In my last two articles, I asked why successful AI pilots so often fail to produce enterprise-level ROI, then argued that enterprises need an intelligence layer that connects what their existing systems know.
That raises the next question: What exactly does that intelligence layer need to understand?
The answer is more than data.
An AI system can retrieve a customer record, read a contract and query a transaction database. It can still give the wrong answer if it does not know which customer entities belong together, which contract version governs, or which transaction status the business considers final.
The enterprise doesn’t have a shortage of data. It has a shortage of shared context.
Consider a seemingly simple question: How profitable is this customer? Sales may define the customer as a relationship. Finance may report by legal entity. Operations may group accounts differently. Each system can produce an accurate number according to its own rules. An AI assistant with access to all three may present one of those numbers confidently without knowing which definition the question requires.
The problem becomes harder when the answer needs action. A system may identify an exception, but who owns it? Which policy applies? What happened the last time a similar exception arose? When should a person review the decision? Much of that knowledge lives in workflows, conversations and the experience of employees rather than in a field labeled “context.”
This is why connecting an AI model to more databases is only part of the work. The enterprise needs a way to establish meaning: definitions, relationships, authority, business rules, exceptions and history. It also needs to know when that meaning is uncertain.
Without that context, AI can be technically impressive and operationally unreliable. It may answer the question it was able to interpret rather than the question the business actually asked.
The opportunity is substantial. When AI can understand how information relates across systems and how decisions are made, it can help people spot problems earlier, explain what matters and act within the right controls. That is a more useful goal than accumulating another hundred isolated pilots.
So where do you begin?
I would start with a consequential business question or decision, then trace what someone must know to answer it correctly. Where do definitions conflict? Which facts are trusted? Who owns the decision? What exceptions require judgment? That exercise exposes the missing context and gives the enterprise a practical place to improve it.
Many technology providers already supply valuable pieces of this picture. The choice is not simply a black-box product or a ground-up build. Once the business context gap is clear, an enterprise can decide what to build, buy—or increasingly, assemble from capabilities it already has.
In the next article, I’ll look at how to identify those gaps in one real workflow and make progress incrementally, without waiting for an enterprise-wide transformation.