Context: The Missing Layer in Enterprise AI
October 3, 2026
Hello, friends. AI assistants have made it easier to generate answers. They haven't necessarily made it easier to make decisions. Without access to reliable company data, those answers leave employees doing the hard work of checking facts and reconciling conflicting information. That's a hidden tax on AI's productivity promise. For enterprises chasing a return on their investment, the next step is giving AI the business context it needs to produce answers people can actually put to work. Thanks to Databricks for sponsoring today’s edition of The Deep View. Let us know what you think! —Jason Hiner
Context: The Missing Layer in Enterprise AI
Most enterprises have rolled out AI assistants broadly, but the ROI hasn't shown up where it matters most. The reason isn't that the AI isn't smart enough—it's that it's disconnected from the business itself.
The core problem: AI without context creates more work, not less
General assistants are fluent, not grounded. They're good at drafting, summarizing, and reformatting — but the decisions that actually run a business (pricing, inventory, risk, forecasting) require reasoning over real, current, authoritative company data. Most tools simply don't have access to that.
The "context tax" absorbs the productivity gain. Before anyone can act on an AI-generated answer, someone — usually an already-overloaded analyst — has to verify it: find the right data, reconcile conflicting sources, and reconstruct what's actually true. That validation work eats the time AI was supposed to save.
Confident-sounding answers create false progress. Ungrounded AI produces responses that read as complete but aren't reliably tied to real data, so the underlying work doesn't disappear — it just shifts back onto people.
The gap breeds shadow AI. When sanctioned tools can't answer real business questions, employees paste sensitive data into unapproved tools or unsanctioned extensions. Productivity looks like it's up, but compliance risk and ungoverned data exposure quietly build underneath it.
The takeaway
The productivity promise of AI was never really about deploying more assistants — it was about closing the distance between "AI that can talk" and "AI that can be trusted with the decisions that run the business."
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