What Article 4 requires
Article 4 of Regulation (EU) 2024/1689 obliges providers and deployers of AI systems to take measures to ensure, to their best extent, a sufficient level of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf. The obligation is people- and process-oriented. It is not a model performance metric and it does not prescribe a single curriculum, certification, or documentation format.
Article 3(56) defines AI literacy as skills, knowledge, and understanding that allow providers, deployers, and affected persons, taking into account their respective rights and obligations, to make an informed deployment of AI systems and to gain awareness about opportunities, risks, and possible harm. Measures must account for the persons’ technical knowledge, experience, education and training, the context in which the AI systems are used, and the persons or groups on whom the systems are used.
Article 4 applies from 2 February 2025, six months after the Act’s entry into force on 1 August 2024. Scope is broader than employees in a narrow employment sense. Anyone who operates or uses AI systems on the organisation’s behalf, including contractors and service partners, may be in scope. Providers and deployers remain responsible for ensuring measures are implemented. High-risk deployers also face separate human oversight and competence duties under other provisions; those duties interact with Article 4 but are distinct from the general literacy obligation.
Determine who is in scope and what “sufficient” means by role
Start with an inventory of AI systems and agents in production or pilot use. For each system, record intended use, risk classification or transparency context, affected persons, and human–AI interaction design. Then map every person who configures, operates, supervises, approves, or acts on outputs on the organisation’s behalf.
“Sufficient” literacy is context-dependent. Article 4 does not define quantitative thresholds or mandatory course hours. Literacy needs scale with operational privilege and consequence of error. A read-only consumer of summaries differs from someone who edits system prompts, connects enterprise tools and data, approves agent tool calls, or authorises autonomous actions. Prior technical knowledge, education, and experience should reduce unnecessary training for some roles while increasing depth for others.
Derive role-specific outcomes from responsibilities and systems used. Typical outcome families include safe permitted use, recognition of limitations and failure modes, correct handling of sensitive data, escalation paths when outputs are unreliable or out of policy, and understanding of transparency obligations toward affected persons. For high-risk or high-impact workflows, align literacy outcomes with human oversight procedures so training supports operational readiness rather than standalone awareness.
Include vendors and outsourced operators explicitly. Contractual clauses should require literacy measures appropriate to the work performed, right to evidence of completion, and notice when personnel or operating procedures change. Without that coverage, organisational responsibility under Article 4 can exceed internal HR systems.
Build an enterprise AI literacy program as continuous governance
Treat Article 4 as an ongoing capability with named owners across AI governance, risk, HR or learning and development, and system owners. A one-time campaign does not match the duty to keep measures appropriate as systems, roles, and use context change.
AI agent operators need deeper literacy and runtime linkage
Personnel who configure, supervise, approve, or respond to autonomous or semi-autonomous AI agent actions require literacy beyond general user awareness. Relevant outcomes include understanding tool and function calling, autonomy boundaries, approval workflows, hallucination and failure modes that affect supervised actions, and when to override or stop an agent.
Document procedures for human review, override, and incident escalation for agent-supervised workflows. Training should state which actions may proceed automatically, which require human approval, and how operators record rationale when accepting or rejecting agent proposals. Literacy needs increase when agents can call internal tools, write data, initiate workflows, or interact with other agents.
Runtime governance strengthens literacy programs by enforcing policy at the point of use. Controls such as agent identity, least-privilege permissions, tool approval workflows, runtime policy enforcement, monitoring, and audit logging help ensure that only appropriately prepared personnel can grant elevated agent privileges. Training completion can be linked to entitlement systems so access and approval rights remain current. Trussed AI focuses on runtime governance and security for enterprise AI agents in these control areas. Literacy remains a people control; runtime controls do not replace Article 4 measures, but they make permitted use and evidence of control design more operational.
Align agent literacy artefacts with broader AI Act compliance materials, including risk management, human oversight design, transparency, and logging. When an incident or audit question arises, reviewers should be able to connect who was authorised, what they were trained to do, which agent permissions were active, and what actions occurred.
Governance practices that keep Article 4 workable
- Name accountable owners: Assign joint ownership across AI governance, risk, HR or L&D, and system owners so literacy does not stall as a compliance-only task.
- Prefer outcomes over hours: Define what each role must be able to decide or escalate. Course length alone is a weak proxy for sufficiency under Article 4.
- Refresh on material change: Trigger updates when models, tools, agent permissions, use cases, or affected populations change, not only on a fixed annual calendar.
- Cover third parties contractually: Require literacy measures and evidence from vendors who operate or use AI on your behalf, with audit and notice rights.
- Measure more than attendance: Use role assessments, sampled oversight quality, and incident lessons to revise content and access rules.
- Accept remaining uncertainty: Sufficiency is context-dependent and EU soft-law expectations may evolve. Document rationale for proportionality decisions.