AI governance: definition, setup and limits
Definition
AI governance is the frame that defines who decides on AI in a company, who approves it, and how its use is controlled and documented. It connects responsibilities, approval steps, quality criteria and evidence into one process. The goal is that AI is used in a traceable, verifiable way and in line with internal and legal requirements.
Why the Term Matters
Without clear ownership, AI applications appear scattered across departments while nobody keeps the overview. That becomes costly at the latest when a model produces wrong results and no one is responsible. As a neutral advisor we build governance that fits the size of the company, no more bureaucracy than needed and no less control than sensible. AI stays steerable while it grows in operation.
When It Is Relevant
- Several departments use AI and no one knows the overall state.
- Approvals and responsibility should be clarified before first productive use.
- The EU AI Act requires documented controls and a responsible function.
- Management wants to know which models run on which data.
When Not
When only a single, clearly scoped AI application is in use and a named person looks after it, a lean rule suffices instead of a comprehensive frame. And when AI is still pure trial in-house, a simple overview of the experiments is enough before a formal governance process is set up.
Industries That Use It
- Insurers: models intervene in pricing and claims review, so clear approvals and audit trails are needed.
- Energy utilities: AI drives forecasts and grid decisions, and critical applications need documented control.
- Wholesale: scattered AI tools in purchasing and planning are consolidated through governance.
- Law and tax firms: confidentiality and duties of care demand a traceable handling of AI.
Typical Use Cases
- A register of all AI applications with purpose, data and responsible person.
- An approval process that checks new models before productive use.
- Roles and responsibilities from the department to management.
- Control points that regularly review quality and results.
- A documentation template that bundles evidence for audits and oversight.
Decision Guide
| Signal | AI governance fits | Alternative |
|---|---|---|
| Many tools, no overview | build a frame with a register | sprawl does not manage itself |
| A single clear application | a lean rule suffices | a full frame would be overbuilt |
| Legal evidence duty | governance with documentation | checking afterwards is risky |
| Pure trial phase | a simple overview is enough | a formal process comes too early |
Example
An insurer uses AI in three departments without shared rules. We capture the applications in a register, name a responsible person per model, and define an approval step before productive use. A light control rhythm reviews results. This describes the method, not an asserted client metric.
Service Connection
The build usually starts with an AI readiness check and AI consulting. For legal requirements, EU AI Act consulting complements the frame.
Sources
This framing describes common practice in building AI governance. Statements on legal evidence and control duties are general orientation and not legal advice; they must be checked case by case against the currently applicable legal texts.