Everyone's talking about being AI native. Management wants it. Developers are doing it. You've got Cursor, Claude, Copilot - the full stack.
And yet somehow, nothing actually got faster at the team level.
Here's the thing nobody warns you about: the problem isn't the AI. The problem is what the AI is working with. Your architecture is in Miro. Your PRD is in Confluence from 2022. Your README hasn't been touched in 14 months.
15 companies over 25 years. The one constant is that everyone has a knowledge problem. Everyone knows it. Nobody fixes it before buying the AI tools.
In this talk we'll go through what actually needs to happen first - how knowledge flows (or doesn't) across a product lifecycle, why RAG alone doesn't solve it, and why GraphRAG is the architecture that finally makes AI useful at the organizational level, not just the individual one.
We'll also look at what this looks like in production — real system, real stack, real lessons.

Every company has a knowledge problem. Everyone knows it. Nobody fixes it before buying the AI tools. This talk is about what happens when you do it the right way — from knowledge fragmentation across your product lifecycle, through RAG and its limits, to GraphRAG in production. Real system, real stack, real lessons.