Haven't you heard? ""You don't have a knowledge problem. You have a context problem.""

This framing has resonated with business leaders in a way that ""knowledge graph"" or ""semantic layer"" often hasn't — and if you're in this room, you already know why. Context is the thing AI actually needs to be useful. And here's what might surprise your stakeholders: you're probably already building it.

Most organizations in this community have the foundational pieces in place — knowledge graphs, ontologies, taxonomies, metadata, reference data. The gap isn't technical. It's that these assets remain siloed, loosely governed, and disconnected from the systems that need them most. The result? AI initiatives that stall. Search that disappoints. Data products that don't deliver.

The real opportunity isn't building more models. It's turning what you already have into authoritative context — governed, trusted, and usable across teams, systems, and AI applications.

In this session, we'll show how leading organizations are making that shift: evolving knowledge graph investments into enterprise-ready semantic infrastructure by focusing on governance, constraints, and operational integration.

We'll cover how governed taxonomies and ontologies become the backbone for AI and data products, how constraints such as SHACL introduce the guardrails needed for consistency and explainability, and how semantic assets move from isolated projects to shared, enterprise-wide infrastructure.

You'll leave with a clear picture of why governance — not modeling — is usually what's blocking progress, and practical steps to mature what you've already built into a scalable Context Layer that works for both humans and AI.

You're closer than you think.