Organizations are moving quickly from informal use of generative AI toward institution-wide adoption. The governance question is no longer whether AI will be used, but how it will be used responsibly and accountably.
1. What problem are we solving?
AI initiatives should begin with a defined organizational problem or opportunity. A tool-first approach can produce activity without measurable value.
2. Who is accountable?
Leadership should establish ownership for AI decisions, risk, data, procurement, monitoring and escalation. Governance cannot be delegated entirely to the technical team.
3. What data is involved?
Before scaling AI, organizations should understand what data enters the system, where it goes, who can access it and what contractual or regulatory obligations apply.
4. Where must humans remain in control?
High-impact decisions require appropriate human oversight. Automation should support judgment where consequences, uncertainty or accountability are significant.
5. How will we know it is working?
Define performance, risk and adoption measures before scaling. Responsible AI combines value measurement with ongoing monitoring.
Our role is to help organizations translate these principles into context-specific diagnostics, governance, capability development and implementation priorities.