The problem is that these definitions make sense to people, not machines. A human reading a text definition of “customer” can fill in the rest from experience: which systems customers live in, how they relate to orders, regions, and revenue. An AI can’t fill in what it was never given. It needs entities, properties, and relationships: a customer is a person, belongs to a domain, and places orders. Give an agent that graph and it can build its own model of the business and infer new information from it. Ask how many customers you serve in Europe, and it can reason its way to an answer. Give it a definition with no structure behind it, and it will guess confidently instead.
BI semantics help within a narrow scope, but they’re rarely built on the open standards that would let AI systems reason over them.
The dream of enterprise semantics in 2026
The power of frontier large language models (LLMs) tempts people into thinking that simply loading enterprise data into these models gets you close to the dream of enterprise semantics. It doesn’t. LLMs still need to be told what the structured data means, how business concepts are defined, and which data is authoritative. They need what people now call context.
