change is managed into an organization. The research says change is felt before it is managed, appraised as threat or safety in the first weeks, and the appraisal spreads. By the time the resistance is visible enough to manage, it has usually already been decided.

Structure Beats Intention

The second discovery is less comfortable, because it removes the consolation of good intentions. Much of this season examined the quiet architecture that shapes behavior long before anyone makes a decision, and the research here is some of the oldest and most solid in organizational psychology. Robert Kahn and his colleagues documented in 1964 what happens to people placed in positions of chronic role conflict, and sixty years later that research still describes your middle managers with uncomfortable precision. They are squeezed between a strategy they did not write and a team that looks to them to absorb the shock. Quy Huy’s work at INSEAD showed that middle managers function as the emotional balancers of change, holding their teams steady while carrying demands from above, and that organizations rely on this invisible labor while structurally punishing the people who perform it. When your transformation dies in the middle, as most do, that is not a failure of commitment. It is the predictable output of a structure that assigned its hardest psychological work to the people given the least authority to do it.

The same structural logic governs what your organization is able to hear. Elizabeth Morrison and Frances Milliken’s research on organizational silence established that when people do not speak up, it is rarely a failure of individual courage. It is a rational response to what the structure rewards and punishes, learned through lived experience. The 2024 Institute of Business Ethics survey found that a significant share of employees who witness misconduct choose not to report it, with fear of retaliation the primary reason, and a 2025 study in Scientific Reports confirmed that silence in organizations is structurally predictable from power dynamics and informal reward systems. Your culture, in other words, has an immune system. As James Elliott and I explored this season, the same mechanisms that protect an organization from genuine threats also attack the new and the unfamiliar, including the change the organization says it wants.

And beneath all of it sits a contract nobody signed. Denise Rousseau’s foundational research at Carnegie Mellon established that the unwritten psychological contract, the implicit promises people believe their organization has made to them, predicts behavior more powerfully than the formal employment terms do. Transformation almost always rewrites that contract unilaterally. This is why the feeling that follows is not resistance but betrayal, and why leaders who treat the two as the same thing consistently lose their best people. A leader can hold the best intentions in the world and still be defeated by an architecture that quietly rewards the opposite of what the strategy asks. If you want different behavior, you usually have to change the structure before you can change the people. Most organizations attempt it in the reverse order, and then blame the people.

The Pattern Is the Proof

Here is the question a skeptical reader should be asking by now. How do we know these dynamics are general, and not just stories from particular organizations? The middle of the season was, in effect, an answer to that question, because it was built around vantage points that almost no one in organizational life occupies. Erin Fuller and her team at MCI work across more than one hundred associations at once, and what she described is the same governance dynamics, the same resistance patterns, the same identity crises and structural silences playing out simultaneously across organizations of entirely different sizes, industries, and missions. Mike Peroni has watched the same thing from inside scaling companies, where what breaks during growth is rarely the strategy but the trust and shared understanding that did not scale with the headcount. Eric Hoplin leads a trade association of more than thirty thousand interdependent companies, and even there, at the scale of an entire industry, the failure mode is human: when one link in a supply chain modernizes ahead of the others, the system does not get faster; it generates new friction, because transformation at every scale is a collective psychological event wearing an operational disguise.

This repetition matters, and systems research explains why we miss it. Peter Senge’s work on systems thinking and Donella Meadows’ research on leverage points both point to the same conclusion: we consistently intervene where problems are visible rather than where they are generated, and single-dimension interventions in multi-dimensional systems reliably fail. Charles Perrow’s work on cascading failure adds the warning that in tightly coupled systems, small unaddressed dynamics compound. This is why the new CRM does not fix the silence, why the reorganization does not repair the broken contract, why the training program does not touch the identity threat. If the same human pattern repeats across a hundred organizations, the pattern is not an indictment of your people. It is evidence that you are dealing with the human factor, and that it obeys laws which do not care about your industry.

The Mirror We Did Not Ask For

No season recorded in this moment could avoid artificial intelligence, but what surprised me was the form the subject insisted on taking. AI entered nearly every conversation not as a technology but as a diagnostic, the clearest instrument we have ever had for revealing how humans actually think, decide, and deny. The research tradition here is older than the current excitement. Raja Parasuraman and Victor Riley documented in 1997 how humans over-rely on automation, monitoring less and questioning less exactly when the system has earned their trust, and Jennifer Logg’s more recent work with Julia Minson and Don Moore found that people weight identical advice more heavily when they believe it comes from an algorithm. Last week’s issue followed those threads in depth. What the season added is the organizational expression of the same psychology: the readiness illusion, our tendency to confuse what a system can do in a controlled demonstration with what an organization is ready to live with in a real environment, on real customer data, with real consequences.

And when readiness is absent but pressure is high, organizations do what people do. They perform. One episode this season named a pattern I suspect every reader has lived through at least once: transformation theater, the elaborate ritual of change without its substance. The meetings occur, the dashboards exist, the steering committees convene, and the motion gets reported as progress. McKinsey’s global transformation surveys and BCG’s analysis of more than 850 organizations converge on the finding that the failure rate has not meaningfully improved despite decades of accumulated experience and an estimated 2.5 trillion dollars in annual global spending. We are not short of investment. We are short of the willingness to let change require something of us. If your organization has eleven initiatives in flight and a quiet feeling that nothing is actually different, you do not have an execution problem. You have a theater problem, and the audience knows.

What the Practitioners Kept Saying

The season closed with the people who have paid for their lessons in years rather than theories, and they kept handing me variations of a single idea. Deborah Patton, my co-author on The Truth About Transformation, and Justin Thorp, who runs email and audience strategy at Oracle, took up the tension between data and craft in how organizations speak to their people and members. You can segment an audience into dozens of behavioral cohorts and still land with the emotional weight of a form letter, and you can write something genuinely moving and send it to the wrong people at the wrong moment. The research we discussed in that conversation suggests psychographic messaging aligned to what people actually care about can outperform demographic targeting by a wide margin, and yet only about one in six employees, customers, subscribers, or members say the communications they receive feel highly relevant. Sit with that for a few moments. More often than not, the people you are writing to feel that you are not really talking to them. Communication is simply the human factor in another form. The message that does not account for the person does not land, and neither does the transformation.

Then Tom Serena, who has led the American Gastroenterological Association since the 1990s, gave the season its plainest sentence. The denominator of change, he said, is pace and patience. You rarely control the pace, and if you try, you will spend the foundation before the change is ever visible. He told me about proposing a venture fund inside a medical association and watching the board refuse it. He read that as resistance. Only later did he understand that the board was not refusing the idea. He had moved faster than they could absorb. Every framework this season examined- identity threat, contagion, the psychological contract, structural silence- is in the end a description of how fast human beings can metabolize change, and Tom’s three decades in one seat are a controlled experiment in respecting that rate. His advice to any new leader deserves to be carried around: do not come in to transform. Come in to find out what needs to be transformed.

