Why you need AI governance
As soon as several people in a company use AI, questions come up that no one answers alone: Which tools are allowed? Who checks the results? Which data may go in? AI governance answers them once for the whole company, instead of every department inventing its own rules. A related term is AI compliance: it means meeting requirements, and governance provides the frame for that.
The six building blocks
We summarize the frame in six building blocks:
| Building block | Guiding question | Result |
|---|---|---|
| AI inventory | Which AI applications are in use, by whom, with which data? | A maintained list that also captures personal use at work |
| Accountability | Who decides on new applications, who reviews, who is the contact? | Named roles with clear responsibility |
| Usage policy | What is allowed, what is not, and which data may go into which tool? | A binding policy everyone knows (template linked below) |
| Risk classification | Which risk level does the application fall under? | A documented classification per application |
| Monitoring and audit | How do we keep checking that the application does what it should? | Spot checks, logs and a fixed review rhythm |
| Training | Do employees know how they may use AI and where the limits are? | Training and a record of who took part |
Usage policy
The policy is the building block that is quickest to put in place. Our free AI policy template gives you a draft to adapt to your company. It is the document, and this guide is the frame behind it.
Risk classification under the EU AI Act
If your company does business in the EU, the EU AI Act may apply. It distinguishes four risk levels: unacceptable risk (banned), high risk, transparency risk, and minimal or no risk (European Commission, accessed October 11, 2026). The Commission states that the vast majority of AI systems currently used in the EU fall into the minimal-risk category. Which obligations apply to your application is a question for legal counsel. This guide does not cover US federal or state law.
Training
Whatever the legal position, people use AI better and more safely when they know what the tools can do and where the limits are. In our AI consulting, we run training together with the rollout.
Building it in five steps
- Take stock: Record which AI tools are in use, personal accounts included. This becomes the AI inventory. The AI readiness assessment helps to sort out where you stand.
- Name owners: One person keeps the inventory, one body decides on new applications, and data protection and IT are involved.
- Adopt a policy: Set the usage rules and make sure everyone knows them.
- Classify applications: Assign each application a risk level and document why.
- Train and review: Train employees and decide how often applications are reviewed.
For an overall plan in which governance is step 4 of the rollout, see the guide to an AI implementation roadmap. We build the applications themselves as a Corporate LLM, a RAG system or AI process automation, depending on the starting point.
Frameworks and standards as orientation
- NIST AI Risk Management Framework (AI RMF 1.0): Released on January 26, 2023, it organizes AI risk management into the functions Govern, Map, Measure and Manage and is intended for voluntary use (NIST, accessed October 11, 2026). NIST also publishes a Playbook with suggested actions for the four functions.
- ISO/IEC 42001:2023: The standard covers AI management systems (ISO, accessed October 11, 2026).
Both are orientation, not a prerequisite for starting.
Sources and note
All sources accessed October 11, 2026: European Commission, AI Act; NIST, AI Risk Management Framework; NIST, AI RMF Playbook; ISO/IEC 42001:2023. This guide is not legal advice. For your specific case, talk to your legal counsel or data protection officer.