AI Literacy in the Workplace: How Employers Can Prepare for Article 4 of the EU AI Act
By AnnexOps 09-10-2026 2
Artificial intelligence is becoming part of everyday business operations. Companies use AI tools to analyse information, support recruitment, automate customer service, generate content and assist employees with decision-making. As adoption grows, organisations must consider whether their employees understand how these systems work, where their limitations lie and what risks may arise from their use.
The European Union has addressed this issue through Article 4 of the EU AI Act, which concerns AI literacy. The provision requires providers and deployers of AI systems to take measures that support the development of AI literacy among their staff and other people involved in operating or using those systems on their behalf.
For employers, this is not simply a matter of organising a general AI awareness session. A useful approach starts with understanding which AI systems the organisation uses, who interacts with them and what knowledge those people need in their specific roles.
What Does Article 4 of the EU AI Act Require?
Article 4 of Regulation (EU) 2024/1689 addresses AI literacy across organisations that provide or deploy AI systems.
Following the 2026 amendments, providers and deployers must take measures to support AI literacy while considering factors such as employees' technical knowledge, experience, education, training and the context in which the AI systems are used. Organisations should also consider the people or groups who may be affected by those systems.
The amended provision does not require organisations to guarantee a specific level of AI literacy for every individual. However, the obligation to take measures remains.
This distinction matters because AI literacy should reflect how AI is actually used within a business. An employee using an AI writing assistant will have different learning needs from an engineer developing an AI system or a manager overseeing an automated decision-making process.
Employers should therefore consider their AI environment before deciding which learning activities are appropriate.
Why AI Literacy Matters for Businesses
AI literacy helps employees make more informed decisions when interacting with AI systems. It also supports responsible use by helping people recognise limitations, understand relevant risks and know when human judgement is necessary.
Several workplace situations demonstrate why this matters.
Recognising inaccurate AI outputs
Generative AI tools can produce convincing answers that contain factual errors, outdated information or unsupported claims. Employees who understand these limitations are better positioned to verify outputs before using them in business decisions or external communications.
Protecting confidential information
Employees may unintentionally enter personal data, confidential business information or commercially sensitive material into AI tools. Appropriate guidance can help them understand which information may be shared, which tools are approved and when additional review is necessary.
Understanding bias and unfair outcomes
AI systems can produce biased or inappropriate results depending on their design, training data and context of use. Employees should understand when such risks may arise and how to escalate concerns.
Maintaining human oversight
In situations involving consequential decisions, employees need to understand the limits of AI-generated recommendations and the importance of appropriate human involvement. Where high-risk AI systems are concerned, separate human oversight requirements under the EU AI Act may also apply.
Who Should Receive AI Literacy Measures?
Article 4 is not limited to employees working in technical departments or organisations developing their own AI products.
Its scope concerns staff and other people dealing with the operation and use of AI systems on behalf of providers and deployers. The appropriate measures depend on the organisation's circumstances and the systems involved.
Potential groups include:
- Management teams: People responsible for approving AI use, allocating resources or overseeing organisational risks.
- Human resources teams: Employees using AI-assisted recruitment, screening or workforce management tools.
- Marketing and communications teams: Staff using generative AI for content, research and customer communications.
- IT and engineering teams: People integrating, configuring, developing or maintaining AI systems.
- Legal, compliance and risk teams: Employees assessing AI-related obligations, risks and governance procedures.
- Operational teams: People relying on AI outputs during routine business activities.
- Relevant external personnel: Other people who operate or use AI systems on the organisation's behalf, where applicable.
A single training format may not address the needs of all these groups. Employers should consider the knowledge required for each role and the potential consequences of errors when AI is used.
How to Develop a Practical AI Literacy Programme
An effective programme begins with the organisation's actual AI use rather than a generic training presentation.
1. Identify the AI systems used across the organisation
Start by identifying the AI systems employees use, including approved business applications, integrated AI features and relevant internally developed systems.
Departments may adopt tools independently, so employers should establish a clear picture of where AI is being used and for what purposes.
This inventory can also support wider AI governance activities by connecting systems to their owners, intended uses and relevant risks.
2. Assess employee knowledge and learning needs
Consider employees' existing technical knowledge, experience, education and previous training.
A developer working directly with AI models may need technical information about system limitations and testing. A marketing employee may need practical guidance on output verification, confidential information and content accuracy.
The objective is to identify relevant learning needs, not to assume that every employee requires identical training.
