A board reviewing $37 million of investment exposure needs more than just confidence that the figure is accurate. It needs to understand what the exposure comprises, how the components connect to each other and whether those connections create dependencies that warrant action. A report can account for every dollar and still leave all of it open. The number is rarely the problem.
Organizations close that gap with people. An analyst supplies the context the report leaves out. An architect spots a dependency between two seemingly unrelated activities. An executive remembers why an exception was approved four years ago. Little of that reasoning survives the meeting, and AI does not remove the need for it. It removes the pause in which it used to happen.
The data–information–knowledge–wisdom model distinguishes between having data and exercising judgment. However, an enterprise needs something it can inspect decision by decision, and that takes finer distinctions: Data → Context → Information → Composition → Relationships → Meaning → Knowledge → Understanding → Judgment → Decision → Action → Outcome → Learning.
When a company doesn't understand its data, AI can't save it. Consider the number 37. On its own, it is merely data. Say that it represents millions of dollars of investment exposure: that adds context. Add the reporting date, the currency and the scope, and it becomes usable information. Its composition is three positions: $15 million with Counterparty A, $12 million with Counterparty B and $10 million with Counterparty C.
Suppose that A and B depend on the same critical supplier. That single relationship ties $27 million, roughly 73 per cent of the total, to one point of failure. The arithmetic has not changed, but the meaning has, because what appeared to be three separate positions holds a concentration.
The Importance of Ontology
Ontology is part of the structure that holds this together. In Tom Gruber’s formulation, an ontology is “an explicit specification of a conceptualization”: what counts as a legal entity, how a contract creates an obligation, how several entities can depend on one supplier.
Every enterprise already operates on such distinctions, whether or not they are formalized in an ontology. Sales treats a customer as a commercial relationship, Finance as a billing account, Legal as a contracting entity. All three are correct, and software designers recognize this as Eric Evans’s bounded contexts.
Forcing a single universal definition destroys the distinctions that people need in order to do their jobs. The job of the enterprise, and of its architects, is to build the translation between those boundaries on purpose.
AI raises the cost of getting this wrong. One assistant reads the exposure correctly and misses the shared supplier. Another identifies the dependency and tests it against a policy that was replaced last quarter.
The practical test is to start with one decision that incurs real costs and keeps requiring manual interpretation or reconciliation. Bring together the people responsible for it, the domain specialists and the teams who build and run the systems, and work through actual cases.
This article was written with the assistance of AI.
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