AI Governance

Most organizations reach a point where AI tools are running across multiple teams, some approved, some not, with no consistent standard for who reviewed them or who's responsible if something goes wrong. AI governance is the structure that closes that gap. It covers policies, intake processes, ownership assignments, risk tiers, and escalation paths that make AI adoption something the organization can actually see, manage, and stand behind. It doesn't have to be elaborate to be effective. What it does have to be is real: followed, enforced, and resourced.

Without governance, AI risk becomes invisible. A model producing biased outputs, an employee tool leaking client data, a vendor system making consequential decisions with no named owner. These are governance failures, and they tend to surface at the worst possible moment. For executives, governance is also a precondition for scale. Teams that don't know the rules work around them. A clear, fast, usable governance process is what allows the organization to expand AI use confidently rather than discovering its exposure after the fact.