Patience, Repeated

So what does the season amount to? Three discoveries, and they stack. Change is felt before it is managed, which means the psychological work comes first, or it does not come at all. Structure beats intention, which means the architecture around people must change before the people can. And the pattern repeats at every scale, which means none of this is a defect in your organization. It is the operating behavior of human systems under change, documented across sixty years of research and confirmed this season by practitioners standing at vantage points from a single team to an entire industry.

But the deepest thing the season taught me is what those three discoveries imply together. Transformation is not an event. It is a practice. It is patience, repeated. The organizations that actually change are not the ones with the boldest launch. They are the ones willing to do the quiet, unglamorous, repeated work long after the announcement, when no one is clapping. We resist this conclusion because it offers no moment of arrival, nothing to celebrate at the all-hands. The institutions that endure are built mostly out of work no one sees while it is happening. That sentence is Tom Serena’s legacy in a single line, and I have not stopped thinking about it since we recorded.

Where This Goes Next

Season 3 begins in late August or September, and its center of gravity is already set: the dynamic between artificial intelligence and human beings, not as a technology story but as the largest live experiment in the human factor ever run, unfolding in public and at full scale. What happens to identity, to trust, and to the psychological contract when the new colleague is not a person, and when work that used to define people starts to be done by software? We will also close the loop on measurement, because what you measure determines what you manage, and readiness, trust, and the gap between stated and revealed commitment remain the things most organizations still do not measure, even though they are exactly the things that decide the outcome.

Between now and then, the work does not stop. Every episode of Season 2 is available at 2040digital.com and wherever you watch or listen to podcasts, and the Human Factor Method and Transformation Readiness Assessment can give you a baseline on whether your people are psychologically prepared for what real change demands. The seventy percent failure rate was never inevitable. It is the result of overlooking what matters most. And so the question this season leaves behind is not whether your organization can survive its next transformation.

The question is whether it is willing to practice one, patiently, repeatedly, long after the announcement, when no one is clapping.

Related Reading

The themes in this issue connect across the Ideas and Innovations newsletter and the full Season 2 catalog of The Human Factor Podcast, available at 2040digital.com/human-factor-podcast. The following pieces offer the most direct connections.

The Confidence Transfer (Issue 271) follows the migration of trust from human judgment to machine output, the behavioral consequence of the algorithmic mirror this season held up.

The Permission Paradox (Issue 269) and The Empathy Outsource (Issue 270) complete the recent arc on how AI reveals, rather than creates, the human dynamics organizations prefer not to see.

Structural Silence (Issue 256) explores in essay form why silence is a designed outcome rather than a failure of courage, the newsletter companion to this season’s examination of the systems that train people not to speak.

Organizational Memory Loss (Issue 258) examines why organizations forget what they learn, the institutional counterpart to Tom Serena’s observation that enduring institutions are built out of work no one sees.

Measuring Trust as a Behavioral Asset (Issue 266) anticipates the measurement agenda of Season 3, treating trust as something organizations can observe in behavior rather than assert in surveys.

Sources

1. Robert L. Kahn et al., Organizational Stress: Studies in Role Conflict and Ambiguity (1964)

2. Quy Nguyen Huy, research on middle managers as emotional balancers of change, INSEAD

3. Elizabeth W. Morrison and Frances J. Milliken, Organizational Silence: A Barrier to Change and Development in a Pluralistic World, Academy of Management Review (2000)

4. Denise M. Rousseau, foundational research on the psychological contract, Carnegie Mellon University, including Psychological Contracts in Organizations (1995)

5. Nicholas A. Christakis and James H. Fowler, research on social contagion in human networks

6. Richard S. Lazarus and Susan Folkman, Stress, Appraisal, and Coping (1984), the transactional model of stress

7. Peter M. Senge, The Fifth Discipline: The Art and Practice of the Learning Organization; Donella H. Meadows, Leverage Points: Places to Intervene in a System; Charles Perrow, Normal Accidents: Living with High-Risk Technologies

8. Raja Parasuraman and Victor Riley, Humans and Automation: Use, Misuse, Disuse, Abuse, Human Factors 39, no. 2 (1997)

Issue 271, July 2, 2026

A senior analyst at a client organization watched a meeting go a direction she knew was wrong. The team was weighing a forecast, and she had fifteen years of direct experience with exactly this kind of decision, the kind of pattern recognition that does not reduce to a slide. She said so. She explained her reasoning, named the specific risk she saw, and recommended a different path. Across the table, a colleague pulled up an AI tool, typed in the parameters, and read the output aloud. The output disagreed with her. The room shifted in a way she described as almost physical. The questions stopped. The decision moved toward the machine’s recommendation, and her fifteen years of judgment were quietly set aside, not because anyone argued against them, but because the model had spoken and the model sounded certain. She told me the part that stayed with her was not being overruled. It was how quickly it happened, and how relieved everyone seemed to no longer have to weigh competing human judgments at all. A month later, the forecast proved her right. By then no one remembered that she had said so.

This is the dynamic I want to examine, because it is becoming one of a series of dynamics representing the most consequential and least discussed shifts in how organizations actually make decisions in the age of AI. We are living through a quiet transfer of confidence, away from human judgment and toward machine output, and it is happening faster than our ability to think clearly about when it is warranted and when it isn’t. I call it The Confidence Transfer, the gradual migration of trust from people to systems, often regardless of which one is actually right in a given case. Its danger isn’t that machines are frequently wrong, although we do underestimate how frequently they hallucinate. It’s that the transfer happens by default rather than by discernment, and that every time it happens, something erodes further in the organization that is very hard to reverse.

The Thread We Have Been Following

For readers who have been with the last several issues, this continues a deliberate arc. We have examined, in The Permission Paradox (Issue 269), why depleted employees adopt AI in the shadows. We have examined, in The Empathy Outsource (Issue 270), what happens when leaders outsource the language of care. And now we turn to judgment itself, the most consequential thing an organization asks of its people. I will restate what I have said in each of these pieces, because it remains true and it matters most here. This is not an AI newsletter, and it has not become one. The subject is, as always, the human factor. AI is simply the most powerful instrument we have ever had for revealing how humans actually behave under uncertainty, pressure, and the temptation of an easier path. I have, for years, raised the point, in this newsletter, podcast and in my books, that we shy away from critical thinking, we shy away from making decisions, particularly when there are too many choices, and we shy away from using more energy than our evolutionary programming allows us. Our technical prowess is once again offering us a solution to our default preferences, for better or worse.

The Confidence Transfer, then, isn’t a story about artificial intelligence. It’s a story about deference, expertise, and what we do with the deeply human discomfort of having to think and decide.

This Is Not New. That Is the Point.

