Far too many organizations went in search of an “AI strategy,” rather than identifying problems that people actually care about. That’s probably why so many focused on using it to reduce headcount through poorly thought-out layoffs, a large portion of which have had to be reversed. They got the worst of all worlds: weakened operations and poor morale.

What’s important is that you go to where the energy is, not try to create or maintain it by yourself. That’s why we always recommend that organizations we advise to start with a Keystone Change, which represents a clear and tangible goal, involves multiple stakeholders, and paves the way for bigger changes down the road.

For example, when Paul O’Neill set out to turn around Alcoa in the 1980s, he started by improving workplace safety, which also paved the way to improvements in operational excellence. At Experian, when CIO Barry Libenson set out to move his company to the cloud, he started with internal APIs. In both cases, the stakeholders who were won over in achieving the Keystone Change also played a part in bringing about the larger vision.

As Martin Heidegger explained long ago, technology is not a creation, but a way of revealing. It unlocks new possibilities that, when channeled in the right direction, can help us solve important problems. That’s what leaders need to focus on now: not on the wonders of AI, but on how they can use new tools to solve problems that matter to people.

In a healthy society with checks and balances, someone would have been held to account. There would have been litigation. A court would have mandated a cleanup. But in a command economy, power flows from a single source, with no one to hold it accountable. Those in power don’t have to admit their mistakes. Questions of truth become questions of loyalty.

We have a similar process underway in the US. When politicians and unelected bureaucrats seek to make scientific funding conform to their ideological preferences, they are not merely making budget allocations. They are seeking to control what is true and what is not. We’ve seen that enough times before to know how it ends.

America achieved technological dominance, in part, because of the scientists who fled Europe to escape similar populist tyranny. Now that process is reversing, with scientists leaving America for Europe. The advantages we built after World War II are quickly eroding, threatening our prosperity and security.

That’s why we don’t want politicians running science. It diminishes science and leaves us with only politics.

Now consider the prominent sociologist Doug McAdam’s paper on recruiting for Freedom Summer during the civil rights movement. He was able to analyze the applications of not only 720 volunteers, but also 239 others who withdrew and 55 who were rejected. He conducted 80 in-depth personal interviews and, because the applicants were asked to list their social contacts, McAdam was able to document the social ties among them.

That type of documentation simply doesn’t exist in Kotter’s work or the work of his imitators. To be fair, it is rare in business research as a whole. We seldom get access to internal data, much less insights from other stakeholders or anything akin to what researchers routinely gather in more rigorous fields, such as sociology, anthropology and psychology.

The ugly truth about change management is that the traditional change models simply don’t work. They aren’t based on any serious research and have shown themselves, over a period of decades, to fail consistently. Often, organizational change management units are used by consulting firms and vendors to cheerlead larger engagements.

That isn’t to say it’s some sort of con. In my experience, organizational change management practitioners are well-meaning and doing their best to make a positive impact. They are hired for engagements, make proposals, deliver on what they promised and leave their clients happy. They usually aren’t around to see what happens after the engagement ends.

Yet we do have a vast body evidence from other fields that we can apply. Strategies like starting with a majority, waiting for a trigger and designing dilemmas are rooted in that evidence. Transformation is too important to entrust to conventional wisdom when decades of research show us a better way.

His secret weapon was a theory he calls reflexivity. The basic idea is that expectations don’t form in a vacuum. They are shaped, in part, by our perceptions of what other people believe. The more widely an idea is accepted, the more likely we are to accept it ourselves and that, in turn, reinforces the collective zeitgeist.

What’s interesting about the reflexivity theory is that it doesn’t rely on the merits of the asset in question. You can believe in the transformative power of AI and still recognize that market fundamentals still apply. When we look back at past boom-bust cycles, the problem was never a lack of intelligence or a lack of capital, but a lack of discipline.

The problem is that cultures that celebrate exceptional intelligence eventually begin to mistake intelligence for infallibility. The truth is that smart people are often more easily fooled than most because they expect to see things that others miss. The greatest danger facing elite cultures isn't a lack of intelligence. It's the gradual loss of intellectual humility that success can produce.

Nobody, no matter how smart, can afford to believe everything they think.

It was just a mold secretion—unstable, unusable, with no way to produce it at scale. So it was largely ignored, buried in a medical journal for over a decade. Then a different team, led by Howard Florey and Ernst Chain, picked it up. Collaborating with American labs, they turned it into a viable treatment, and in 1945—more than 15 years after Fleming’s “Eureka moment”—penicillin became commercially available.

Simply announcing a breakthrough or devising a solution doesn’t immediately translate into impact. It takes time for people who understand new solutions to learn how to collaborate effectively with people who deeply understand the problems they can solve. That’s not a technology problem. It’s a human problem. And no amount of computer chips or software code will make it go significantly faster.

