May 22, 2026

AI for the Over 40 – Week 25: 3 Shifts for AI Leadership Transformation

My daughter convinced me to try a Zumba class to start the new year. I was mildly nervous about being the only man in the room, but I figured I could survive the embarrassment to prove to my daughter I was willing to try something new.

As it turns out, being the only man in the room wasn’t the embarrassing part. The real problem was discovering that somewhere over the last couple of decades, I seem to have lost all sense of rhythm.

That’s what happens when you finally get off the couch. You discover the real problem isn’t what you expected.

The same thing is happening with AI right now.

Leaders are making resolutions to “figure out AI,” but most are approaching it the same way people approach the gym in January: without a framework. They dabble, feel overwhelmed, try random things without a plan, and eventually conclude they’ll revisit it later when the technology is “more mature.”

After 24 weeks documenting my own AI journey, I’ve realized the biggest shifts aren’t about tools or technology. They’re about mindset.

The two types of leaders I keep meeting

Over the last few months, I’ve noticed two distinct groups of leaders struggling with AI adoption.

The first group has been experimenting personally for a while. They summarize documents, draft emails, maybe brainstorm ideas occasionally. They understand AI can improve personal productivity, but they can’t connect those experiences to business strategy. When leadership asks about AI direction, they default to vague language like “we’re evaluating opportunities.”

The second group is already organizationally committed. They’ve approved pilots, purchased licenses, and declared AI a strategic priority. But they’re overwhelmed by competing ideas, vendor pitches, webinars, and LinkedIn hot takes. They have activity without clarity.

What both groups have in common is this: they’re trying to skip the part that matters most.

The first group is waiting for organizational clarity before building personal mastery. The second group is trying to drive organizational transformation before experiencing personal transformation. Both approaches fail for the same reason:

You cannot lead what you haven’t experienced yourself.

The principle that changes everything

The single most important thing I’ve learned over the last 24 weeks is this: personal transformation before organizational transformation.

You don’t need to become a technical expert. But you do need enough firsthand experience to distinguish real capability from hype. You need your own stories of what worked, what failed, and where AI genuinely changes how work happens.

The leaders who will separate themselves this year won’t necessarily have the biggest budgets or the most aggressive strategies. They’ll be the ones who put in the reps personally.

Everything else builds from there.

Shift 1: From search engine to thinking partner

Most people still use AI like Google with better grammar. They ask a question, get an answer, decide whether it’s good enough, and move on. When the response disappoints them, they conclude AI isn’t ready for serious work.

That’s like walking into a gym, failing to bench press 200 pounds on day one, and deciding weightlifting doesn’t work.

The real breakthrough happens when you stop treating AI like an answer machine and start treating it like a thinking partner.

AI works best through dialogue. Context matters. Iteration matters. Pushback matters. The shift happens when you stop asking isolated questions and start having conversations: explaining your goals and constraints, refining responses collaboratively, challenging shallow answers, and letting the interaction evolve.

You know you’ve made this shift when you stop judging AI by its first response and start building on it instead.

Shift 2: From waiting to exploring

Once you know how to think with AI, the next shift is learning what to explore.

Most leaders are waiting for the obvious use case — the one so compelling and risk-free that adoption becomes automatic. I don’t think that moment is coming.

The best opportunities are usually hidden behind frustrations you’ve normalized: broken processes, recurring annoyances, inefficiencies everyone has learned to tolerate because “that’s just how it works.”

The shift happens when you stop waiting for AI to prove itself and start bringing your frustrations to AI as diagnostic conversations.

Not: “How do I automate this?”

But: “What’s actually broken here?”

One of the most valuable techniques I’ve discovered came from adapting a medical diagnostic framework: start with the chief complaint, let AI ask systematic questions, and look for the unlocking moment where your understanding of the problem changes.

That’s when real progress starts. You know you’ve made this shift when you stop accepting friction as normal and start investigating it instead.

Shift 3: From consumer to creator

This was the most profound shift for me personally.

For years, I searched for better tools, better apps, better integrations. When I couldn’t find exactly what I wanted, I assumed building it myself would be too expensive, too technical, or too time-consuming.

Then I used AI to solve a 20-year frustration with my personal task management system in less than a week. Not by finding the perfect software. By creating exactly what I needed.

That changed how I think about capability entirely.

You no longer have to wait for someone else to build the exact solution you want. AI dramatically lowers the barrier between identifying a problem and prototyping a solution.

That doesn’t mean everyone needs to become a developer. It means leaders can now describe what they want in plain language, iterate rapidly, and test ideas before making formal investments.

You know you’ve made this shift when the question changes from “Does this exist?” to “How could I build this?”

The sequence matters

These shifts build on each other.

You can’t create solutions until you’re exploring meaningful problems. You can’t explore meaningful problems until you know how to think with AI collaboratively.

Most organizations try to skip straight to enterprise transformation without building leadership literacy first. That’s why so many AI initiatives produce activity without meaningful change.

The sequence matters because the mindset matters. And the mindset has to become personal before it becomes organizational.

Your Week 25 challenge

This week, identify one frustration you’ve been living with for a long time. Something small, personal, and fully within your control.

Then spend 30 minutes talking to AI about it. Not asking for solutions. Diagnosing the problem.

Let AI ask questions. Push back on shallow answers. Explore the issue until your understanding changes.

Because that’s the real goal at this stage. Not mastering AI. Learning how to think differently with it.

The year ahead

Twelve months from now, there will be two kinds of leaders.

The first group will spend the year waiting for clearer use cases, better tools, more mature technology, or someone else to figure it out first.

The second group will spend the year experimenting, learning, building literacy, and discovering opportunities hidden inside frustrations they’d stopped noticing.

The difference between those groups won’t be technical skill. It will be whether they were willing to get off the couch and start learning before they felt ready.

This post is part of my “AI Over 40” series. It first appeared on LinkedIn: AI for the Over 40 [Week 25]: The Three Shifts That Actually Matter

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