The most important thing to understand about the Confidence Transfer is that the underlying psychology was documented long before generative AI existed, which tells us the behavior is human, not technological. The machines changed. The tendency didn’t.

In 1997, Raja Parasuraman and Victor Riley published what remains a foundational paper in the journal Human Factors, titled “Humans and Automation: Use, Misuse, Disuse, Abuse.” Studying how human operators interacted with automated systems in aviation and other high-stakes settings, they named a pattern they called misuse, defined as overreliance on automation that produces failures of monitoring and decision bias. I explored this dynamic in The Readiness Illusion (Issue 265) and in Season 2, Episode 26 of The Human Factor Podcast.

Parasuraman and Riley documented that when people are given an automated aid, they begin to monitor less, question less, and defer more, and that this complacency grows precisely when the automation has been reliable in the past. The more the system earns trust, the less the human scrutinizes it, which means the human’s attention fades exactly when a rare error would be most catastrophic. This body of work, extended by Parasuraman and Dietrich Manzey in a 2010 review on complacency and automation bias, established something that should anchor every conversation about AI in organizations today. The human tendency to over-defer to machines is not a reaction to how good the machines are. It’s a feature of how human attention and accountability work under conditions of automation.

What makes the current moment different is not the psychology. It’s the scope. Aviation automation governed a bounded set of technical functions. Generative AI now offers an opinion on almost anything, in fluent and confident language, to almost everyone in the organization at once. The narrow channel through which the Confidence Transfer once operated has become the entire river.

The Finding That Should Unsettle Every Expert

If the Parasuraman tradition gives us the historical foundation, a 2019 study provides us with the version of the problem that organizations are living right now, and it contains a finding that should unsettle anyone who has spent years developing expertise.

Jennifer Logg, then at Harvard, with Julia Minson and Don Moore at Berkeley, published “Algorithm Appreciation: People Prefer Algorithmic to Human Judgment” in Organizational Behavior and Human Decision Processes. Across six experiments, they found that people adhered more to identical advice when they believed it came from an algorithm than when they believed it came from a person. This directly challenged the older assumption of algorithm aversion, the idea that people instinctively distrust machine recommendations. Logg’s work showed the opposite is often true. When the advice is labeled algorithmic, people weigh it more heavily, defer to it more readily, and discount their own view more willingly. The label alone moves the needle.

But the finding that matters most for the human factor is buried in the later experiments, and it’s genuinely paradoxical. Algorithm appreciation waned among experts. Experienced professionals, the people who make forecasts and judgments for a living, relied less on algorithmic advice than novices did. That sounds reassuring until you read the consequence the researchers documented: this expert skepticism, in their study, hurt the experts’ accuracy in the specific tasks tested. The very experience that makes someone valuable also makes them resistant to outside input, and there are cases where that resistance is costly. This is the genuine complexity at the center of the Confidence Transfer, and I am not going to flatten it for the sake of a cleaner argument. I explored this paradox of expertise in The Confidence Calibration Problem (Issue 247) and in more depth in Season 2, Episode 14 of The Human Factor Podcast, The Identity Crisis of Expertise, where the fusion of professional identity and subject matter knowledge creates exactly the kind of resistance Logg’s research documented.

The honest position isn’t that human judgment is always right and machines should defer to us. The honest position is that both over-deference and under-deference are real failure modes, that they coexist, and that the organizational challenge is calibration rather than allegiance to either side.

This is where most commentary on AI and decision-making goes wrong. It picks a team. Either AI is a threat to human wisdom, or human bias is a problem AI will solve. The research supports neither side. It supports the uncomfortable middle, where the right amount of trust in the machine is a moving target that depends on the task, the stakes, the quality of the model, and the actual track record of the human in that specific domain. The organizations that will navigate this well are not the ones that pick a side. They are the ones that build the capacity to tell the difference, case by case, which is far harder than either blanket trust or blanket skepticism.

What the Transfer Costs Over Time

The immediate risk of the Confidence Transfer is the wrong decision in a single meeting. The deeper risk is what happens to an organization’s expertise when deference to machines becomes habitual, and here the most recent research is genuinely sobering and somewhat scary.

A 2025 study by researchers at Microsoft and Carnegie Mellon University, published at the CHI Conference on Human Factors in Computing Systems, surveyed 319 knowledge workers about critical thinking in their AI-assisted work. The pattern they found is a near-perfect description of the Confidence Transfer in motion. The more confidence a worker had in the AI, the less critical thinking they reported engaging in. Crucially, the researchers observed that knowledge workers using these tools shifted their cognitive effort away from the substantive work of analysis and toward the thinner work of verifying and editing what the AI produced. The thinking didn’t disappear, but it changed character, from generating and evaluating ideas to checking a machine’s output, a shift the researchers framed as a move from deep engagement to surface-level oversight. When trust in the tool rises, the human’s own analytical effort falls.

A neuroscientific study from MIT researchers in 2025 sharpened the concern further. Using EEG to monitor brain activity in college students completing writing tasks, the researchers found that those who used an LLM assistant showed lower brain connectivity, diminished sense of ownership over their work, and reduced engagement with their own ideas compared to participants who worked unaided. The LLM lowered cognitive friction and improved short-term efficiency, but it came at a measurable cost to learning and to the depth of the person’s involvement with the task. Let that all sink in for a few moments.

The finding I want to be careful with here is that this is early research on a specific task, and the long-term effects on skilled professionals in real workflows are not yet established. But read alongside the Microsoft survey and decades of automation research, it points in a consistent and concerning direction. The capability we offload, we slowly stop developing. The judgment we stop exercising, we slowly stop having. Abilities are like muscles; atrophy occurs when usage is limited or completely halts. The implications extend beyond individual organizations. A generation of professionals who have never needed to exercise the full weight of their judgment will inherit decisions that require exactly that.

This is the mechanism that makes the Confidence Transfer a compounding problem rather than a static one. An organization that defers to the machine in one decision has lost one decision. An organization whose people have spent three years deferring to the machine has something worse on its hands. It has a workforce that no longer practices the judgment the organization will desperately need the day the machine is confidently, catastrophically wrong. Expertise is not a possession. It is a practice, sustained only by use, and the Confidence Transfer quietly removes the occasions for that use. We have built elaborate systems to capture what our experts know. We have built almost nothing to ensure they keep knowing it.

In Organizational Memory Loss (Issue 258), I examined why organizations forget what they learn. In Decision Theater (Issue 254), I described how organizations mistake the appearance of decision-making for the practice of it. In Structural Silence (Issue 256), I explored why organizational culture trains people not to speak. Each of those dynamics is amplified by the Confidence Transfer, because deferring to the machine replaces the friction through which learning, genuine decision-making, and dissent occur. We are therefore already operating from a deficit. Our deference to automated decision-making compounds rather than resolves the organizational vulnerabilities we carry forward.

