Why AI-generated business metrics can silently shift without anyone noticing

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AI assistants querying business data like Monthly Recurring Revenue can return technically valid but semantically inconsistent results when underlying business definitions change. Finance teams may quietly alter which plans are counted, how credits are applied, or which exchange rates are used, causing the same SQL query to mean different things over time. Experts recommend that production metrics carry immutable version identifiers covering filters, dimensions, timezones, source systems, and policy digests. Metric versions should also be embedded in cache keys, scheduled reports, and exports to ensure historical figures remain reproducible. The core principle is that AI models can retrieve and explain metrics, but business semantics must be explicitly defined and versioned by humans.

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