Satyen Sangani

What does it take to get AI agents into production? Electronic Arts (EA) processes petabytes of telemetry and player data across a complex cloud ecosystem, a challenge familiar to most enterprises. Daimler Truck North America (DTNA) builds precision-engineered semi-trucks, coordinating hundreds of thousands of parts. Hear firsthand how both companies are building AI-ready data foundations: EA is accelerating live operations decision-making with the goal of increasing analytics bandwidth by 50%, while DTNA is giving Material Planners superpowers to ensure trucks are never delayed. Leave with a blueprint you can bring back to your organization.

As organisations scale AI and achieve outcomes, success depends on trusted, high-quality data underpinned by strong governance. In this fireside chat with LIPTON Tea and Infusions, Robin Rietveldt will share how domain-aligned metadata, data quality, and governance, powered by Alation, enable a unified delivery framework using Critical Data Elements (CDEs) to reduce risk, drive self-service, and power AI-ready analytics and product initiatives.

Scaling AI agents requires knowledge foundations, not just better models. Cisco has seen strong results by deploying agents across the Supply Chain. This session shares lessons and practical use cases showing how AI can free up employees' time, reduce costs, and unlock new capabilities. Success comes from working backwards from business outcomes to data management, choosing what to automate first, and establishing governance to scale effectively.