Energy trading has never been more complex or more consequential. Multi-ISO portfolios, surging data center and EV load, renewable intermittency, and real-time market volatility are compressing decision windows, and utilities can no longer afford to make billion-dollar trading calls from disconnected spreadsheets and stale data.

Join us to see how forward-thinking utilities are rebuilding their trading operations from the ground up. Learn how to replace hours of manual reconciliation across siloed market, weather, operational, and settlement systems with governed lakehouses that surface real-time P&L attribution, imbalance risk, and AI-generated bid packages in minutes.

You'll leave with a concrete understanding of what a production-ready trading intelligence architecture looks like, which use cases deliver the fastest ROI, and how to move from spreadsheet-driven decisions to agentic AI recommendations without a multi-year transformation timeline. Whether you're on the trading desk or leading a data platform initiative, this session delivers a practical framework you can bring home.

Session Takeaways:
•    Describe the core components of a production-ready AI trading intelligence architecture, from data ingestion to governed lakehouses.
•    Assess which energy trading use cases (P&L attribution, imbalance risk, bid optimization) deliver the fastest ROI.
•    Explain how agentic AI can replace manual reconciliation workflows across market, weather, operational, and settlement data.
•    Design a practical roadmap for shifting from spreadsheet-driven decisions to AI-powered recommendations without a multi-year transformation.