The Future We Are Walking Toward

It’s worth looking down the road, because the trajectory of the Confidence Transfer raises challenges that organizations (and humans in general) are not yet structured to handle.

The first is what we might call the calibration crisis. As AI systems become more capable and more frequently correct, the rational basis for trusting them grows, which means the deference becomes harder to argue against in any individual case. Every time the machine is right, the cost of having questioned it looks like wasted effort, and the cost of not questioning it stays invisible until the rare failure. Organizations will face mounting pressure to defer more, justified by an accumulating track record, even as that very deference erodes the human capacity that serves as the last line of defense when the system fails. The better the machine gets, the more dangerous unexamined trust becomes, because the failures grow rarer, stranger, and harder for an out-of-practice workforce to catch.

The second challenge is the erosion of what I will call productive disagreement. The analyst I described at the opening was a source of friction in that meeting, and friction of that kind is not a flaw in organizational decision-making. It’s the organizational immune system. The dissenting expert, the colleague who says wait, the person who has seen this go wrong before, these are the mechanisms by which organizations avoid their worst decisions. The Confidence Transfer threatens to route around all of it, because a confident machine answer ends deliberation in a way a human opinion rarely does. We are already uncomfortable with confrontation; we seek consensus, but often we don’t want to make the effort to gain consensus, and we hold biases that blind us to alternatives demonstrated by others. So when we have already concluded that our best modes of working require more effort than we are willing to invest, deferring to automation seems easy. Any fault that results is not on the humans in the room; it is attributed to the model.

So when the model speaks, the conversation that would have surfaced the dissent never happens, and many are more than happy it didn’t. The risk isn’t just worse individual decisions. It’s the slow atrophy of the organization’s entire capacity to disagree productively, to hold competing views in tension long enough to find the better answer.

The third challenge is accountability, and it is the one leaders should be thinking about now. When a human expert makes a call and is wrong, the organization learns something about that person’s judgment, and that person carries the weight of the outcome in a way that sharpens their future thinking. When the machine makes the call and is wrong, who learns? The research on automation bias found that people are often willing to defer to automated systems partly because it diffuses personal accountability.

The Confidence Transfer is, then, not only a transfer of trust. It is a transfer of responsibility, away from people who can be held to account and toward a system that cannot, and an organization that loses the thread of who is actually accountable for its decisions has lost something it will not easily notice is gone until it needs it.

What Leaders Owe This Moment

I am not going to offer a prescription for the right level of trust in AI, because the entire argument of this issue is that no fixed level exists, and anyone selling you one is selling you a simplification that will cost you. What the Confidence Transfer asks of leaders is harder than a policy. It asks for the deliberate preservation of human judgment as a living practice, even when deferring to the machine would be faster, cheaper, and defensible in the moment.

In practice, that means treating the question of when to trust the machine as a decision in its own right, rather than a default that operates invisibly. It means building the kind of culture where a colleague can say I think the model is wrong here and is met with genuine inquiry rather than the subtle social cost the analyst paid. It means noticing when the room goes quiet after the machine speaks and treating that silence as a signal to slow down rather than a sign of resolution. And it means protecting the conditions under which expertise is exercised and therefore sustained, because a workforce that has outsourced its judgment for a decade will not be able to reclaim it in the crisis that finally requires it.

The deepest point I am seeking to express is this. The value of human expertise was never only in being right more often than a machine. Sometimes the machine will be right more often. The value is in the practice of judgment itself, the friction, the accountability, the productive disagreement, the hard-won pattern recognition that makes a person worth listening to even when, especially when, the easier path is to let the confident answer settle the matter. An organization can transfer its confidence to machines and gain real efficiency in the bargain. What it must not do is make that transfer by default, without noticing, until the day it reaches for human judgment and finds that the practice has quietly gone out of the building. The question isn’t whether to trust the machine. The question is whether your organization is still deciding that question on purpose, or whether it has already stopped deciding it at all. I fear for some, it may be the latter.

Related Reading

The themes explored in this article connect to a wide body of work across the Ideas and Innovations newsletter and The Human Factor Podcast. The following pieces offer the most direct connections to the dynamics examined here.

The Confidence Calibration Problem (Issue 247) examines why self-assessment fails us at the worst possible moments, with direct relevance to the expert skepticism dynamic documented by Logg, Minson, and Moore in this article. The analyst in the opening was living the Dunning-Kruger paradox that issue explored, amplified by algorithmic authority.

When Being Right Isn’t Enough (Issue 248) explores why human expertise, even when it produces the correct answer, often fails to influence the decision. The opening anecdote in this article is a near-perfect illustration: the analyst was right, and it did not matter. That issue examined the dynamics in human-to-human influence. The Confidence Transfer shows what happens when the prevailing voice is a machine.

The Advice We Never Take (Issue 246) examines why leaders seek counsel they systematically ignore, the flip side of the Confidence Transfer. Where that issue documented the dismissal of human counsel, this one documents the uncritical acceptance of machine counsel, two expressions of the same underlying difficulty with genuine deliberation.

The Algorithmic Mirror (Issue 253) examines what AI reveals about how we actually think and decide. Where that issue treated AI as a diagnostic lens into human cognition, the Confidence Transfer is the behavioral consequence: once the mirror reveals our patterns, we defer rather than reckon with what we see.

The Busyness Trap (Issue 250) explores why we wear exhaustion as a status symbol. The cognitive depletion that issue documented is precisely the condition under which the Confidence Transfer accelerates, because a depleted workforce experiences deferral to AI as relief rather than abdication.

De-Risking Too Much Choice examines the decision paralysis that arises from too many options, a dynamic directly referenced in this article. The Confidence Transfer offers a seductive resolution to choice overload: let the machine decide.

The Human Factor Podcast, Season 2, Episode 14: The Identity Crisis of Expertise explores what happens when professional identity fuses with subject matter knowledge and that identity is then threatened. The Confidence Transfer threatens expertise at its most foundational level, and this episode examines the psychological mechanisms at work when that threat materializes.

The Human Factor Podcast, Season 1, Episode 3: Why Smart People Make Bad Decisions examines the psychology of bias in leadership, the underlying cognitive architecture that makes the Confidence Transfer possible. The biases that lead experienced leaders to poor judgment do not disappear when AI enters the room; they find new expression.

