The Operations Bridge

Point five in the AI Strategy Points series. Last time was about the adoption gap between people. This post is about a related but distinct idea: what AI actually does to the gap between your strongest and weakest performers.

A multiplier, not an equalizer

There’s a comfortable assumption floating around that AI will level the playing field — that it will lift weaker performers up to match stronger ones, closing skill gaps automatically. I don’t think that’s what actually happens.

What I’ve observed instead: AI will make smart people genius and average people smart. It’s a multiplier on the judgment someone already brings to the table, not a replacement for that judgment. Someone who already knows how to ask sharp questions, structure a problem, and evaluate an answer critically gets dramatically more out of these tools than someone who doesn’t yet have those instincts.

That’s not entirely bad news — average performers genuinely do become more capable, and that’s a real gain. But it means the gap between your best and your average people doesn’t close. In a lot of cases, it widens, because your best people are extracting far more leverage from the same tool.

What separates the genius outcome from the smart outcome

The difference isn’t raw intelligence in the traditional sense. It’s the ability to ask a better second question after the first answer comes back, to notice when an output is subtly wrong instead of just plausible, and to already have a clear enough picture of the problem that the AI has something real to sharpen rather than a blank canvas to fill with guesses.

In other words, the multiplier effect depends heavily on the quality of thinking someone brings before they ever open the tool. That’s a skill you can build deliberately, and it’s a skill leaders should be actively developing rather than assuming it will emerge on its own.

What this means for how you develop people

If AI is a multiplier on existing judgment, then AI training that skips straight to tool mechanics and ignores underlying thinking skills is solving the wrong problem. Teaching someone to write a better prompt without teaching them to define the problem clearly first just produces faster, more confident wrong answers.

The leaders who get the most out of this will be the ones who keep investing in fundamentals — root cause thinking, clear problem definition, healthy skepticism of first answers — alongside AI tool training, not instead of it. The tool amplifies whatever discipline is already there.

A question for leaders

Are you developing your team’s underlying judgment and problem-solving skills at the same rate you’re rolling out AI tools? Or are you handing people a multiplier and hoping it fixes gaps that were never actually addressed?

Next: why most AI initiatives start with platform training or prompt training, when the real starting point should be your data.