In brief
AI governance is the framework of policies, responsibilities, controls and documentation that an organisation uses to deploy AI systems responsibly, traceably and in compliance with the rules. It answers four questions: Which AI do we use? Who is responsible? How do we assess and limit risks? How do we evidence this?
Why AI governance matters now
- Regulation: The EU AI Act obliges providers and deployers of AI systems – depending on their role and risk category – to ensure AI literacy, transparency, documentation and human oversight.
- Data protection and confidentiality: Inputs into AI tools may contain personal data and trade secrets.
- Customers and auditors: More and more customers, supply chains and certifications ask for rules on the use of AI.
- Quality: AI results must be checked – otherwise errors flow unchecked into quotations, code or customer communication.
The building blocks of AI governance
| Building block | What it governs |
|---|---|
| Roles & responsibilities | Who approves AI applications, who operates them, who is the point of contact? |
| AI inventory | Which AI systems are in use – with purpose, provider, data and user groups. |
| Risk assessment | Classification under the EU AI Act, data protection and business risks, and the measures derived from them. |
| AI policy | Permitted tools, permissible data, review obligations and labelling – understandable for all employees. |
| Approval process | New AI applications are assessed and approved in a documented way before use. |
| AI literacy | Training and awareness measures appropriate to role and context of use (Art. 4 AI Act). |
| Monitoring & evidence | Key figures, incidents, reviews and auditable documentation. |
A pragmatic start for SMEs
- Inventory: Record all AI applications in use and planned – including “shadow AI” such as private ChatGPT accounts.
- Assign responsibility: Appoint a responsible person and regulate approvals.
- Short AI policy: Set out the most important rules on one or two pages.
- Build competence: Train employees according to their role and document the measures.
- Integrate into existing processes: Use quality, risk and data protection processes instead of building parallel structures (minimal-change principle).
Standards as orientation
ISO/IEC 42001 is an international standard for AI management systems. Certification is voluntary, but the standard provides a good structure – especially if you already operate a quality or information security management system.
Our approach
We start with a free assessment of your maturity level, record your AI applications and integrate the necessary rules where your work happens anyway. The result is AI governance that is actually lived – and that you can demonstrate to management, customers and auditors.
Last updated: September 2026 · Author: Dipl.-Ing. (FH) Herbert Schrank