What AI Governance Can Learn From The Data Governance Era

InnovationWhat AI Governance Can Learn From The Data Governance EraByVinod Bijlani,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)May 29, 2026, 11:30am EDTVinod Bijlani is an AI practice leader at Hewlett Packard Enterprise. gettyEvery enterprise I speak with has the same story. AI initiatives in flight, promising pilots in the works, ambitions to deploy at scale and somewhere in the middle of it all, a growing recognition that the organization does not yet have the governance infrastructure to manage what it is building. I have spent the last year studying how the world's most AI-mature organizations—particularly in financial services, where the regulatory and reputational consequences of getting it wrong are highest—have gone about defining and operationalizing their AI governance strategies. What I found was not a story about compliance. It was a story I had seen before, in a different context.It looked exactly like what happened with data governance a decade ago, and the parallels are too instructive to ignore.AI Governance Is Having Its Data Governance MomentIn the early 2010s, enterprises were sitting on vast lakes of customer data, transaction records and operational signals. The promise was enormous. The reality was chaos: duplicate records, inconsistent definitions, no clear ownership and regulators beginning to circle. The organizations that paused to build data governance frameworks before scaling their data programs were the ones that eventually monetized that data with confidence. AI is at exactly that inflection point. And the first crisis, just like in the data era, is visibility. In the data world, nobody knew where all the data lived, who owned it or whether two systems using the term "customer" meant the same thing. AI has the same sprawl problem, only faster and with higher stakes. Acco...