Governance guidance
1. Move the conversation from innovation to accountable adoption
AI board packs often overemphasize pilots, tools, and efficiency gains. A stronger board briefing explains how AI aligns with strategy, risk appetite, customer outcomes, data obligations, outsourcing controls, and operational resilience.
- Show AI use cases by strategic value, risk level, owner, and control status.
- Identify which AI uses affect customers, regulated processes, sensitive data, or critical operations.
- Separate experimentation from approved, monitored institutional use.
Governance guidance
2. Make control evidence visible to senior management
A board cannot oversee AI through policy statements alone. Senior management needs a concise governance dashboard that shows approval status, open risks, vendor dependencies, monitoring results, incidents, exceptions, and overdue remediation.
- Define regular AI governance reporting for committees and accountable executives.
- Track material use cases, risk ratings, control owners, and outstanding actions.
- Use consistent escalation criteria for high-risk AI, GenAI misuse, and vendor changes.
Governance guidance
3. Integrate AI governance into existing frameworks
AI governance becomes more durable when it connects to established structures: enterprise risk management, model risk, outsourcing, cyber security, privacy, conduct, data governance, and internal audit. This avoids a parallel framework that looks good on paper but is not used in decisions.
- Map AI requirements to existing policies and committees.
- Clarify when model-risk, outsourcing, cyber, privacy, compliance, and legal review are required.
- Ensure audit and second-line functions can challenge AI governance evidence.
Governance guidance
4. Ask better questions before approving scale
The board and senior management should be able to challenge whether proposed AI expansion is supported by clear ownership, reliable data, explainability appropriate to the use, vendor accountability, human oversight, monitoring, and employee training.
- Require clear go/no-go criteria for material AI use cases.
- Confirm how customers, staff, and regulators would be affected by failure or misuse.
- Review whether human oversight is meaningful, documented, and assigned.