Enterprise AI talks often assume clean labels, stable users, and a forgiving failure mode. Commodity trading platforms do not. Prices move continuously. Regulations differ by market. Integrations fail in partial, ugly ways. Operators need decisions under uncertainty, not another dashboard of predictions.

This session shares product lessons from building AI and data products inside a cloud-native energy trading and risk management (ETRM) environment that processes about $100 billion in commodity value daily. The focus is transferable: how product managers should design AI when wrong answers, delayed data, or brittle workflows have real operational cost.

We cover three themes. First, uncertainty is a first-class product requirement: confidence, fallbacks, and human override matter as much as model accuracy. Second, the best signals are often operational (exceptions, latency, reconciliation friction, support patterns), not vanity engagement metrics. Third, "prescriptive" beats "predictive": the system should recommend or trigger the next action, not only score risk.

Attendees leave with a practical lens for AI roadmaps in any high-stakes B2B domain: trading, finance, logistics, or regulated operations. Examples stay vendor-agnostic and emphasize product judgment over tooling hype.