Governance is often framed as the enemy of speed: policies, reviews, and blocked releases. In enterprise GenAI, weak governance is what actually slows you down, through incidents, lost trust, and rework.

This talk covers how to ship GenAI with lightweight governance that operators and customers can trust. Drawing on building RAG and LLM systems for early enterprise customers, and on working in regulated trading contexts, it outlines a practical control set: data access and tenancy, evaluation before and after release, logging for audit, human escalation, and clear ownership when answers are wrong.

We discuss how product managers can keep a dual-track cadence: fast experiments in low-risk sandboxes, and higher bars for workflows that touch customers, money, or compliance. The goal is not zero risk. It is known risk, recoverable failure, and continuous learning without freezing the roadmap.

Attendees leave with a one-page governance checklist they can adapt to their stack.