The Disappearing Skill: Why Intuition Still Matters in an AI World

How the rise of AI is threatening the experience we need to build intuition, and what we can do about it.

In the last couple of years, I have been hearing all the time that our decisions should be 100% data-driven. And in some cases, I confess, I was the person saying this.

When I was working at VTEX, discussing about prioritizing and implementing the Delivery Promises feature, as the engineering leader I was advocating for getting enough data to support the decision whether we should or should not prioritize the initiative.

But how can we come up with the numbers to justify something so complex?

And the results were obvious: of course, doing something that anticipates customers’ expectations would result positively. I have seen that before, there’s a clear pattern easily recognizable. Maybe the discussion should be how much we should invest, but not if we should initially invest in that.

That’s a good example of when intuition wins over analytics, while analytics support and refine the decision over time.

Today, intuition is at risk. As AI takes on more operational and repetitive tasks, many companies are reshaping or even eliminating entry-level roles. This raises a critical question: if experience is the raw material from which intuition is created, how will the next generation of professionals build the judgment needed to grow into senior and leadership positions?

First things first: some basic concepts

Intuition is not magic. Gary Klein, in The Power of Intuition, defines it as “the way we translate our experience into action.” It is the product of pattern recognition, built through repeated exposure to real situations, feedback loops, and reflection.

Daniel Kahneman would put it as the System 1 thinking: fast, automatic, and effortless. It’s the firefighter who knows where the fire will spread before seeing the flames, the chess master who sees a checkmate five moves ahead without calculating every branch, the senior engineer who senses a cascading failure from a single metric behaving oddly.

What makes intuition powerful is that it compresses years of experience into a fraction of a second. You don’t need to consciously run the numbers — you just know something is off.

And this is not irrational: research shows that experts often make better, faster decisions under uncertainty precisely because their brains have stored thousands of cases that inform those choices. In fast-moving organizations, this can be the difference between acting in time or missing the opportunity entirely.

The challenge that arises with the evolution of AI

Here’s where the problem emerges. If intuition depends on experience, and AI is now doing the repetitive, entry-level work that used to provide that experience, where will the next generation of professionals get theirs?

Take the example of a junior SRE or support engineer a decade ago: they would triage dozens of tickets per day, learn to read logs, spot patterns, and escalate issues. Over time, they built a mental library of “failure signals.”

Now, with AI handling log analysis, auto-remediating incidents, and summarizing alerts, that engineer might only see the most complex edge cases — without the “baseline cases” that teach them what normal looks like.

This is not just a technical challenge — it’s a leadership pipeline challenge.

Tomorrow’s senior engineers, product managers, and directors will be asked to make judgment calls, but they may lack the accumulated intuition that today’s leaders take for granted.

Gary Klein warns that “intuition is only reliable when it’s based on genuine expertise.” Without the repetitions, expertise never forms, and we risk raising a generation of professionals who are great at asking AI for answers but cannot sense when the AI is confidently wrong.

How to solve it

We can’t simply turn back the clock and remove AI from the equation. But we can be intentional about designing experiences that build intuition.

One solution is Decision-Making Exercises (DMX), structured scenarios where professionals must make choices with partial information, defend their reasoning, and review outcomes. This mirrors what Gary Klein calls recognition-primed decision making, forcing participants to build mental models they can use later.

Another effective method is the same one used in SRE practices: simulations, GameDays, and live drills. Netflix’s Chaos Engineering program is a well-known example, deliberately breaking production-like environments so engineers learn to diagnose, communicate, and recover under pressure. These repetitions create the “muscle memory” that intuition depends on.

The formula is simple:

• Expose people to realistic situations.

• Allow them to fail safely.

• Debrief and reflect so the lesson sticks.

If AI removes the natural volume of learning opportunities, we must create artificial ones, otherwise the intuition skill will indeed disappear.

Food for thought

AI is rewriting the way we work. But intuition, that ability to make timely, high-quality decisions in ambiguity, remains one of the rarest and most valuable human skills.

As we embrace AI, we must ask ourselves:

• Are we still creating paths for people to accumulate the experiences that form intuition?

• Are we prioritizing efficiency at the expense of future leadership capacity?

• Are we willing to slow down enough to let people learn, fail, and grow?

If we fail to answer these questions, we might end up with organizations that are efficient but fragile, dependent on machines that lack judgment and on humans who have never developed it.

Intuition might be disappearing, but it doesn’t have to. We just have to fight for it.