AI literacy under the EU AI Act: building a programme that holds up

What Article 4 asks for

Article 4 of the AI Act requires providers and deployers to take measures to ensure AI literacy among their staff and other people dealing with AI systems on their behalf, taking into account their technical knowledge, experience, education and training, and the context in which the systems are used¹. It has applied since 2 February 2025⁵.

The Digital Omnibus changed the wording. Commentary describes Article 4 as recalibrated rather than removed: organisations must still take measures, but the revised text no longer requires guaranteeing a specific level of competence for each individual, and the Commission and Member States take on a supporting role³. Practitioners have argued the change may complicate compliance as much as simplify it, because "appropriate measures" leaves more to judgement⁴. Confirm the current consolidated text with counsel.

Why a generic course isn't enough

The Commission publishes example practices from organisations, but its own page says that replicating them doesn't automatically establish compliance². The duty is tied to context: the systems you use and the people using them. A one-hour company-wide video on "what is AI" doesn't address the claims assessor relying on a model score or the engineer wiring an LLM into a customer workflow.

The checklist

1. Inventory AI systems and their users. Start from the same register you need for the rest of the Act: which systems, who uses them, for what decisions.
2. Define roles, not departments. Typical groups: everyday users of AI tools, people who rely on AI outputs for decisions, people who build or configure AI systems, and leaders who approve AI use.
3. Set learning outcomes per role. For decision-makers relying on outputs: what the system does, its known limits, when to override it and how to report problems. For builders: data protection, evaluation, security and the containment patterns in prompt injection defence.
4. Teach on your own systems. Use real examples of your tools getting things wrong. People remember a failure they've seen in their own workflow.
5. Embed it in workflow. Short guidance at the point of use, such as inside the tool or in the approval screen, does more than annual training.
6. Keep evidence. Who completed what, when, and how content maps to systems and roles. Penalties are set nationally⁵, so a regulator or auditor will ask what you did and why it was appropriate.
7. Refresh when systems change. A new model, a new use case or an incident is a trigger to update the material, not just the calendar.

Where it connects to architecture

Literacy and design reinforce each other. A system that shows confidence, sources and known limitations in its interface makes users more literate every time they use it. A system that hides them depends entirely on training. When I design AI products, the interface carries part of the literacy load: citations for retrieved content, clear marking of AI-generated text, and approval screens that show what is about to happen.

The same evidence also supports other frameworks. Training records and role definitions feed SOC 2 controls, and for companies operating in the Gulf they overlap with governance expectations under the UAE PDPL and DIFC Regulation 10.

How I can help

I help organisations build AI governance that engineers and business teams actually use, from system registers to role-based literacy and product design. As a fractional CTO I can own that programme. Or analyse your challenge and bring the result to a call.