Enterprise AI Governance

• Starting point for AI governance is to establish an AI Authority.
• A cross-functional body that sets both the strategic ambitions and the boundaries for AI within the organization.
• To be effective, it must assemble senior leaders representing business, operations and control functions – not technologists alone.
• The AI Authority ensures that the organization’s AI vision and strategic objectives are clearly articulated and that all activities align to them.

Among the most important

• Human-in-the-loop accountability
  Define who owns each AI-supported decision. Automated does not mean unaccountable and a named owner should be assigned to every process where AI plays a role.
• Risk-tiered use case 
  Not all AI is equal, a clear framework should state the appropriate policy and controls for low, medium, high and critical risk levels. An internal chatbot is not a credit-scoring engine and attention should be distributed accordingly.
• Data privacy by design
  A policy should require the mapping of every data source feeding AI models and controls must be defined to enforce consent, anonymization and audit trails.

• AI risk management should then be woven into existing risk management structures, with particular attention to agentic AI processes.
• Here periodic controls are insufficient: real-time monitoring and human validation checkpoints will be essential.

Finally, AI governance must ensure performance management is implemented, setting targets and measuring outcomes, just like for any other transformation program.