AI capability doubles every 100 days. Data quality doesn't. While banks race to deploy new models, they're discovering the real bottleneck isn't computing power or algorithms—it's fragmented, low-quality data trapped in legacy systems. In this session, panelists tackle the unglamorous but critical foundation layer as they explore building centralized data platforms that work across silos, implement data lineage governance that satisfies both AI teams and auditors, and protect against "AI debt"—the compounding cost of models built on poorly documented, unverified data. Learn why nearly half of all banks cite data quality as their number one AI obstacle and the importance of treating information assets like the strategic resources they are.