Many organisations can explain that they "do something about AI". But in audits, evidence is what counts: who was trained, what was assessed, what were the outcomes, and how is corrective action managed?
This checklist helps you make AI literacy measurable and demonstrable.
Step 1 — Define scope and roles
- Which departments use AI (or will start using it)?
- Which roles carry higher risk (e.g. HR selection, legal, finance, security)?
- Which tools are permitted (approved tooling)?
Step 2 — Define learning objectives per role
- Basics: prompt skills, hallucinations, bias, privacy/security.
- Role-specific: risks, do's/don'ts, quality checks, and escalation.
Step 3 — Train and assess
Training without assessment provides little assurance. Combine microlearning with short assessment moments and real-world scenarios.
- Short quiz (understanding of key concepts).
- Scenarios (what do you do when in doubt, data breach, bias, erroneous output?).
- Practical assignment per role (with rubric/criteria).
Step 4 — Document the evidence
- Per employee: date, score, certificate/outcome.
- Version control: which learning materials and which policy version applied at the time?
- Repetition: schedule updates when new tooling or regulations are introduced.
Step 5 — Make it audit-proof with reporting
- Coverage: % of employees trained per role/department.
- Quality: score distribution and gaps.
- Actions: which improvement actions have been initiated and what is their status?
- Export: can you quickly provide evidence (PDF/CSV)?
Conclusion: demonstrability without administrative pain
With a clear checklist, you make AI literacy verifiable and scalable.
Qrio helps with training, assessment, and dashboards so you can demonstrate in minutes what your team is capable of.