Many organizations introduce AI coding tools expecting immediate productivity gains, only to discover inconsistent results, architectural friction, quality concerns, and team confusion.

I was part of the core group introducing AI-assisted engineering workflows in a 5,000+ engineer organization, working through adoption challenges, governance questions, and architectural constraints at scale. I later built an AI-native engineering setup from day one in a startup environment.

In this session, I’ll share the practical lessons learned from both environments:

- Why unclear architecture kills AI leverage
- What needs to change in codebase structure
- How team practices must evolve
- The common mistakes that create AI chaos instead of acceleration

At the end of the talk you will have a clear, actionable items that you can start embedding into your teams right away

This talk is a field guide for architects, engineering managers, and CTOs who want to introduce AI into their engineering organization and actually benefit from it.