Enterprise AI is failing, and it's not the model's fault.
Most enterprise AI pilots deliver zero measurable business impact. The LLM is capable. The last mile is context.
AI works when there's structure.
Developers see massive productivity gains from AI because code has explicit structure: dependencies, imports, call graphs. Your business has none of that. Without structure, AI guesses.
Memories without relationships are dead ends.
Vector stores capture facts. They can't trace what connects to what. That's the difference between retrieving a fact and reasoning to insights and action.
Every organization has unique DNA.
Your ontology, the relationships between your people, systems, and processes, is unlike anyone else's. Without understanding it, AI is just guessing about your business.
Context must be discovered automatically.
The only catalog that stays accurate is one that builds itself, continuously, from the systems you already run. Manual data entry can't keep pace with systems that change by the second.
Ontology requires rules, not just data.
Raw data isn't structure. An ontology emerges from rules that define what entities exist, how they relate, and what those relationships mean. Rules make relationships queryable. Rules make AI trustworthy.
AI agents need operational intelligence.
MCP gave agents a way to call tools. But tools without operational intelligence are blind. Agents need to know who owns what, what's blocked, and what's actually running before they can act.