The AI Experiments Every Executive Should Be Running Right Now

I want to tell you about two CEOs I spoke with last month. Same industry, similar-sized companies, comparable resources.

The first spent thirty minutes showing me a tool she had built herself — a dashboard that pulled her company's support tickets, categorized them by root cause using AI, and surfaced patterns her team had been missing for months. She built it in an evening, using Claude, after getting frustrated that the data existed but nobody was looking at it the right way. The insight it produced — that 40 percent of support volume came from a single onboarding step — led to a product change that cut ticket volume by a third in six weeks.

The second CEO had a polished AI strategy deck. Fifteen slides. Vendor evaluations. A roadmap for a company-wide rollout in Q3. He asked thoughtful questions about governance and data security. He was being responsible.

He was also twelve months behind. Not on a technology adoption timeline — on developing the judgment to know what AI can actually do for his business. And that kind of gap does not close with a strategy deck.

This wave breaks the pattern

Every executive has navigated a technology shift before — internet, mobile, cloud. In every one of those waves, the job of a non-technical leader was the same: understand what the technology can do, then direct others to build it. Your value was vision and decision-making. The building was someone else's job.

This wave breaks that pattern. For the first time, a COO can prototype the internal dashboard she's been requesting for six months. A CMO can build the content workflow he sketched on a whiteboard. A founder can turn a process doc into a working tool — not in six weeks, in an afternoon. Not because these leaders became engineers, but because the distance between "I want something that does this" and "here is something that does this" collapsed so dramatically that, for a growing category of work, directing and doing are now the same activity.

The skill that matters is not coding. It is clarity — knowing what you want, communicating it precisely, and iterating until you get there. Every experienced executive already has this. They just have not applied it to building yet.

What "getting invested" actually looks like

"Executives should use AI" has become background noise. Here's what it means concretely — three things, in order.

Pick a real problem, not a demo. Not "ask ChatGPT a question and see what happens." Find something you actually need solved: the weekly report that takes four hours to compile, the process your team has been begging to automate. The learning comes from applying the tool to a real constraint, not from a sandbox.

Do it yourself. Personally. Not through an assistant. Not through a pilot team. Sit with the tool. Feel the friction of your first bad prompt. Notice where the output is surprisingly good and where it misses. That judgment cannot be delegated or absorbed from a briefing — it can only be earned through direct experience, the same way you earned your judgment about people, markets, and operations.

Talk about what you find. Openly. This is the multiplier most leaders skip. Teams take their cues from senior leadership. When an executive experiments and shares what they learned — wins and failures — the whole organization gets permission to do the same. When AI gets handed entirely to IT as a "managed rollout," a ceiling forms.

The organizations moving fastest are not the ones with the best AI strategy decks. They are the ones where curious leaders run small experiments, talk about the results, and build a culture where the question is not "are we allowed to try this?" but "what should we try next?"

The advantage that cannot be purchased

I spent twelve years in the Army, much of it in aviation — an environment where the quality of your decisions under pressure is the only thing that matters. The clearest lesson of that career: the organizations that win are rarely the ones with the most resources. They are the ones that develop better judgment faster. And judgment comes from one place: repetition. Do the thing, reflect, do it again slightly better.

The same dynamic is playing out with AI. The leaders who are experimenting aren't just getting more efficient — they're developing intuition. Which problems AI handles well. How to frame a question. How to evaluate an output. When to trust the tool and when to override it. That intuition compounds with every experiment, and it cannot be acquired secondhand. You can't read your way to it, hire your way to it, or buy a platform that includes it.

The gap between those experimenting and those waiting is not a knowledge gap. It is an experience gap. And experience only closes one way — by doing the work.

The real cost of waiting

In previous cycles, waiting was defensible. Let the early adopters work out the bugs, let the market settle, then adopt the winning platform. That worked for the internet, cloud, and mobile.

It won't work this time, because the advantage being built isn't technological — it's cognitive. The CEO with the support-ticket dashboard didn't just find an insight her team missed. She learned she could go from question to answer in an evening instead of a quarter, and she started asking different questions — bigger ones, more frequent ones, ones she'd never have bothered asking when each answer required a three-week analytics project.

That shift in thinking is the real advantage, and it compounds: leaders who think this way start building AI-native workflows and AI-assisted decision-making, and each one makes the next easier. The gap doesn't just persist. It widens — and the target the waiting organizations eventually need to catch is moving faster than they are.

Your first hour

Here's what I'd ask you to do this week. Not this quarter. This week.

Block one hour. Pick one real problem in your business. Open Claude or another AI tool and start talking to it about the problem. You'll be frustrated at first — your first prompts will produce vague, generic output. That's not the tool failing; that's the starting line. Tell it what's wrong with its response. Give it context. Show it examples.

After sixty minutes you'll have learned more about what AI can and can't do for your work than any strategy deck, analyst report, or keynote could teach you. And that first hour doesn't end at sixty minutes. Something clicks. You start asking "what if I just tried..." about things you'd mentally filed under "someday." That shift — from someday to today — is the most important thing happening in business right now.

If you want to compare notes on what you find — or explore what AI fluency could look like across your organization — reach out through the contact page. And if something here sparked a thought, drop it in the comments below. I read every single one.