Most organizations don't struggle to build an AI prototype. They struggle to get it into production. The blockers aren't technical — they're organizational: legal wants auditability, compliance wants guardrails, expert users will catch every mistake, and leadership wants to know when the model is wrong.
This session is a product manager's guide to the decisions that determine whether an LLM feature ships or stalls. Drawing on experience building GenAI platforms at a YCombinator-backed startup and leading AI product development at an enterprise SaaS used by hedge funds and Fortune 100 companies, the talk covers: how to scope what the model can and can't own in a high-stakes workflow; how to design evaluation frameworks that satisfy both product and compliance teams; how to communicate model limitations to expert users who will test your system aggressively; and how to build the organizational trust that gets AI features past legal, risk, and the CISO.
Practical and non-commercial, designed for product leaders, AI practitioners, and anyone trying to close the gap between a working demo and a trusted production system.