Data Is The Fuel For AI. Context Is The Refinery That Turns It Into Value

Sean Nathaniel is CEO of Upland, a provider of enterprise intelligence that turns knowledge, content, and data into AI-powered outcomes.

In most of my conversations with other executives about AI, two discussions are happening in two different places. In one, the business is asking why its AI investment hasn’t produced the return it expected. In the other, security and legal are asking what that same AI can see and whom it might show it to. Rarely are the same people in both rooms.

​They should be, because they’re asking about the same thing.

The common line is that data is the fuel for AI. While that’s true, fuel doesn’t power anything until it’s refined. For AI, context is the refinery: the layer that turns raw knowledge, content and data into answers that are accurate and appropriate to share.

I’ve written before about the three asset classes enterprises feed AI (knowledge, content and data) and the semantic and context layer that connects them. This article focuses on that layer itself, and why it determines both the return an organization gets from AI and the security of the information AI touches.​​

Why The ROI Problem And The Security Problem Share A Root

Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data. IBM’s 2025 Cost of a Data Breach Report found that among organizations reporting a breach of their AI models or applications, 97% lacked proper AI access controls.

I’d read those two findings together. One is about value, and the other is about risk, but both trace back to the same gap: AI that can reach information without understanding what it means, who it’s for or when it applies. When that understanding is missing, AI produces answers that are wrong, answers that shouldn’t have been shared or both. Organizations usually respond with separate fixes owned by separate teams, when a single foundation would address both.​