Rethinking Data Risk And Governance In The Age Of AI
AI (artificial intelligence) is forcing organizations to rethink something they’ve long treated as background infrastructure: data governance and risk management. In a recent article for Forbes, John M. Bremen – Managing Director and Chief Innovation & Acceleration Officer for WTW – explores how enterprises are re-evaluating data strategy as AI moves deeper into decision-making and operations. A central argument is that data is no longer just an IT asset — it’s a core driver of AI performance. Yet many organizations still struggle with basics like data quality, access, and ownership. Research cited in the article shows that more than half of organizations see data quality and availability as the biggest barrier to successful AI adoption. To address this, the article outlines five key practices. First, companies need to stop treating data as a commodity and instead recognize the complexity behind ownership, regulation, and security. Second, they must understand that not all data is the same — transactional, operational, and analytical data each serve different purposes and carry different risks. Third, organizations should quantify data risk in business terms, not just compliance terms, focusing on how data quality impacts real decisions. Fourth, governance needs to evolve from rigid rules to more dynamic, principle-based models that can keep up with AI systems. And finally, companies should shift from strict data control to data stewardship, focusing on how data is used and what outcomes it enables. The article also breaks down different data types — from transactional and master data to unstructured, synthetic, and real-time streams — emphasizing that each requires its own governance approach. Breman concludes as AI becomes more central to business strategy, strong data governance isn’t optional anymore. It’s the foundation that determines whether AI delivers value or creates risk.
