More than half of organisations have made a "bad AI hire" in the past year, according to a new report, as AI fluency takes over domain expertise in employers' hiring priorities.

A "bad AI hire" refers to an individual who is fluent in AI jargon and tools during the recruitment process, but is unable to apply this knowledge in their role, according to TestGorilla.

These bad AI hires emerge amid employers' shifting priorities, with hiring managers now preferring candidates with strong AI fluency over those with deep subject matter expertise.

But this shift alone isn't the problem, said TestGorilla CEO Wouter Durville.

"The right framing isn't AI skills vs. domain skills. It's AI skills applied to domain skills," Durville told HRD.

"Hire for the combination. The 'bad AI hire' problem is what happens when you optimise for one without testing for both."

Issues in the modern hiring process

TestGorilla's report identified three critical issues of modern AI hiring and recruitment processes.

The first critical issue is setting the minimum bar at tool awareness, with the second one leaving AI assessment entirely to the individual discretion of hiring managers without a shared rubric.

The last critical issue in the modern recruitment process is its current design where it observes communication skills among candidates, and not their ability to execute.

This means candidates are being hired for being able to speak fluently about putting AI workflows into practice, despite not ever having audited an output or redesigned a workflow in the past.

Avoiding the 'bad AI hire'

Taking on a bad AI hire could cost employers in lost output, failed projects, and the time and money to rehire, according to TestGorilla.

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