Sources

1. Raja Parasuraman and Victor Riley, Humans and Automation: Use, Misuse, Disuse, Abuse, Human Factors 39, no. 2 (1997): 230-253

2. Raja Parasuraman and Dietrich Manzey, Complacency and Bias in Human Use of Automation: An Attentional Integration, Human Factors 52, no. 3 (2010): 381-410

3. Jennifer M. Logg, Julia A. Minson, and Don A. Moore, Algorithm Appreciation: People Prefer Algorithmic to Human Judgment, Organizational Behavior and Human Decision Processes 151 (2019): 90-103

4. Hao-Ping (Hank) Lee et al., The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers, Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (Microsoft Research and Carnegie Mellon University)

5. Nataliya Kosmyna et al., Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task, MIT Media Lab, arXiv preprint 2506.08872 (2025)

Beyond Demographics: Why Data Without Craft Fails and Craft Without Data Guesses

Season 2 | Special Guests: Deborah Patton and Justin Thorp

You can segment an audience into dozens of behavioral cohorts and still land with the emotional weight of a form letter. You can write something genuinely moving and send it to entirely the wrong people at the wrong moment. The first is what happens when you have data without craft. The second is what happens when you have craft without data. And most organizations, the research suggests, are operating with one or the other, but never both.

In this episode of the Human Factor Podcast, Kevin Novak is joined by two people who embody the two halves of effective communication and who, in most organizations, sit in separate departments that rarely speak. Deborah Patton, COO at The Robin Report and a co-author of The Truth About Transformation, with decades in publishing and editorial strategy. Justin Thorp is the director of email marketing and audience strategy at Oracle, where he runs communication at enterprise scale. Kevin frames the research, Deborah speaks for the craft, and Justin speaks for the data, and the three of them work through where those capabilities collide and where they finally combine.

The conversation draws on Daphna Oyserman’s identity-based motivation research at USC, Paul Zak’s neuroscience of trust, and the behavioral science of retention to explain three psychological drivers that determine whether a communication connects or simply gets processed and ignored. It maps the four mindsets that shape engagement: the identity seeker, the knowledge acquirer, the network builder, and the passive consumer, and it confronts the question every communicator is now living with, which is where AI belongs in the work, and what it cannot replace. The honest answer the conversation arrives at is that effective communication is not a data problem or a writing problem. It is an integration problem.

Watch or listen to the full episode wherever you get your podcasts.

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Transformation Psychology Series

The Truth About Transformation (Book)

Issue 270, June 25, 2026

A manager at a client organization told me about a message she received from her director on the morning after her father’s funeral. It was warm, specific, and unexpectedly moving. It acknowledged her loss, named the particular strain of returning to work too soon, and told her to take whatever time she needed without guilt. She read it twice. And then, in the way that the mind sometimes betrays us, she noticed something in the cadence of the third sentence, a smoothness, a balance, a certain rounded perfection that did not match how her director actually spoke. She could not prove it. But the suspicion arrived and would not leave: this message, the one that had just comforted her, had probably been written by a machine. She told me that the comfort did not just fade. It inverted. The same words that had made her feel seen now made her feel like she was processed. And she could not decide which was worse, that her director hadn’t written it, or that she would now never be sure.

This is the dynamic I want to dive into because it is becoming one of the most quietly consequential developments in how human beings relate to one another inside organizations (and perhaps even personally in day-to-day life), and almost no one is talking about it openly and honestly or taking a critical thinking moment to consider that what they may have just received was written by something other than a human.

We have entered an era in which everyone can delegate the language of care to AI. In an organizational context, that means our co-workers, our immediate supervisors, and yes, the leaders of an organization. The recognition note, the performance feedback, the condolence message, the difficult conversation prepared in advance, all of it can now be drafted, in seconds, by a system that produces warmth on demand. Either via a chat interface, an agent, or via the ever-growing use of an “AI-Twin”. The question is not whether the output is good. The unsettling research finding is that the output is often better than what we would have written ourselves, more thoughtful, clearer, warmer, and seemingly more personal. The question, however, is what happens to the recipient, and to the relationship when the care was real but the author was not. Let me let that land with you for a minute because, as I mentioned, I don’t think it is something we have yet pondered.

I am calling this The Empathy Outsource, and the human costs to this new development and behavior operate in a place that no productivity metric will ever capture: emotions, thoughts, self-conceptions, and perceptions of the humans who are in receipt.

The Newsletter’s Continuing Thread

A note for readers who have followed the last several issues of this newsletter. We have spent recent weeks inside the human dynamics that AI is surfacing in organizations, from the shadow adoption examined in The Permission Paradox to the cognitive depletion driving people to these tools in the first place. This issue continues that arc, and I want to restate what I said a few weeks ago, because it matters even more here. This is not an AI newsletter, and Ideas and Innovations has not become one. The subject has always been the human factor. What AI does is hold up a mirror to behaviors and needs that were present all along, as we now have case study after case study and situation after situation bringing elements of our very humanness forward that we likely have been dismissing or overlooking for some time.

The Empathy Outsource is one of the clearest examples because the thing under examination is not artificial intelligence at all. It is the oldest currency in human relationships, the sense that another person took the time to care, to write a note, send an email, or give a card, and what happens to that currency when its source becomes uncertain.

We as humans know the people around us, whether at work or in our personal lives. We learn how they communicate: the words they use, the information they bring forward, and even the tone, facial expressions, and body movements that encompass their communications and interactions. This knowing is perhaps one of the best “tells” we have to assess whether what we have received is written by someone we know or by a machine. There is a counterpoint worth acknowledging here. Most of us do not write the way we speak, and I have heard this observation about my own communication, where my writing carries more formality and complexity than my day-to-day speech. That gap between spoken and written voice adds another layer to the challenge of discerning whether what we have received was authored by a person or by a machine, and it is a layer that AI’s increasingly fluent output is making harder to navigate by the day.

The Research Is Strikingly Consistent, and Strikingly Counterintuitive

The most important thing to understand about AI-generated empathy is that it works, right up until the moment the recipient knows where it came from (if they are even able to determine where it came from). This isn’t speculation. It is one of the most consistent findings in the emerging research on human-AI interaction that is showing how easily manipulated we can be, particularly in situations where we are vulnerable.

A study published in the Proceedings of the National Academy of Sciences in 2024 examined whether AI could perform the deeply human function of making someone feel heard. The researchers found that AI-generated messages actually made recipients feel more heard than human-generated messages did, and that AI was more effective at detecting the emotions in what people shared. Then came the reversal. When recipients learned that a message had come from AI rather than a human, the feeling of being heard dropped sharply. The exact same capacity that made the message effective became the thing that hollowed it out, the instant its origin was known.

This pattern has now been confirmed across a remarkable body of research work. In a study published in Nature Human Behaviour in 2025, a research team, including Amit Goldenberg of Harvard Business School, conducted nine studies involving more than 6,000 participants across multiple countries to test what they came to call the human empathy premium. Their finding was more than blunt. People consistently rate identical messages as less empathic the moment they believe AI produced them. The researchers named the inverse effect the AI Penalty: label a message as AI-generated, accurately or not, and the recipient downgrades its value immediately. The content of the message doesn’t change. The words are identical. Only the perceived author changes, and that alone is enough to collapse the message’s emotional worth.

The studies found something even more specific. When AI was tuned to deliver cognitive empathy, the simple acknowledgment of someone’s situation, people rated it about the same whether they thought it came from a human or a machine. A really interesting finding that shows that often words, regardless of where they came from, do indeed influence us.

