LinkedIn·Tuesday, 28 July 2026·29d ago

The failure point in enterprise AI is rarely the model. It is the context around it. Ask two AI applications the same question about…

Felix Van De Maele

Entrepreneur, Founder, CEO of Collibra

The failure point in enterprise AI is rarely the model. It is the context around it. Ask two AI applications the same question about monthly active users and you can get two different answers. Neither model is wrong. They are working from different business definitions. Most organizations have spent years building that context. Then they leave it in a catalog the agents acting on it can't reach. A definition an agent can't access is just documentation. Governed context can't simply exist. It has to flow. Every agent, platform, and application making decisions needs the same definitions, ownership, and policies, delivered in the format it actually consumes. That is what we built the governed context compiler to do. It pulls governed context from the Collibra Knowledge Graph and delivers it to downstream systems through REST APIs, MCP, or YAML export. It is deterministic by design, so production agents receive the same trusted context every time. Gartner predicts that 60% of AI projects will be abandoned because organizations are not ready with their data. Governed context is a critical part of solving that problem. The companies that pull ahead won't treat governed context as documentation. They'll treat it as infrastructure that reaches every AI system in the enterprise. The context layer is becoming the real differentiator in enterprise AI. The companies that operationalize it will build AI they can trust at scale.