Organizations today analyze vast amounts of data across cloud platforms, data warehouses, AI services, and processing engines. As these environments become more complex, keeping analytics fast and cost-efficient is increasingly challenging, and much optimization still relies on manual effort.This session shows how data systems are learning to optimize themselves: cloud warehouses that cut unnecessary processing, cross-platform tools like Apache Wayang that pick the best execution environment automatically and avoid vendor lock-in, and AI-driven pipelines that balance speed, cost, accuracy, and energy on their own. The takeaway: insight into how self-optimizing platforms are evolving from improving individual queries to optimizing complete AI and analytics pipelines.