The argument boils down to one claim: models without operational context are decision engines flying blind. That's largely right — retrieval and fine-tuning alone don't encode the messy constraints of a supply chain or a compliance regime, which is why so many enterprise pilots never survive contact with production. The weak spot is that 'context-aware AI' is already drifting toward buzzword status, with vendors slapping the label on RAG pipelines plus a dashboard. What actually moves the needle, in our view, is boring infrastructure work: data contracts, process maps, and feedback loops from the people who own the decisions. Expect the next wave of enterprise pitches to compete less on model quality and more on who owns your context.