What the term really means
Knowledge and skills needed to understand the capabilities, limits, risks and responsible use of AI systems.
How it works in practice
AI-literate staff can select a suitable tool, verify output, recognise sensitive data and know when an automated answer is insufficient.
The decision to make before implementation
Training should follow real roles: a content creator, analyst, administrator and decision approver need different capabilities.
How to verify that it works
Test behaviour in scenarios, verification quality and risky incidents rather than course completion alone. Compare results with an agreed baseline and review routine cases, difficult exceptions and human hand-offs separately. A practical AI Literacy test should have an owner, a review date and a recorded example of an outcome the team will not accept.
The PAR HOUSE Agency approach
We approach AI Literacy from the workflow rather than a tool demonstration. Training should follow real roles: a content creator, analyst, administrator and decision approver need different capabilities. We then build a small measurable scope, record assumptions and expand only after quality review.