But when the message was tuned for affective empathy, the sense that the sender genuinely shares your feelings, people strongly preferred the human source. We will accept a machine that understands our circumstances. We resist, at a deep level, the idea that a machine could care. Perhaps revealing some deeply programmed evolutionary filter that recognizes real human interaction that can, in many ways, run counter to how we humanize material items and our pets.

A 2025 study published in Frontiers in Psychology sharpened the stakes further by testing emotionally charged scenarios, the birth of a child and the diagnosis of a terminal illness. The researchers manipulated who recipients believed had authored a message, ranging from a close friend to ChatGPT. The result was that attribution to an artificial source produced the steepest declines in perceived authenticity and moral respect, and that anthropomorphic cues, the design tricks that make AI feel more human, temporarily elevated the evaluation but collapsed entirely upon disclosure, particularly in the highest-stakes contexts. The more the moment mattered, the more the artificial source cost.

Why the Brain Reacts This Way

It would be easy to dismiss all of this as irrational prejudice, a bias against machines that will fade as the technology improves and people grow accustomed to it. I have already shared that many across the Generation Z population are interacting with AI on a personal level daily, and who consider the AI a friend and counselor.

But, I don’t think the research to date supports that weirdly comfortable conclusion, and a theoretical paper published in Trends in Cognitive Sciences in early 2026 explains why the reaction may be neither irrational nor temporary.

The argument is that empathy, in humans, is not primarily about the words. It is a predictive signal. I have dove into this topic time and time again via this newsletter and in my presentations to help others understand communication theory and technological determinism (and its various interpretations).

When someone extends genuine empathy to us, our minds register far more than the content of the message. We register evidence about that person’s future commitment to us, their likely cooperation, and their place in our social world. An empathic message from a colleague updates what the researcher calls our mental social map. It moves that person closer, strengthens the connection, and raises our prediction that they will show up for us again. This is what empathy is for, evolutionarily. It is a costly signal of relational investment, and we are exquisitely tuned to read it.

Now consider what happens when the message comes from AI or is suspected to. The warmth is still there in the words. But the signal underneath, the evidence of another person’s investment, is gone. A machine generated the care at near-zero cost, which means it tells us nothing about anyone’s commitment to us. The social map doesn’t update because there is no relational information to update it with. This is why the suspicion alone is so corrosive and leads to many “OMG” moments when the recognition dawns. The manager who received the condolence note did not need proof that her director used AI. The mere possibility severed the message from its function as a signal of investment, and once that link is cut, the words become just words, however beautiful. The same paper notes the darker implication. If empathic language can now be produced at almost no cost, people can learn to deploy it to appear caring without actually committing anything, effectively hacking the appearance of social closeness. A very scary thought in a world of humans craving human collaboration and connection. Our deep sensitivity to AI-authored empathy may be the mind’s defense against exactly that exploitation.

The Workplace Is Already Living This

This is not a future problem. It’s very much here in our lives today. The outsourcing of relational language is already so widespread in organizations and the data on how employees respond should give every leader pause.

Major employers, including JP Morgan, Citi, and IBM, have introduced AI tools to assist with performance appraisals and feedback. The efficiency case is obvious. The trust case is far more troubling. A 2025 survey of UK employees found that only 28 percent would fully trust a manager if AI had contributed to their feedback, and 30 percent said that AI involvement would actively damage their trust in that manager. Nearly half, 49 percent, believed that AI-assisted recognition or praise simply lacks authenticity. The skepticism was highest among both the oldest and the youngest workers, which undercuts the common assumption that resistance to AI empathy is merely a generational holdover that will disappear with time. The youngest workers, the ones most fluent with these tools, were among the most resistant to receiving care through them. Which is furthered by related research showing that Gen-Z is uncomfortable using AI in a professional setting unless directed to do so. Ryan Vet and I dove into this interesting development in an episode of The Human Factor Podcast.

Researchers Anthony Coman of the University of Florida and Peter Cardon of the University of Southern California drew the line precisely in their 2025 work. Employees consider a manager’s use of AI writing assistance appropriate and professional for informational and routine communication, the meeting reminder, the scheduling note, the policy summary. But for relationship-oriented messages, the ones requiring empathy, praise, congratulations, motivation, or personal feedback, employees believe these are better handled with minimal technological intervention. That said, handled by a human, not by AI.

There is a boundary in the employee’s mind, and it falls exactly at the line between information and relationship. AI may carry the information. The relationship, people deeply insist, must be carried by a real person.

There is a genuine complication here that intellectual honesty requires me to name, because it cuts against the argument I am building. Some research points the other way. Work referenced by performance management platforms has found that AI-generated feedback can improve employee performance more than human feedback does, but, and this is the essential qualifier, only when the employee does not know the feedback was generated by AI. The moment it is disclosed, performance gains decline and trust in the feedback’s quality drops. This finding is real, and it deserves to be sat with rather than waved away, because it surfaces the most uncomfortable question in this entire domain. If AI-generated care produces better outcomes precisely when its origin is concealed, then the efficiency argument for the Empathy Outsource is also, inescapably, an argument for deception. The tool works best in the dark, keeping us humans in the dark on the source of what we receive. And a leadership practice that depends on the people it touches, never finding out how it works, is not a practice any leader should be comfortable building a culture on. These are mind-boggling considerations, particularly given how fast our adoption of AI is across the entire world of humans.

The Asymmetry Leaders Miss

The reason intelligent, well-meaning leaders walk into the Empathy Outsource is that the math looks compelling from where they sit. They are depleted, a condition we examined directly two weeks ago. They have forty people to recognize, a dozen difficult conversations pending, and a tool that produces warmer, more articulate, more consistent language than they could generate at the end of an exhausting week. From the sender’s side, the trade appears to be efficiency at no cost to quality. The output is often better. Where is the harm?

The harm lives entirely on the other side of the exchange, and it’s invisible to the sender. This is the asymmetry that the research makes completely unavoidable. The leader experiences AI-assisted empathy as a quality-neutral efficiency gain. The recipient, if they ever know or even suspect, experiences it as the discovery that their moment of being cared for was, at least in part, automated. The sender saves ten minutes. The recipient potentially loses something far harder to restore: the belief that when this person reached out to them in a moment that mattered, it was actually this person reaching out. Those two experiences are not symmetric, and because the cost is borne by someone who isn’t in the room when the decision to use the tool is made, it’s almost never weighed. We have measured the time these tools save with great enthusiasm in both reality and hype. We have measured what they cost the person on the receiving end with almost nothing at all.

And there is a compounding effect that makes this more dangerous than a single disappointing message. Trust, once a suspicion like this takes hold, it doesn’t stay contained to one note. The manager who suspects one condolence message was automated will reread every warm message that the director ever sent her through the same lens. Was the birthday recognition real? The praise after the difficult project? The reassurance during the reorganization? Let that sit for a few moments.

