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What is AI governance?
AI governance describes the interplay of roles, processes, rules, and tools that an organization uses to manage the development and deployment of AI systems. It is the foundation for responsible AI.
Key components: Roles and responsibilities (AI Officer, Ethics Committee, Data Officer), processes (approval of new applications, change management, incident response), policies (data protection, ethics, security), criteria (risk classes, approval thresholds), and tools (registers, documentation, monitoring).
Governance operates on three levels: strategic (what are the company’s AI goals?), tactical (which projects are prioritized and how are they approved?), and operational (how are AI systems operated and monitored on a day-to-day basis?).
For small and medium-sized businesses, governance is not a paper tiger, but practical management. It lays the foundation for transforming AI from an experiment into a reliable operational asset.
Best Practices for AI Governance
These six principles have proven effective:
- Supported by leadership: Without a clear commitment from senior management, governance will be ignored.
- Start pragmatically: Don’t aim for the perfect framework—take small, practical steps.
- Risk-based: Strictly regulate high-risk AI; keep standard applications lightweight.
- Integrated, not separate: Align with existing IT and compliance processes.
- Automate where possible: Use tools for registries, assessments, and documentation instead of Excel spreadsheets.
- Continuously refine: Governance isn’t static—schedule reviews and adjustments.
Common Mistakes in AI Governance
We see these pitfalls time and time again:
- Paper tigers: Extensive guidelines are written but never followed. Little impact.
- An IT-Only Issue: Governance is delegated to IT—business units and compliance are left out.
- Too Bureaucratic: Every small AI application goes through cumbersome approval processes—innovation dies.
- No Registry: No one knows which AI applications are running in the company. Flying blind.
- No follow-up: Assessments are conducted, but corrective actions aren’t implemented.
Governance vs. Compliance vs. Risk Management
A comparison of three related disciplines:
- Governance: A framework for steering—roles, rules, processes.
- Compliance: Adherence to rules—laws, standards, internal guidelines.
- Risk Management: Dealing with uncertainty — identifying, assessing, and mitigating risks.
Establishing AI Governance in Your Company
In a free initial consultation, we’ll assess your current level of governance maturity and outline a practical roadmap—including roles, processes, registries, and tools.
As an AI partner for small and medium-sized businesses, we take a pragmatic approach to establishing AI governance—no paper tigers, but with a clear structure and compliance with the AI Act.
What We Offer
- AI Consulting — Governance Development and Structure.
- AI Compliance in the Glossary — The Regulatory Framework.
- EU AI Act in the Glossary — What the Regulator Requires.
- AI Audit in the Glossary — Auditing as Part of Governance.
