What is AI literacy?
AI literacy is the knowledge, skill, and judgment that let people use AI systems sensibly in their work. For most staff it has little to do with how models are trained. It means knowing what a tool is designed for, how it typically fails, what data may be entered into it, when an output needs checking, and whom to tell when something looks wrong. The right depth depends on the role. A claims processor who uses a summarization tool needs different knowledge from the analyst who configures it or the manager who approves its use.
AI literacy in the EU AI Act
Article 4 of the EU AI Act requires providers and deployers of AI systems to take measures that support AI literacy among their staff and others who operate or use AI systems on their behalf. The original text asked them to ensure a sufficient level of AI literacy. An amendment adopted in 2026, part of the EU’s simplification package, turned that into a duty of effort, so a company has to show what it did and does not have to guarantee what each person knows. The duty covers every AI system regardless of risk classification, including general-purpose chat assistants. The Act does not prescribe a course, an exam, or a certificate. Deployers of high-risk systems must also assign human oversight to people with the necessary competence and training. A record of training and guidance is a practical way to show what was done.
AI literacy in practice
A multi-site medical practice introduces an assistant that drafts replies to patient portal messages. Front-desk staff learn which message types the assistant may handle and which must go to a clinician. Nurses who approve drafts practice spotting errors in medication names and doses, because fluent wording makes those easy to miss. The practice manager learns where message content is stored and how to report a bad output. Each role gets training of a different length, and the practice records who completed what.
What to watch for
- Tie training to the tools people use and the decisions they make, because generic awareness sessions fade quickly.
- Include everyone who approves or acts on AI output, since automation bias is a literacy problem as much as a design problem.
- Cover data handling explicitly, alongside the AI acceptable use policy, including what must never be pasted into an external tool.
- Check the current text of the AI Act and the European Commission’s guidance, because the EU continues to refine how its obligations apply.