The Wrong Question
Most enterprise AI governance conversations start with "which model" and "how do we prevent hallucination." Both are the wrong first question. An agent that retrieves the wrong version of a pricing table and states it confidently isn't hallucinating — it's accurately reporting bad data. No model choice fixes a lineage problem.
What Lineage Actually Means Here
Data lineage, in the context of agentic AI, means being able to answer three questions for any output an agent produces: which system did this fact come from, when was it last updated, and who is accountable for its accuracy. Most enterprises can answer this for financial reporting, because auditors require it. Almost none can answer it for the data an AI agent pulls into a customer-facing answer.
Why This Gets Skipped
Lineage work is unglamorous and doesn't demo well. A retrieval-augmented generation pilot can look impressive in a steering committee with three weeks of clean, hand-picked source documents. It falls apart in production against the actual mess of duplicate folders, three versions of the same policy document, and a data warehouse where "customer segment" means something different in two divisions.
The Governance Model That Scales
Model risk management borrowed from financial services is a better governance template for agentic AI than most AI-specific frameworks being pitched today: a model inventory, a defined owner per model, a documented data source per output, and a review cadence tied to how much authority the model has — an internal drafting assistant needs less oversight than an agent that can adjudicate a warranty claim or issue a customer-facing quote.
Where to Start
Before scaling any agentic AI initiative past a pilot, build the lineage map for the three data sources it depends on most. If that map can't be built in under two weeks, the initiative isn't ready to scale — it's ready for a data cleanup project that should happen first, and will make every AI initiative after it faster to trust.
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