Job seekers frequently overestimate what employers mean by "AI skills" in a listing, assuming it requires deep technical expertise. In most non-technical roles, the actual bar is more practical.
For most non-technical roles
It usually means comfortable, effective use of AI tools for the role's actual tasks — writing, research, analysis — plus the judgment to know when to double-check the output. Not building or training models.
For roles explicitly labeled technical
It means something closer to what job seekers assume — genuine understanding of AI systems, and for some roles, the ability to build and deploy them.
The mismatch that costs candidates opportunities
Candidates who assume they need deep technical AI knowledge for a marketing or operations role sometimes don't apply at all, when their actual practical AI fluency would have been more than sufficient.
What to actually demonstrate in an application
Concrete examples of using AI tools to improve a real outcome — faster research, better content, smarter analysis — communicate the right kind of "AI skills" far more effectively than a vague claim of familiarity.
The honest advice
Read the actual listed responsibilities, not just the "AI skills" line, to gauge what level of AI fluency is genuinely expected — the phrase means different things in different postings, and context resolves the ambiguity.