The Empathy Outsource doesn’t just devalue the message that triggers the suspicion. It retroactively contaminates the entire history of care between two people, and it poisons the well for everything that comes after. This is the precise opposite of what relational language is supposed to accomplish.

What This Asks of Leaders

I am not going to argue that leaders should never let AI near a sentence, because that position doesn’t survive contact with how work is actually happening in today’s terms, and it is not what the research recommends. The boundary that employees themselves draw is more useful and more honest than a blanket prohibition. Use the tools for the informational and the routine, where they genuinely help and where no one expects a piece of your heart to be in the scheduling reminder. But the relational core, the moments that exist precisely to signal that one human being is invested in another, must remain unambiguously human. Not because human writing is always better, the research is clear that it often isn’t, but because in these specific moments the authorship is the message. The point of a condolence note was never the elegance of its prose. It was the evidence it carried that you stopped, you thought about this person, and you spent something of yourself on them. AI can replicate the prose. It cannot replicate the spending, and the recipient’s mind is built to know the difference.

This leads somewhere genuinely difficult, and I want to end in the difficulty rather than escape it. The deepest problem with the Empathy Outsource isn’t really about disclosure or detection or which messages are appropriate to automate. It’s about what we are practicing when we delegate care. Recognition, feedback, and hard conversations are not just outputs to be delivered. They are the actual substance of relational leadership, the work through which a leader becomes the kind of person whose care means something.

A leader who routes that work through a machine may produce better messages in the short term while slowly losing the capacity and the standing to mean them. The muscle that isn’t used atrophies. We have spent this issue examining what the Empathy Outsource does to the people who receive automated care. The harder question, the one worth carrying out of this newsletter and into your week, is what it does to the leader who sends it?

The question then is not whether AI can write a warmer note than you can. It almost certainly can. The question is whether you still want to be the kind of leader whose people never have to wonder.

Related Reading

The Empathy Outsource: What Happens When the Care Was Real but the Author Was Not (Issue 270)

Directly Referenced in This Issue

The Permission Paradox: Why Your People Are Hiding the Very Things You Say You Want

Directly referenced for its examination of shadow adoption, where employees adopt AI tools privately because the organization has neither permitted nor prohibited them. The Empathy Outsource extends this dynamic into emotional territory, where the tools being hidden are not productivity aids but expressions of care.

The Readiness Illusion: Why the AI Agent Era’s Loudest Claims Outrun the Evidence

The preceding issue in the AI adoption arc, which argues that capability and readiness are not the same thing. The Empathy Outsource builds on this distinction by asking what happens when AI becomes capable of producing language that reads as empathetic but the organization is not ready to govern its use in emotionally significant communication.

The Truth About Transformation: Revised and Expanded

Kevin Novak’s book, referenced for its central argument that transformation succeeds or fails at the human level. The Empathy Outsource asks what happens to the human level when the language of care is delegated to a machine, and the people receiving it cannot tell the difference.

Ideas and Innovations Newsletter Issues

The Algorithmic Mirror: What AI Reveals About How We Actually Think and Decide

Explores how AI functions as a diagnostic lens for organizational behavior. The Empathy Outsource applies this same framing to emotional expression, arguing that AI-generated empathy reveals how selectively and inconsistently we have been offering care in its absence.

Artificial Understanding: The Intelligence We Built and the Comprehension We Didn’t

Examines the gap between AI capability and genuine human comprehension. The Empathy Outsource extends this into the emotional domain, where the gap between producing empathetic language and genuinely comprehending another person’s experience is the central tension.

The Busyness Trap: Why We Wear Exhaustion as a Status Symbol

The cognitive depletion narrative in The Empathy Outsource traces directly to this earlier issue. When professionals are too depleted to write a thoughtful note, send a considered email, or compose a genuine expression of care, AI becomes the pressure valve for emotional labor that the organization never acknowledged as labor at all.

The Mental Overload of Modern Leadership: Why Today’s Executives Are Burning Out Differently

The cognitive architecture research cited in The Empathy Outsource connects to this earlier examination of how leadership demands have outpaced human processing capacity. Emotional expression is one of the first capacities to be shed under cognitive load, which explains why leaders are among the earliest adopters of AI-generated empathy.

When Change Champions Burn Out: The Hidden Cost of Driving Change and Transformation

Explores the cognitive burden on individuals who carry transformation forward, the same population most likely to outsource emotional communication when their capacity for genuine care expression is exhausted by the demands of their role.

The Relationship Decay Rate: Why Professional Connections Atrophy Without Intention

Examines how professional relationships deteriorate without sustained investment. The Empathy Outsource raises the question of whether AI-generated care counts as genuine investment or whether it accelerates the very decay it appears to address by replacing intentional human attention with scalable but hollow language.

The Loyalty Trap: When Commitment Becomes a Cage

Examines how organizational loyalty can suppress authentic expression. The Empathy Outsource explores a parallel dynamic where professional norms around emotional communication create pressure to express care in forms that are expected rather than felt, making AI delegation a natural extension of performative workplace empathy.

The Authenticity Paradox in Transformation Leadership

Explores the tension between strategic authenticity and genuine vulnerability in leadership. The Empathy Outsource heightens this paradox by introducing AI as a third variable: when leaders use AI to produce empathetic language, the care may be strategically authentic but experientially indistinguishable from the real thing.

Structural Silence: Why Organizations Train People Not to Speak

Examines how organizations systematically teach employees when not to speak. The Empathy Outsource describes a related pattern where organizations have never taught employees that emotional expression is a professional competency, creating the void that AI adoption fills.

Beyond Demographics: The Psychology of Communication That Actually Drives Engagement

Explores the psychological mechanisms behind communication that genuinely connects with people. The Empathy Outsource interrogates what happens when those mechanisms are replicated by AI, and the recipient responds to the communication rather than the communicator.

The Generational Fault Line: Why Your Change Initiative Lands Five Different Ways

Examines how generational psychology shapes responses to organizational change. The Empathy Outsource cites research showing that Gen Z’s comfort with AI-generated emotional content, including romantic relationships with AI, represents a generational shift in how authenticity itself is defined.

Survival Mode Leadership: The Hidden Costs of Managing by Fear

Explores how fear-driven environments push people into survival mode. The Empathy Outsource argues that when emotional communication becomes another obligation in a fear-based environment, outsourcing it to AI is not laziness but a rational allocation of depleted cognitive resources.

Measuring What Matters Series

Measuring Trust as a Behavioral Asset: How to Operationalize the Most Important Intangible in Organizations (Issue 266)

The Empathy Outsource identifies trust as the variable most at risk when empathetic communication is delegated to AI. This Measuring What Matters article provides the framework for operationalizing trust measurement, including the behavioral markers that would reveal whether AI-mediated care is building or eroding relational capital.

