Elliott shares how Cohere is shaping the path from simple demos to enterprise deployment, why oversight, auditability, and security are essential for agent adoption, and the role of automation and memory in moving toward fully autonomous AI agents.

Megh shares how Crunchbase's customer-first approach to AI implementations preserved user trust while transforming search workflows, and practical strategies for rolling out AI features without alienating existing power users.

Anant shares lessons from building AI products for regulated industries as well as enterprises, and the winners will be those who spend their efforts adding value through end-to-end workflows and a great AI-native user experience.

Laura shares how Descript's "open world, infinite lives" design philosophy enables users to maintain control over AI creative workflows, and practical strategies for building AI interfaces that feel collaborative rather than restrictive.

Aman from Arize explains why traditional metrics fail for AI products and shares frameworks for better evaluation plus essential AI PM skills.

Michael reveals the infrastructure gaps preventing MCPs from enterprise adoption, why most APIs weren't built for AI, and how companies are seeing inversions from human to agent usage.

Richard shares the AI product strategy and framework they used to beating OpenAI's Deep Research agent on all benchmarks, and how you can replicate their success for your niche.

Aaron shares the importance of building a data flywheel for your AI product, and all the new challenges around acceess control AI products must solve for adoption.