Most organizations think they have an AI readiness problem.
What they actually have is an organizational adoption problem. That distinction matters more than most leaders realize.
I've watched the same pattern repeat across every major technology shift. When adoption stalls, the instinct is to blame people. Employees aren't engaging. Teams are resistant. People just don't want to change.
Resistance is an easy conclusion. It's also frequently the wrong one.
Before concluding that people are the obstacle, look at the system surrounding them. Are organizational goals constantly shifting, leaving people no stable ground to adopt anything new? Are roles so overloaded that AI feels like one more demand on an already exhausted workforce? Is the underlying process so broken that layering AI on top simply automates the chaos? Are teams interpersonally fractured in ways no technology will fix? The GRPI model gives us a more useful diagnostic, and more often than not, it points away from people.
AI doesn't fix organizational dysfunction. It exposes it.
The second mistake organizations make is reaching for education as the first response. Launch an AI literacy program. Require prompt engineering training. Mandate completion before the end of the quarter.
This creates push.
And push, in an environment where people are already drowning in AI noise at home, on social media, and in every professional conversation, doesn't create adoption. It creates resistance. Organizations are dampening the very excitement they're trying to build by adding more pressure onto people who are already overwhelmed by it everywhere else.
What creates adoption is pull. And pull comes from proof.
I've watched organizations approach AI in three very different ways.
The first treated AI as an educational challenge. We built awareness, offered learning, and hoped adoption would follow. It didn't. The people who were already interested leaned in. Most everyone else continued working the way they always had.
The second treated AI as a targeted initiative. Adoption improved because it focused on a specific population with a clear need. But the impact remained limited because the learning never extended beyond that group.
The third took a fundamentally different approach. Instead of leading with education, we led with experimentation. People were encouraged to solve real business problems, share what they learned, and demonstrate value. Education came later, once demand already existed. The role of learning wasn't to create adoption. It was to accelerate and scale what people had already proven was valuable.
That experience fundamentally changed how I think about AI readiness.
The difference wasn't the technology. It wasn't the training. It was the sequence.
The organizations that get AI adoption right don't start with education. They start with a specific business problem. They identify AI as a specific response to that problem. They pilot it with a small group and measure what actually changes. When other teams see what's possible, and how achievable it was, they ask to be next.
That's the difference between forcing people onto the boat and making the boat worth boarding.
On measurement: the right metric depends on where you're deploying AI, but the logic is always the same. Every AI investment should connect to a measurable business outcome downstream. Completion rates measure compliance, not capability, not commitment, and certainly not business impact. If you're deploying AI in HR operations, look for a reduction in service-level agreements. If you're deploying it in training, look for a decrease in time-to-proficiency or an increase in on-the-job performance. If you can't define what success looks like before you select a tool, adoption will become the metric by default. And adoption, by itself, tells us very little about business value.
There is one more dimension worth naming.
AI holds extraordinary potential not just to automate human tasks, but to develop human capability. The strongest leaders I've worked with built judgment through repetition, feedback, and experience. The problem is that the situations where judgment matters most are also the situations where mistakes are most costly. High-stakes decisions rarely come with the luxury of practice.
AI changes that equation. When designed intentionally, it creates opportunities for high repetitions in high-stakes situations, before the real thing happens. Leaders can practice difficult conversations before having them with employees. Customer-facing teams can navigate complex scenarios before serving real customers. New managers can build confidence before their decisions carry real consequences.
AI doesn't replace experience. It accelerates it.
And through all of it, human judgment must remain at the center. AI is only as good as what goes into it. Whatever it produces is not gospel. Leaders, strategists, and teams must begin and end every AI loop with human discernment, challenging what the model returns, stress-testing assumptions, and thinking critically about how things could play out. No one knows the future. The best we can do is make better predictions. Even those require human beings willing to question them. That idea deserves more than a paragraph, and I'll return to it in a future piece.
So what should leaders do instead of launching another AI initiative?
Three things.
Start with a measurable business outcome, not a tool. Identify a specific problem with a specific metric attached to it before a single platform is selected.
Pilot around a real workflow, not a broad capability. Find a small group, deploy AI against a defined problem, and measure what changes. Keep it contained until you have proof.
Scale only after you've created pull. Let results do the talking. When other teams see what's possible and ask to be next, that's the signal to expand. Not before.
Technology can remove routine work. That alone creates value. But the greater opportunity is what happens after the routine work is gone, people freed to do more meaningful work while building the judgment, confidence, and adaptability organizations will always need.