Five Pitfalls of Building a Universal Semantic Layer for AI
Every organization that wants to talk to its data hits the same wall: the AI needs semantics, and
the default plan is to build a universal semantic layer from scratch. If that’s your current project,
come see what’s waiting for you. Each pitfall in this session comes with the alternative that
avoids it.
The volume problem – Your semantics aren’t missing; they’re scattered across thousands of
reports and models. Don’t rebuild them by hand. Extract them automatically into a knowledge
graph.
The conflict problem – Extraction surfaces three definitions of “active customer” and four
revenue measures that almost agree. Detect and resolve conflicts as new models are
published.
The moving target – New content arrives weekly, so a one-time build is stale at launch. Keep
the extraction running instead.
The ownership problem – Don’t stand up new stewardship. Inherit the certification, ownership,
and access controls your BI environment already has.
The proof problem – Replace spot checks with benchmarks that score accuracy, latency, and
cost on every change, with token usage attributed per question and per model.
Live demos throughout.