Most companies charge into AI without invigorating or fixing their data infrastructure. The following panel of leaders share stories about what worked— and what hasn’t — when rebuilding their data foundations for AI. You'll hear how companies are solving specific challenges: connecting fragmented data across business units with data intelligence, handling the massive volume requirements of training through data operations, and building data platforms that maintain data quality at scale. 

• How do you prioritize data quality, integration and governance before scaling AI initiatives?
• How can your enterprise break down data silos and unify fragmented information to fuel AI models effectively?
• What team structures and operational strategies can ensure your organization is an AI winner and not stuck in endless data chaos?