That’s why we need to take the claims of the Silicon Valley types with a grain of salt. Yes, artificial intelligence will likely be transformative, as will quantum computing, CRISPR, mRNA and probably other things as well. But there will be no shortage of wrong turns and blind alleys along the way.

Breakthroughs can happen in an instant, but genuine transformation still happens at human speed.

When you use AI, the learning corpus is the most important thing you have control over. If, for example, you ask AI to give you answers from the broader Internet, it will draw from everyone, no matter how smart or dumb. It will even learn from its own AI slop, leading to what experts call model collapse.

But if you control the material it works from, you can pick and choose the sources. So if you ask AI to write something for you, it will give you mostly slop. However, if you ask it to edit your own writing or summarize your own documents, it will do a much better job. It can also do a pretty good job transforming your thoughts from one form to another, such as creating a video script from a report that you wrote.

There are some things that a machine will never do. A machine will never strike out in a Little League game, have its heart broken, or see its child born. Those personal experiences, perspectives and insights are what you have to offer the world. They are also what AI can’t emulate without you.

In the age of AI, the best way to avoid sounding like a robot is to become more fully human.

Yet leadership isn’t about performing, it’s about raising the level of those around you and for that you need to create an environment where people feel safe enough to do their best work, share ideas even before they're fully formed, and to collaborate with colleagues in a productive way. Part of that is how you hire, manage and discipline. But the most important thing a leader can do is learn to be a great listener.

Fortunately, these skills can be learned. David McRaney’s "How Minds Change" offers an excellent introduction to techniques like Deep Canvassing, Street Epistemology and the Change Conversation Pyramid. Chris Voss’s book "Never Split The Difference" is another valuable resource, and many of his talks are available online for free.

Yet the most important factor is your desire to overcome years of training and education that rewarded you for speaking up first and delivering the “right” answers in the shortest possible time. In the real world, when you deal with tough, complicated issues, you rarely know what the “right” answer is. You only have the answer based on your best information.

How good that information is depends on how well you listen.

It felt apocalyptic. Every day on the way to work, I would look at the exchange kiosks showing the Ukrainian currency crashing in value. When I arrived at the office, there would be a series of excruciating decisions to make. We closed all of the new brands we launched since our IPO. At one point, I had to fire 150 people in a day, not because they had failed us, but because we, blinded by hubris and stupidity, had failed them.

The real tragedy was that if we had been more methodical, the cash from our IPO would likely have sustained us through the crisis. In fact, having cash when very few others did would have put us in a great position. But we were sure we were on to the next big thing. We felt a profound sense of urgency to beat everyone else to the punch and moved forward at breakneck speed.

I wish I could say I learned my lesson then, but I didn’t. The same pattern persisted. I would get excited by an idea, convince myself I was onto something important, and rush to move forward. What I learned over time is that we are incredibly poor judges of our own ideas. We need to take a Bayesian approach, moving forward steadily, iterating and testing as we go.

The truth is that every idea is wrong. Some are off by a little, and some are off by a lot, but they are all wrong. The key is not to come up with the “right” ideas, but to make them less wrong over time.

A paper by researchers at the University of Sheffield sheds some light on what happened. First, there was great disparity between firms in how effectively the technology was deployed. Second, while computers really did transform some tasks, those made up for a relatively small proportion of the overall economy, so the productivity effect was muted.

There’s probably something similar going on now. While artificial intelligence is rapidly transforming the world of bits, we still live largely in the world of atoms. Most of the things we spend money on, like houses, cars, food, and leisure time are, at best, minimally affected by information technology in general and artificial intelligence in particular.

That explains why, despite the emergence of impressive technologies such as the mobile web, big data, and cloud computing over the past twenty years, productivity growth has been mostly depressed. It also suggests that, much like earlier investment booms, this one will end in a bust.

That’s the problem with “new era” thinking. It’s not that things don’t change. They do. It’s just that everything rarely changes all at once. As I wrote in Mapping Innovation, innovation is never a single event. It’s a process of discovery, engineering and transformation—and the transformation part always takes much longer than anybody thinks it will.

Yet anybody who has ever been married or had children knows how difficult it can be to convince even a single person of something they don’t want to be convinced of. To set out to persuade hundreds—or even thousands—that they should adopt an idea that they are inherently hostile to is not only hubris, but incredibly foolish. Decades of evidence also show that changing attitudes, by itself, rarely changes behavior.

That also helps explain why so many change efforts fail. They start with tactics designed to create a specific effect, such as “build awareness” or “create a sense of urgency.” They roll out communication campaigns, design training programs, or host events like hackathons. Then they congratulate themselves and wonder why genuine change never happened.

That’s what the change consultants get wrong. You don’t “manage” change. You empower it by enabling those who believe in it to show it can work and then bringing in others who can bring in others still. The truth is that you don’t need a clever slogan to bring change about, you need a network. That’s how you create a movement that drives transformation.

Change doesn’t succeed through snappier slogans or even through better training, it’s when shared values align with shared purpose and sense of mission. Change succeeds when it works for people.