From AI Pilots to Enterprise AI Engineering: Building the Operating Model That Scales
About this session
Most enterprises don't struggle to build AI pilots, they struggle to operationalize AI at scale. As organizations race to adopt Generative AI and Agentic AI, they discover the limiting factor isn't model performance. It's the absence of an engineering operating model that lets AI be delivered, governed, and improved continuously across a regulated enterprise.
In this session, I'll introduce Enterprise AI Engineering an emerging discipline that turns isolated AI experiments into repeatable business capabilities; through the lens of building it from zero inside a large, regulated healthcare enterprise. I'll walk through three moves I had to make at once, not in sequence: scaling an enterprise AI coding assistant with usage data that proved real adoption instead of shelfware licenses; designing a governed enterprise agent platform with compliance built into its foundation from day one which is what let a high-risk, customer-facing use case go live safely alongside lower-risk internal ones; and standardizing an AI-native SDLC so every future initiative, has a repeatable, governed path to production.
Rather than chasing the next model or tool, this session challenges leaders to rethink the engineering systems - platform engineering, governance by design, agent lifecycle management and developer experience that actually determine whether AI creates lasting enterprise value.
Speaker
Key takeaways
- Pilots don't fail on model quality
- Treat the AI platform as a product with a roadmap and a team, not an integration project
- Governance embedded in the SDLC from day one is a speed advantage, not a tax