Organizations deploy AI tools without role-level capability targets. Employees then have nothing concrete to train toward — and managers have nothing to coach.
Rafał RadziszewskiFounder, FluencyRank
Buying a generic AI 101 catalog feels productive. Without a role-level definition of “good,” training has no target and assessment has nothing to measure against.
Workforce research on AI literacy stresses career-related competencies: judging when AI fits the task, interpreting outputs in domain context, and explaining findings to colleagues — not only abstract model knowledge.
What a role standard looks like (examples)
- Sales: draft outreach that a human verifies for claims and tone before send
- Finance: use AI for a first-pass summary, then apply policy and number checks
- HR: never paste confidential employee data into consumer tools; escalate when unsure
Then train toward that standard
- Short, daily workplace scenarios — not a single workshop
- Paths by function, not one company-wide video track
- Leaders see engagement and skill-area mix; they do not get fake “certified AI expert” badges
Education alone is not enough if work design never changes. Pair practice with a clear call on when to automate vs when humans must stay in the loop.
Start with one role, one standard, one week of practice. Expand after you can see who practiced and where judgment still fails.
Sources
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About the author
Rafał Radziszewski · Founder, FluencyRank
Rafał Radziszewski is the founder of FluencyRank. He is a Senior Director of Engineering and Poland Site Lead with 17+ years in commercial software and nearly a decade leading engineering teams — including FinTech, pharma, industrial enterprise, consulting/outsourcing delivery, and retail & corporate banking at Bank Millennium. He focuses on practical AI enablement for workplace teams — building fluency through daily practice, not tool rollouts alone.