Why your best-interviewing AI candidate may not be your best AI hire

Demand for AI fluency has risen nearly sevenfold in two years, faster than demand for any other skill, according to the McKinsey Global Institute.

My conversations with technology leaders echo this: nearly every one I speak with is hiring for AI skills.

The problem, however, is that fewer can tell me what AI fluency actually looks like on the job at their organizations.

McKinsey found nearly 90% of companies have invested in AI.

Fewer than 40%, however, report measurable gains.

Most AI post-mortems take a hard look at models, data and workflows.

Few look at the people hired to champion AI transformation or how and why they were chosen.

The risks of mistaking confidence for competence A side effect of AI advancement is that it has made the ability to speak about AI use much easier.

A candidate might start by naming every model, then walking you through an architecture they read about last week.

In a short conversation, fluent language is almost impossible to separate from fluent practice.

Those who win over the hiring manager are those who sound the most at ease when speaking to AI.

Whether they can actually use AI to do the job is a separate question, and most hiring processes never ask it.

Think of an interview as a demo on the candidate’s chosen grounds with controlled setup and their narration.

On-the-job AI use is where the trouble starts: without proper AI fluency, it’s easy to lose control of messy data, missing edge cases, systems that refuse to talk to each other, or a model that hallucinates at the worst possible moment.