Why Provenance Graphs May Become Core Infrastructure for Enterprise AI
When an enterprise AI system flags a decision — a transaction as fraudulent, an emissions disclosure as anomalous, a supplier record as non-compliant — the prediction is rarely the hard part. The hard part is the question that lands immediately after: why? And beneath that, a harder one still: can you prove, deterministically, how you arrived there — six months from now, in front of an auditor, against the exact state that produced the decision?
Most production AI systems cannot. They persist predictions; they do not persist lineage. The model records that record 48,113 scored 0.91 on the anomaly axis, but the system holds no first-class representation of where that record originated, what transformed it, which upstream entities influenced it, and how that influence propagated downstream. The provenance technically exists — scattered across logs, join tables, and pipeline code — but it is not a structure you can traverse...
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