Can your AI prove how it got there?

The distance between what an AI system can tell an organization and what the organization can establish about how that conclusion was reached. It opens when sources cannot be identified, when transformations disappear inside a workflow, when important definitions are implicit, when generated claims become detached from their supporting calculations, and when a final presentation survives but the work behind it does not.

Isn't this what citations and conversation history already solve?

A citation shows that a document was retrieved; it does not establish the role that document played in the result. A conversation preserves the interaction, not the queries, the records returned, the calculation logic, the business definitions, the intermediate results or the assumptions applied. Enterprise analysis should not depend on reconstructing a process from the final chat.

How is auditability different from explainability?

Explainability asks whether a result can be described. Auditability asks whether the evidence behind the result can be inspected. A language model can explain an answer fluently after the fact, and fluency is not evidence. What matters is the observable analytical process — not a model's internal chain of thought.

Doesn't logging everything solve it?

Auditability is not a data-retention contest. Logging records events; lineage preserves the meaningful relationships between them — this query produced this metric, this methodology defined this calculation, this calculation supports this claim, this reviewer changed this conclusion. An auditor does not need four million system events; they need the chain that matters.