3. Connect learning activities to actual risks
Training should address the AI systems employees encounter and the context in which those systems operate.
Depending on the circumstances, relevant subjects may include:
- AI capabilities and limitations
- Data protection and information security
- Bias and potentially discriminatory outcomes
- Verification of AI-generated information
- Responsible use of AI tools
- Human oversight and escalation procedures
- Relevant EU AI Act obligations
- Internal policies and approved use cases
Organisations should also consider the people affected by their AI systems when identifying risks and deciding what employees need to understand.
4. Choose appropriate learning methods
Formal training sessions can be useful, but they are not the only possible approach. Depending on the organisation's needs, learning activities may include workshops, practical demonstrations, written guidance, role-specific learning materials and internal discussions.
The European Commission's AI literacy guidance emphasises that there is no single approach suitable for every organisation. The appropriate measures depend on the systems involved and the knowledge of the people using them.
Employers should choose methods that address identified learning needs and help employees apply what they learn in their daily work.
5. Review and update the programme
AI systems and their uses can change over time. New tools may be introduced, existing systems may receive new capabilities, and employees may take on different responsibilities.
Organisations should therefore review their AI literacy measures when relevant changes occur. Periodic reviews can help identify new learning needs and determine whether existing guidance remains appropriate.
How Should Employers Document AI Literacy Activities?
Documentation helps organisations demonstrate what measures they have taken and provides a record for internal review.
Article 4 does not prescribe a universal training certificate or a single mandatory documentation format. The European Commission's guidance explains that organisations can maintain internal records of training and other guidance initiatives.
Depending on the organisation's circumstances, useful records may include:
- The AI systems or use cases covered by the initiative
- The employee groups or roles addressed
- The learning objectives and subjects covered
- Training materials or guidance provided
- Dates and attendance records, where relevant
- Decisions about additional learning needs
- Reviews and updates to the programme
Records should reflect activities that actually took place. Employers should avoid treating a completed attendance sheet as proof that every AI-related risk has been addressed.
Documentation is most useful when it connects learning activities to the organisation's AI systems, identified risks and governance responsibilities.
Common Mistakes Employers Should Avoid
Organisations can make their AI literacy efforts less effective by treating them as an isolated administrative exercise.
Providing identical training to everyone: Different roles and systems create different learning needs. A tailored approach is often more useful than a single generic session.
Focusing only on technical knowledge: AI literacy can also involve legal, ethical, operational and data protection considerations.
Ignoring informal AI use: Employees may use AI features within existing applications or adopt tools without a central approval process. Organisations should understand their actual AI use rather than relying only on formal procurement records.
Treating documentation as the entire programme: Records are useful, but the underlying measures must be relevant to the organisation and its AI systems.
Assuming certification is mandatory: Article 4 does not establish a universal external certification requirement for AI literacy. Employers should select appropriate learning methods based on their needs.
Failing to revisit learning needs: New systems, changing workflows and emerging risks may require updates to existing guidance.
Connecting AI Literacy With Wider AI Governance
AI literacy is one component of a broader AI governance process. Organisations may also need to identify their AI systems, assess applicable risk categories, understand provider and deployer responsibilities, manage documentation and establish appropriate oversight.
These activities can inform one another. For example, an AI inventory can help identify which teams need specific guidance, while a risk assessment can highlight the limitations and potential harms employees should understand.
An organised AI governance workflow can also help teams connect policies, responsibilities, learning records and other compliance evidence.
For businesses managing several AI systems, AI compliance software may support this work by bringing system information, risk classification and documentation into a structured process. The software should complement appropriate organisational judgement and legal review rather than replace them.
Conclusion
AI literacy requires organisations to consider how their people interact with AI systems and what they need to understand to use those systems responsibly.
Article 4 of the EU AI Act provides the regulatory basis for this work. Employers should identify relevant AI use cases, assess employee learning needs, select appropriate measures and maintain records that accurately reflect their activities.
The 2026 amendments do not remove the obligation to take measures supporting AI literacy. Instead, organisations must continue to approach the subject in a way that reflects their systems, people and operating context.
For a more detailed explanation of employer responsibilities, practical implementation steps and documentation considerations, read the guide to AI literacy training under Article 4 of the EU AI Act.
About the resource: AnnexOps provides AI compliance software designed to support EU AI Act compliance activities and AI governance operations, including risk classification, documentation and evidence management.
This article is for general informational purposes and does not constitute legal advice.
