01
Start with roles and use cases
List the people who procure, configure, operate, supervise or rely on AI outputs. A basic introduction may be shared, but practical modules should reflect the decisions each group makes.
Someone drafting marketing content needs copyright, source and confidentiality examples. A process owner also needs risk, testing, monitoring and supplier-change controls.
02
Teach decisions, not slogans
Participants should practise identifying sensitive data, checking a claim, responding to an incorrect output and escalating a high-impact case.
- what AI can and cannot reliably do;
- approved tools and data classes;
- hallucinations, bias and misleading confidence;
- human oversight and accountability;
- incident and concern reporting.
03
Keep proportionate evidence
Record the programme, date, audience, trainer or material, covered use cases and a knowledge check. Evidence should show that training reflects the context, not simply attendance.
Retrain when a role changes, a new high-impact tool is introduced or monitoring shows recurring errors.
04
Measure effective literacy
Use short scenario exercises, quality reviews and incident trends. The objective is better decisions: fewer unsafe inputs, more source checks and earlier escalation.
AI literacy is not a one-off event. A brief recurring update is often more useful than an annual presentation disconnected from current tools.
SOURCES
Verified source material
Information was checked on the publication date. Legal obligations should always be assessed for the specific organisation and use case.