How to Track AI Literacy and Training for EU AI Act Compliance

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AI is becoming part of everyday business operations. Employees use AI assistants to write content, analyse information, generate code, summarise documents, support customers, and automate routine tasks.

As adoption grows, organisations face an important question:

How do you know whether the people using AI have the knowledge they need to use it responsibly?

This is where AI literacy becomes important.

Under EU AI Act Article 4, providers and deployers of AI systems are expected to take measures to ensure, to the best of their ability, a sufficient level of AI literacy among staff and other people involved in operating or using AI systems on their behalf.

For businesses, this means AI literacy should be more than a one-time training course. Organisations need a practical way to identify training requirements, assign learning, track completion, maintain evidence, and review those requirements as AI use changes.

What Is AI Literacy Under the EU AI Act?

AI literacy is the knowledge and understanding people need to use AI systems appropriately and responsibly.

It can include understanding:

  • What an AI system is designed to do
  • Its capabilities and limitations
  • Potential risks associated with its use
  • When human judgement is required
  • How to verify AI-generated outputs
  • Relevant organisational policies
  • The user's responsibilities

Not everyone needs the same level of knowledge.

For example, an employee using generative AI to brainstorm marketing ideas has different responsibilities from a developer integrating an AI model into a product.

Therefore, AI literacy should be appropriate to the person's role and the context in which AI is being used.

Why Should Organisations Track AI Literacy?

Delivering training is only the beginning.

Imagine an organisation has hundreds of employees using different AI tools. Six months after a training programme, management may need to know:

  • Who completed the training?
  • What training did they complete?
  • Was it appropriate for their role?
  • Who still needs training?
  • When should training be reviewed?
  • Can the organisation demonstrate its efforts?

If these records are spread across emails, spreadsheets, and different systems, answering these questions can become difficult.

A structured tracking process provides better visibility and connects AI literacy with broader AI governance and AI risk management.

1. Identify Where AI Is Being Used

Start by understanding your organisation's AI landscape.

Identify the AI tools and systems used across departments, such as:

  • Generative AI assistants
  • Coding tools
  • AI-powered business applications
  • Recruitment systems
  • Customer service AI
  • Data analysis tools
  • Internal AI applications

Record basic information such as the system's purpose, department, users, owner, and potential risks.

This inventory can become the foundation for determining who needs training and what that training should cover.

2. Map Employees to AI Systems

Next, identify who interacts with each AI system and what their responsibilities are.

For example:

Marketing teams may use generative AI for content creation.

Developers may use AI coding assistants.

HR teams may use AI-supported recruitment tools.

Managers may use AI-generated analysis to support business activities.

Compliance teams may oversee AI governance.

These roles do not require identical AI literacy.

Mapping employees to AI systems helps organisations create a more targeted training programme instead of assigning the same course to everyone.

3. Define Role-Based Training Requirements

Training should reflect actual responsibilities.

General employees may need to understand:

  • AI fundamentals
  • Responsible AI use
  • AI limitations
  • Data confidentiality
  • Output verification
  • Internal AI policies

Employees with greater AI responsibilities may need additional training covering:

  • AI risks
  • Human oversight
  • System limitations
  • Appropriate use cases
  • Escalation procedures

Technical, legal, and compliance teams may require deeper knowledge of AI risk management, regulatory requirements, documentation, and governance controls.

The key principle is simple:

Training should match responsibility.

4. Track Training Completion

Once requirements are defined, create a central record for monitoring progress.

Useful information can include:

  • Employee name or identifier
  • Role and department
  • Relevant AI system
  • Required training
  • Training status
  • Completion date
  • Assessment results
  • Review or refresher date

The objective is to maintain clear visibility into who has completed required training and who still needs attention.

This becomes increasingly important as an organisation's AI footprint grows.

5. Keep Evidence of Training

Completion percentages alone may not provide enough context.

Organisations should consider maintaining relevant evidence, such as:

Training materials

Completion records

Attendance records

Assessment results

Policy acknowledgements

Role-based training assignments

Refresher training records

Maintaining organised records can support broader EU AI Act compliance activities and make internal reviews easier.

The goal is not to create unnecessary paperwork. Good documentation simply makes it easier to demonstrate what the organisation has done and identify remaining gaps.

6. Connect Training With AI Risk Management

AI literacy becomes more effective when connected to the organisation's broader governance process.

For example:

  • AI system identified
  • Risks assessed
  • Responsible roles identified
  • Training requirements defined
  • Training assigned
  • Completion documented
  • Requirements reviewed

This connects employees, AI systems, risks, and governance responsibilities.

If an AI system changes significantly, the organisation can also reconsider whether the people using it need additional training.

7. Review Training Regularly

AI literacy should not be treated as a one-time activity.

New AI systems are introduced, existing tools gain capabilities, employees change roles, and new risks emerge.

Review training requirements when:

  • A new AI system is introduced
  • An existing system changes significantly
  • Employee responsibilities change
  • New risks are identified
  • Internal AI policies are updated
  • Regulatory requirements develop

A simple continuous cycle is:

Identify → Assess → Train → Track → Review → Update

This makes AI literacy part of ongoing governance rather than an annual checkbox.

Common AI Literacy Tracking Mistakes

Using One Course for Everyone

Different roles involve different AI responsibilities and risks. A single generic course may not address the needs of every employee.

Tracking Completion Without Context

Knowing that someone completed training does not necessarily demonstrate that the training was relevant to their responsibilities.

Forgetting New Employees

Relevant AI literacy requirements should be incorporated into appropriate onboarding processes.

Failing to Update Training

AI tools and capabilities change quickly. Training should evolve alongside them.

Keeping Records in Disconnected Systems

Scattered records across spreadsheets, emails, and different platforms can make it difficult to understand the organisation's overall training position.

Separating AI Literacy From Governance

AI literacy becomes more valuable when connected to AI systems, risks, responsibilities, policies, and oversight.

Where AI Compliance Software Can Help

As organisations adopt more AI systems, manually managing AI inventories, risk assessments, documentation, and training records can become challenging.

This is where AI compliance software can help create a more structured governance process.

Instead of maintaining disconnected records, organisations can work toward connecting:

Who uses AI → Which systems they use → What risks exist → What training they need → What training they completed → What needs review.

Platforms such as AnnexOps take an operational approach to AI governance, helping organisations manage AI systems, risks, documentation, compliance workflows, and related governance activities.

The value is not simply having another place to store training records. The bigger benefit is connecting AI literacy with the broader governance context around the systems employees actually use.

Final Thoughts

Effective AI literacy is not simply about making employees complete a course.

It is about knowing who needs training, what they need to understand, whether they have completed it, and when their requirements should be reviewed.

By connecting AI literacy with AI governance, risk management, documentation, and ongoing review, organisations can build a more practical approach to EU AI Act compliance.

As AI becomes increasingly embedded in business operations, maintaining a clear view of employee AI literacy will become an important part of responsible AI governance.

The organisations that approach AI literacy as an ongoing process—not a one-time training exercise, will be better positioned to manage the people, risks, and responsibilities that come with widespread AI adoption.


Location: Marienburger Str. 14A, 10405 Berlin, Germany

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