Priya Sarathy

 Why AI Scaling Fails:  The Data and Governance Gaps Leaders Underestimate.

As organizations race to adopt AI, many find that early pilot successes don’t translate into sustainable, enterprise-wide impact. Models perform well in isolation—yet initiatives stall, trust erodes, and risk quietly accumulates as AI scales.

This session explores why AI scaling so often fails—not because of technology limitations, but due to underestimated data and governance gaps. We examine what data leaders commonly overlook when moving from experimentation to execution: data readiness versus availability, unmanaged feedback loops, shifting human behavior, and governance models that struggle to keep pace with everyday AI use.

This talk reframes data management as the execution layer of AI strategy and governance as a decision-support mechanism, not just a compliance function.  Attendees will leave with concrete insights on where AI initiatives stall, what didn’t work in practice, and how data leaders can scale AI responsibly by scaling decision stewardship alongside technology.