AI Isn’t a Technology Problem Anymore...

Over the next couple of years, AI will no longer feel experimental. It will be embedded—often quietly—into how organizations make decisions, manage risk, allocate resources, and serve constituents. At that point, the primary challenge will not be building AI capabilities, but governing them responsibly.

AI governance is often framed as a technical or compliance exercise. In practice, it is neither. It is an institutional discipline that sits at the intersection of strategy, risk, and stewardship. Governing AI means ensuring that powerful systems remain aligned with mission, values, risk tolerance, and long-term trust.

When AI influences hiring, eligibility determinations, financial decisions, healthcare prioritization, or donor engagement, it stops being a technology topic. It becomes a board-level issue. Boards do not need to manage AI, but they do need to exercise informed oversight—starting with a few basic questions: Where is AI being used? For what purpose? With what level of human accountability? And what happens when systems fail or behave in unexpected ways?

The shift now underway is from experimentation to stewardship. Early efforts focused on capability-building. The next phase demands clarity and discipline—knowing where AI is deployed (including through vendors) and distinguishing low-impact productivity tools from high-impact decision systems. Not all AI requires the same scrutiny, but high-impact use cases demand explicit accountability and clear escalation paths.

AI risk evolves. Models drift. Data changes. Context shifts. Effective governance must therefore span the full lifecycle—from design and deployment to monitoring and retirement. One principle should remain constant: accountability always remains human.

The purpose of AI governance is not to slow innovation. It is to make innovation durable. Over the next couple of years, the differentiator will not be who has the most advanced models, but who governs them with discipline and judgment.