Make the technical artifacts platform, not policy

AI governance policies are easy to ignore as written guidelines, but it’s far harder for the business to sidestep them if they’re part of the overall platform and formal set of strategy and governance systems.

“Strategy and governance drift apart when governance lives in a document, and strategy lives in systems,” says Hill. “Registration and logging should be properties of the platform, and there’s no model access, compute, or production path without being in the inventory. If it’s opt-in policy, shadow AI wins and the inventory is stale the day it’s finished. If it’s infrastructure, every new initiative inherits governance by default, and oversight scales at the speed of adoption rather than being an afterthought. This is the CIO’s highest-leverage contribution, and the one that no one else in the organization can make.”

 Scaling AI implementations is far more than just getting AI governance right. It’s about developing clear lines of sight and alignment between AI strategy, governance systems, and processes, so governance is cognizant of strategy and vice versa. It comes down to human accountability, and setting clear roles and expectations for both the business and IT.