Article 4 of the EU AI Act requires providers and deployers of AI systems to take measures ensuring their staff and anyone else operating AI systems on their behalf have a sufficient level of AI literacy, an obligation that has applied since 2 February 2025, well before most other provisions of the Act take effect. The article defines AI literacy broadly as the skills, knowledge, and understanding needed to make informed decisions about AI systems and to be aware of the opportunities, risks, and potential harms they can cause, and it explicitly says the required level should account for each person's technical knowledge, experience, education, and the specific context in which the AI system is used. In practice this means a company cannot satisfy Article 4 with a single generic training video for the whole organization; a data scientist deploying a high-risk system needs materially different training than a customer service employee using an AI-assisted support tool. Regulators expect documented, role-based training programs with records showing who was trained, when, and on what content, since the obligation is framed as an ongoing organizational measure rather than a one-time announcement. Nanobase AI, an NVIDIA Inception Program member, develops role-based AI literacy training as part of its EU AI Act compliance support for clients.
Article 4 is broader than a training video
Article 4 of the EU AI Act, in force since 2 February 2025, requires providers and deployers to ensure staff and anyone operating AI systems on their behalf have a sufficient level of AI literacy, defined broadly as the skills and understanding needed to make informed decisions about AI and to recognize its risks and opportunities. The article explicitly ties the required literacy level to each person's role, technical background, and the specific context in which they use AI, which means a single generic training session for the whole company does not satisfy the obligation as written. This is general information about a regulatory provision, not a legal opinion on what your specific training program must contain.
Training depth by audience
| Audience | Training focus | Typical depth |
|---|---|---|
| Executives and decision-makers | AI Act risk tiers, business impact, governance accountability | Briefing-level, focused on decisions they will actually make |
| AI or ML builders and developers | Technical risk, bias testing, documentation obligations for high-risk systems | Deep, hands-on, tied to specific development practices |
| Business users of AI tools | Acceptable use, data handling limits, when to escalate a concerning output | Practical, scenario-based, tied to daily tool use |
| HR and hiring teams | AI Act high-risk classification for employment decisions, Article 22 interaction | Focused specifically on employment-related AI use cases |
| Procurement | Vendor AI Act obligations, questions to ask before adopting a new AI vendor | Checklist-based, tied to the procurement process itself |
Treating these as one audience produces training that is too technical for business users and too shallow to be useful for developers, which satisfies neither the letter nor the intent of the requirement.
Building the evidence trail
Documented, role-specific training records are what separate a defensible Article 4 program from one that only exists as an assumption.
- Segment employees into the audience categories relevant to the organization, adapting the table above to actual roles rather than adopting it unchanged.
- Develop role-specific content for each segment, tied to the AI tools that segment actually uses rather than generic AI concepts.
- Deliver training with a recorded completion date and content version for each employee, since regulators expect documented evidence, not an assurance that training happened.
- Refresh training when the organization adopts a new category of AI tool, such as agents or coding assistants, rather than treating the initial rollout as permanently sufficient.
- Tie the training program to the organization's AI acceptable use policy so the two reinforce each other rather than existing as separate initiatives.
Common gaps regulators are likely to probe
The most common gap is training that covers general AI awareness without connecting it to the specific tools and use cases the organization actually deploys, which fails the article's requirement that literacy account for the specific context of use. A second common gap is training records that show attendance without any record of what content was actually delivered or when it was last updated, leaving no evidence the training matched current tools and current risks. A third gap, particularly relevant for organizations deploying high-risk systems, is training that covers general AI literacy but skips the role-specific technical depth developers and system operators need to meet their higher bar under the article.
Frequently asked questions
Does Article 4 apply only to companies building AI systems?
No, it applies to both providers who build AI systems and deployers who use them, which means any organization using AI tools, not only those developing models, needs an AI literacy program covering its own staff.
How does AI literacy training differ from the acceptable use policy?
The policy defines the rules for what employees can and cannot do with AI tools, while literacy training builds the underlying understanding of AI's risks and limitations that lets employees apply those rules with judgment in situations the policy did not explicitly anticipate.
Is a one-time training session sufficient to satisfy Article 4?
A single initial session is unlikely to remain sufficient on its own, since the article frames AI literacy as an ongoing organizational measure and new AI tools and risks emerge continuously, making periodic refresh training part of a defensible compliance posture.
How Nanobase AI helps
Nanobase AI, an NVIDIA Inception Program member, develops role-based AI literacy training as part of its EU AI Act compliance support for clients, segmenting content by audience and building the documentation trail regulators expect to see.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.