1.        Why AI Documentation Has Become a Management Issue

• EU AI Act: documentation as a legal prerequisite for market access
• Documentation as evidence of governance, not paperwork
• Consequences of insufficient documentation (audits, liability, reputational risk)

2.        AI Use Cases and Risk-Based Classification

• Why use cases, not algorithms, determine regulatory obligations
• Key dimensions for characterizing AI use cases (purpose, autonomy, impact, data, domain)
• Overview of EU AI Act risk classes:
  • Minimal risk
  • Limited risk
  • High-risk AI
  • GPAI and GPAI with systemic risk
• Management implications of each class

3.        What “AI Act–Compliant Documentation” Really Means

• Minimal documentation required for all AI systems
• Additional obligations for:
  • High-risk AI systems
  • GPAI models and GPAI-based applications
• What decision makers must ensure vs. what can be delegated

4.        Risk-Driven Documentation as a Steering Instrument

• Why documentation must be risk-driven and lifecycle-oriented
• The role of a central risk register for AI governance
• Avoiding duplicated documentation across AI Act, cybersecurity, and data protection

5.        Organizational Setup and Responsibilities

• Provider, deployer, customer, partner, and authority perspectives
• Who is accountable for what under the AI Act
• Typical organizational pitfalls and how management can prevent them

6.        Practical Takeaways for Management

• How to assess current AI documentation maturity
• Key questions management should ask their teams
• First steps toward an AI Act–ready documentation strategy

• Executive management and senior leadership
• Product owners and product managers
• Heads of AI, data, digitalization, or innovation
• Risk, compliance, and quality managers
• Program and portfolio managers responsible for AI systems

(No technical AI or ML background required.)

After the seminar…

• Participants will understand which AI systems in their organization are affected by the EU AI Act and why documentation is unavoidable for market access and risk control.
• They will be able to differentiate between minimal, GPAI-specific, and high-risk documentation obligations and assess the organizational impact of each.
• They will gain a management-level framework to steer AI documentation strategically, avoiding both over-documentation and compliance gaps.

Why is documentation mandatory under the EU AI Act?

Because compliance cannot be assumed or inferred. It must be demonstrated through documented evidence, especially for high-risk AI systems.
 

Is this only relevant for “high-risk” AI?

No. While high-risk AI has the most extensive requirements, every AI system requires a minimum level of documentation to justify its risk classification and governance.
 

Can documentation be created later if requested by regulators?

No. Documentation must exist before deployment or market entry; retroactive documentation is typically treated as non-compliance.
 

Is documentation mainly a technical problem?

No. It is primarily a management and governance responsibility, even though technical teams contribute content.
 

What is the risk of over-documentation?

Over-documentation wastes resources and reduces clarity; the goal is risk-proportionate, decision-ready documentation, not maximal documentation.