AI Literacy for Workers

AI literacy does not mean learning to code. It is a set of working habits that let a non-specialist get value from AI without being misled by it. Six competencies cover most of it. First, understand roughly what the tool is. Generative AI models learn statistical patterns from large amounts of text or images and produce likely continuations. They do not look up verified facts unless they are connected to a search or document tool. Second, know the strengths and limits. These tools are good at drafting, summarizing, rephrasing, brainstorming and explaining. They are weak at exact figures, recent events after their training cutoff, niche facts and anything that needs your organization's internal context. They can also produce hallucinations: confident statements that are false. Third, verify. The more important the output, the more checking it needs. Confirm that sources exist, recompute numbers and compare claims against authoritative documents. Fourth, take care with data. Know which tools your employer approves and whether a tool keeps or trains on what you enter. Keep personal, confidential and client data out of unapproved tools. Fifth, match the tool to the task and prompt clearly. Give context, the audience, the format you want and any constraints. Sixth, stay accountable and disclose. AI use does not shift responsibility, so follow your workplace's rules about saying when AI helped. AI literacy is now a legal expectation in some places. Article 4 of the EU AI Act, which has applied since 2 February 2025, requires providers and deployers of AI systems to take measures to ensure a sufficient level of AI literacy among their staff. A quick self-check: can you explain why a chatbot might invent a source, name one thing you would never paste into it, and describe how you would verify a statistic it gave you?