In regulated enterprise settings, the hard part of AI is rarely training a model. It is deciding what the system is allowed to do, what evidence it must show, and when a human must stay in the loop.

This talk reframes AI product work around judgment: scoping decisions that affect money, compliance, or customer trust; choosing which workflows are safe to automate; and designing governance that does not freeze shipping. Drawing on experience in energy trading software and earlier GenAI infrastructure work (RAG, vector search, LLM orchestration), it contrasts demo-friendly copilots with operator-grade systems that must be auditable and recoverable.

We walk through a simple decision framework: classify the workflow by risk, define the action the AI may recommend or take, specify the evidence trail, and set clear escalation paths. The session also covers how product managers negotiate tradeoffs with engineering, compliance, and customers without turning every release into a committee.

Attendees should leave able to pressure-test any "AI feature" proposal: Is the judgment clear? Is the failure mode acceptable? Can an operator explain why the system acted?