AI Governance and the EU AI Act for Business

Set principles that change decisions

Translate broad values into enforceable rules for approved tools, sensitive data, automated decisions, transparency, human review and prohibited uses. Every rule needs an owner and exception process.

Align governance with the company's actual risk appetite rather than copying a generic policy.

Create a tiered approval model

Low-impact use cases should move through a lightweight path, while consequential systems require deeper legal, privacy, security and business review. Define objective escalation triggers.

• Purpose and people affected
• Data sensitivity and scale
• Decision impact and reversibility
• Autonomy and external communication
• Vendor dependency and model opacity

Connect governance to existing functions

Integrate AI intake with procurement, privacy, information security, product development, HR and change management. Avoid creating a parallel bureaucracy that teams learn to bypass.

The AI inventory should point to owners, assessments, controls, contracts, evaluations and incidents.

Govern vendors and material changes

Require evidence appropriate to the use case and contract for change notifications, incident support and data restrictions. Reassess when the provider, model, purpose, data, users or autonomy changes.

Report leading indicators

Boards need more than a list of AI projects. Track inventory coverage, unresolved classifications, overdue controls, exceptions, incidents, evaluation failures, training and realized business value.