The Right AI Fluency Criterion

The requirement cannot be completely avoided, but it needs a proper checklist. For Liisa Pursiheimo, a Finland-based HR strategist, AI fluency as a promotion criterion depends on the hiring organization’s AI maturity. “If they are ahead of the curve, the expectation for AI fluency is higher. Companies that are in earlier stages of exploring and experimenting most likely don’t have an overarching AI strategy, thus have not reached the state where it would translate into competency requirements.”

In May, KPMG launched an internal dashboard to monitor employee AI usage, setting a 75% adoption target across most of its staff. Meanwhile, Meta announced that its employees’ performance will be assessed by AI-driven impact starting in 2026. Other key firms on the AI-implementation bandwagon include Cisco, Microsoft, Amazon, and Google. 

For such companies, Prof. Churchill advocates sequencing AI fluency responsibly: invest in structured, role-relevant training before evaluating performance; ensure managers understand what meaningful AI use looks like, rather than rewarding only visible AI use; and monitor whether adoption and advancement rates diverge across demographic groups.

The definition should sit across three levels: literacy (understanding what AI systems are and are not), applied competence (the ability to use AI tools critically and effectively within one’s domain), and contextual judgment (knowing when AI assistance adds value and when it introduces risk). “The weighting across these levels will legitimately differ by role. A finance analyst and a product designer need different fluency profiles, but both need judgment,” she adds.

Many organizations are still working out what genuine, context-sensitive AI competence looks like. A lawyer, a designer, a software engineer, and a public-sector leader won’t need the same specific skills. Still, each one needs to grasp where AI falls short, judge its outputs with a critical eye, manage the risks involved, and apply these tools responsibly within their own fields.

An effective strategy for leaders should combine conceptual understanding, applied practice, and judgment. Meanwhile, employees should be made aware of what AI systems actually do at a functional level, be able to improve real workflows with them demonstrably, and know how to recognize problems such as bias, hallucination, privacy risk, uncertainty, or misuse.