Most AI adoption talks focus on model capability. This one focuses on what happens after the model works: getting AI into a production system that a Fortune 500 company depends on for legally consequential, irreversible decisions. Drawing on a decade of building enterprise IP infrastructure including a patented confidence-scoring workflow engine and a production payment automation platform processing renewal decisions for intellectual property rights worldwide , this talk walks through the real engineering tradeoffs of deploying domain-specific AI where generic models fall short: policy-bound automation, human-in-the-loop escalation thresholds, and the audit and governance requirements that separate a demo from a system enterprises can actually trust. Attendees will leave with concrete patterns for deciding when to automate, when to keep a human in the loop, and how to build AI systems that hold up under real operational and regulatory scrutiny.