The Identity Problem in Measurement

Examines how professional identity shapes what organizations choose to measure. The Empathy Outsource reveals a measurement blind spot: organizations track communication volume and response time, but have no mechanism for measuring whether the care expressed in those communications was genuinely authored by a human being.

Transformation Psychology

Why AI Adoption Resistance in the Workplace Is a Leadership Problem, Not a Technology Problem

The foundational transformation psychology article argues that AI resistance is rooted in identity and trust rather than technology. The Empathy Outsource extends this by examining what happens when AI adoption is not resisted but embraced for the most psychologically intimate dimension of professional life: emotional expression.

The Communication Paradox in Transformation

Explores why more communication often produces less understanding. The Empathy Outsource reveals a new dimension of this paradox: AI enables more empathetic language at a greater scale, but if the comprehension behind that language is absent, the volume of care may increase while the depth of connection decreases.

Emotional Exhaustion in Change Management: Warning Signs and Solutions

Examines the warning signs and consequences of emotional exhaustion during organizational change. The Empathy Outsource argues that the outsourcing of emotional communication to AI is itself a warning sign of exhaustion that most organizations are not equipped to recognize.

The Partnership Posture: A Working Frame for Humans and AI

Offers a constructive framework for the human and AI relationship. The Empathy Outsource raises the question of what the partnership posture looks like when the domain is not productivity or analysis but emotional care, and whether that boundary requires a different kind of governance entirely.

Human Factor Podcast Episodes

Season 2, Episode 021: When Generations Collide: The Generational Fault Lines of Organizational Transformation

Features Ryan Vet and explores generational dynamics in transformation. Directly relevant to The Empathy Outsource’s discussion of Gen Z’s comfort with AI-generated emotional content and the research showing that nearly three-quarters of Gen Z have engaged in some form of relationship with AI, representing a fundamental shift in how authenticity is defined across generations.

Season 2, Episode 015: The Contagion Effect: How Emotions Spread Through Organizations During Change and Transformation

Explores how emotions spread through organizations, drawing on research from Hatfield, Cacioppo, and Rapson as well as Sigal Barsade’s foundational work. The Empathy Outsource raises the question of what happens to emotional contagion when the original emotional signal was generated by AI rather than felt by a human being.

Season 2, Episode 017: The Algorithmic Mirror: What AI Reveals About How We Actually Think, Decide, and Deny

The companion podcast episode to the newsletter article. Explores AI as a diagnostic lens for organizational behavior, the same framing that informs The Empathy Outsource’s argument that AI-generated empathy reveals more about our capacity constraints than it does about the technology.

Season 2, Episode 019: Structural Silence: Why Organizations Train People Not to Speak

Examines how organizations systematically train employees when and how not to speak. The Empathy Outsource extends this into the emotional register, where organizations have never taught employees that emotional expression is a professional competency, creating the conditions under which AI delegation becomes the default.

Season 1, Episode 006: The Communication Paradox: When More Words Create Less Understanding

Explores why comprehensive communication strategies often backfire and how cognitive overload prevents genuine comprehension. The Empathy Outsource describes a new form of the communication paradox where AI enables more empathetic words while potentially producing less genuine understanding.

Season 1, Episode 007: The Vulnerability Advantage: Why Admitting Weakness Makes Organizations Stronger

Explores how measured vulnerability strengthens organizational trust. The Empathy Outsource raises the question of whether AI-generated empathy represents the opposite of vulnerability: a form of emotional expression that carries none of the personal risk that makes genuine care meaningful.

Season 1, Episode 009: Transformation Fatigue: When Your Organization Can’t Absorb More Change

The cognitive depletion argument in The Empathy Outsource builds on the transformation fatigue research explored here, where exhaustion becomes the precondition for delegating not just tasks but emotional expression to AI.

Sources

1. Yidan Yin, Nan Jia, and Cheryl J. Wakslak, AI can help people feel heard, but an AI label diminishes this impact, Proceedings of the National Academy of Sciences, 2024

2. Matan Rubin, Amit Goldenberg et al., Comparing the Value of Perceived Human Versus AI-Generated Empathy, Nature Human Behaviour, November 2025 (nine studies, 6,000+ participants)

3. Frontiers in Psychology, The illusion of empathy: evaluating AI-generated outputs in moments that matter, 2025

4. Empathy as a predictive signal: why we devalue AI empathy, Trends in Cognitive Sciences, 2026

5. UK employee trust survey reported by Raconteur, 2025

6. Anthony Coman (University of Florida) and Peter Cardon (University of Southern California), research on supervisor use of AI-assisted writing, 2025, reported by HR Dive

7. AI-disclosed performance feedback research referenced by Lattice, 2021

The Trust Paradox: Transforming the Institutions Built to Stay the Same

Season 2 | Special Guest: Elizabeth Maza Stewart

Most organizations send signals of change to prove they are moving forward. A small category of institutions cannot. Standards bodies, credentialing organizations, and professional associations derive their authority from perceived stability, and for them, every signal of change can be misread as a signal of instability by the very stakeholders who depend on them. Change your certification standards, and you risk telling every professional you already certified that their credential is now suspect. Update your brand, and you risk telling the public that the institution they trust is no longer the same institution.

In this episode of the Human Factor Podcast, Kevin Novak welcomes back Elizabeth Maza Stewart, senior advisor at 2040 Digital and one of the show’s most frequent voices, for a conversation about the paradox she has spent more than thirty years navigating from the inside. Across leadership roles at the American Red Cross, the American Institute of Architects, and as Chief Operating Officer of the Certified Financial Planner Board of Standards, Elizabeth has led transformation inside three of the most trust dependent institutions in the country. She brings a rare combination of legal training, economic and quantitative analysis, and marketing practice to a problem that standard change management playbooks do not address.

The conversation works through the dual audience problem, where the internal workforce needs honesty about what is changing while the external audience needs reassurance that what they depend on is not at risk. Elizabeth describes how the Red Cross rebuilt its brand promise around helping to save lives during the tainted blood crisis of the 1990s, how the CFP Board evolved its competency standards to include the human and counseling skills that clients actually need, and how the AIA reframed architecture around form following function and evidence-based design. Throughout, the discussion returns to the emotional labor of leading institutions where trust is the product, the cost of performing confidence while processing uncertainty, and the psychological contract that breaks when professionals are told the expertise that once qualified them is no longer sufficient.

This episode builds on the season’s exploration of identity threat, emotional contagion, structural silence, the broken psychological contract, and last week’s solo episode on transformation theater. It extends those threads into the institutional setting, where the stakes are higher because the trust at risk reaches beyond the organization to the profession and the public it serves.

Watch or listen to the full episode wherever you get your podcasts.

Resources

Learn more about the Human Factor Podcast

Transformation Psychology Series

The Truth About Transformation (Book)