James Finlay works at the intersection of applied AI, user behavior, and go-to-market execution at OpenAI, where he focuses on helping organizations translate advances in large language models into sustained, real-world behavior change. His work centers on individual adoption in practice: reducing friction, integrating large language model capabilities into existing tools and workflows, and designing experiences that move people from initial exposure to everyday, trusted use.

His perspective is shaped by a dual graduate background in behavioral communications and business administration, with a focus on how behavior changes at scale and how those changes translate into measurable outcomes. Across roles in product growth, experimentation, and enterprise software — including consumer-focused environments serving users inside large organizations — he has consistently explored how individual user dynamics shape successful business transformation. His work emphasizes understanding what motivates people, where friction appears in real workflows, and how systems can be designed so organizational goals align with individual incentives and habits.

What James is most excited to talk about is what happens after a large language model works technically. He is drawn to how these systems succeed or fail once they meet real human behavior: how trust forms, where friction quietly breaks adoption, and why surface-level usage often masks deeper failures in integration. His work emphasizes that observed behavior — not stated demand or model capability alone — should shape how large language models are deployed, evaluated, and improved over time.