Services / AI Operating Model / Governance
Governance: What changes through AI
Governance defines when AI may act independently, when people must intervene and who stands behind the result.
The shift
Governance defines when AI may act independently, when people must intervene and who stands behind the result.
From
To
- Basic oversight
- Low automation risk
→
- Steering and accountability for AI
- Risk and quality management
Guiding questions
- Which decisions may AI make on its own, under which conditions – and who defines that?
- How are goals, decision rights, tool access, data access and escalation paths defined?
- How are quality, risks and impact monitored on an ongoing basis?
The typical mistake
Governance as a pure compliance document. It only becomes effective when it is anchored in workflows, roles and KPIs.
What you can expect from us
- Governance model with committees, decision rights and escalation paths
- Autonomy levels per workflow (see Autonomy Levels)
- Risk and quality framework including monitoring
- Documentation and traceability of AI use
This page describes organizational design principles and is not legal advice. Regulatory requirements for AI should be assessed by qualified legal counsel.
Would you like to align your operating model with AI?
We are glad to discuss with you where AI can create impact in your organization – and which structures, roles and rules are needed for it.