Generative AI is surging across the enterprise, but most initiatives stall on the same bottlenecks: fragmented data, runaway costs, and high latency. Without a unified, real-time data foundation, AI stays in the lab.

This talk shows how an incremental-compute Lakehouse becomes the missing substrate for production AI—merging batch and streaming, keeping data fresh at second-level SLAs, and exposing it through an AI-ready semantic layer so models can reliably consume features across online/offline paths. We’ll cover how to unify data management across multi-cloud estates to reduce compliance and operational complexity, and how organizations are cutting up to 60% in data-platform spend while improving performance for real-time analytics and experimentation.

Grounded in retail and internet case studies, attendees will leave with a pragmatic blueprint to turn fresh data into the growth engine for AI-driven products, recommendations, and monitoring at scale.