AI in practice

AI literacy and the AI Act: build a useful learning plan

What teams should learn about AI and how to document measures, working rules and ownership clearly.

WERKVERSTAND / CONNECTING INTELLIGENCE

The essential answer

AI literacy means understanding capabilities and limits in your working context. A useful learning plan connects tasks, data questions, output verification and human accountability.

Use current guidance rather than old training promises

Following the 2026 Article 4 amendment, Commission guidance continues to describe measures supporting AI literacy development. It also clarifies that no particular individual knowledge level must be guaranteed and individual knowledge testing is not required. A company’s legal position remains a separate assessment. A quiz or workshop alone does not certify blanket compliance.

Start with the task’s actual risk

Record who uses which AI systems, what data they enter and the possible consequences of an incorrect output. Research emphasizes source verification, customer communication adds commitments and sending, and software changes require tests and access controls. Learning goals follow these differences rather than a universal presentation for everyone.

Document measures and practical application

A compact record identifies the audience, learning goal, date, examples covered and rules supplied. Add who handles open questions and when updates make sense. Practical exercises reveal whether material is understandable. They support development and do not replace required employment or privacy assessments of employee data.

Establish a small, repeatable learning routine

After onboarding, collect actual questions and discuss them through short examples. System, data-flow or allowed-action changes lead to updated rules. Use the literacy check for personal learning prompts and the policy template for an initial shared draft. Werkverstand can turn these into role-based training with your team.

Keep it verifiable

Primary sources

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