Enterprise CIO notes: static ontologies break internal AI agents
These are notes from a conversation with the CIO of a large enterprise, discussing , ontologies, and s. Large enterprises are focusing heavily on building internal agents rather than customer-facing ones, mainly to cut labor costs, but the to support this is still immature.
Enterprises sense they need better but don't yet have settled terminology for it. In practice, many point agents at fragmented internal systems and hope the model can infer business meaning across them on its own — an approach that breaks down quickly once it hits production.
A core issue is that static ontologies (fixed models of business concepts) are outdated the moment they ship: the real world changes daily, but the ontology or (the layer that maps raw data to business meaning) only gets updated once a quarter. Even human get restructured every few years, underscoring how constantly reality shifts.
Key points
- Enterprises prioritize internal cost-cutting agents over customer-facing ones
- They recognize the need for but lack settled terminology for it
- Pointing agents at fragmented systems and hoping the model infers meaning breaks in production
- Static ontologies refresh quarterly, so they're outdated by the time they ship
- Even human get restructured every few years, showing how fast reality changes