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What AI literacy should cover
AI literacy is not the same as turning every employee into a machine-learning engineer. It means role-appropriate understanding: what AI can and cannot do, when outputs need review, which data must not be entered, how bias and errors can appear, when AI use must be disclosed, and how incidents should be reported.
Training topics
- Approved AI tools and prohibited tools.
- Rules for personal data, confidential data, customer data and source code.
- Hallucination, bias, accuracy and human review.
- Transparency and disclosure expectations.
- High-impact use cases that require approval.
- Security, phishing, prompt injection and data leakage risks.
- Incident reporting and escalation.
Role-based training
Different teams need different examples. HR needs examples about hiring and worker management. Marketing needs examples about AI-generated content and disclosure. Engineering needs examples about code, security and model integration. Customer support needs examples about chatbot handoffs and escalation. Leadership needs dashboards and risk ownership.
Evidence to keep
Keep training records, policy acknowledgements, version history, attendance, example materials, and refresher dates. This makes AI literacy easier to prove and maintain.
FAQ
Who needs AI literacy training?
People who use, manage, procure, develop or oversee AI should receive role-appropriate training.
How long should training be?
A short baseline session plus role-specific examples is better than a long generic presentation no one remembers.
Should training be repeated?
Yes. Repeat when tools, policies, laws or use cases materially change.
Sources and review method
This page is written as general business guidance, not legal advice. It is maintained from official AI Act materials, European Commission / AI Office updates, the NIST AI Risk Management Framework and practical AI governance controls.