Building an AI analytics copilot requires careful consideration of its decision boundaries to prevent dangerous product outcomes. Developers should establish a "metric contract" that clearly defines each metric's owner, definition, data source, and limitations. The copilot should be evaluated on its ability to correctly select metrics, apply filters, cite sources, and refuse to answer when data is insufficient or stale. A phased rollout, starting with read-only aggregate metrics and progressing to row-level data after rigorous access control and auditing, is recommended to build trust and ensure responsible AI usage. AI
IMPACT Establishes a framework for developing trustworthy AI analytics tools by emphasizing clear metric definitions and controlled data access.
RANK_REASON Article discusses best practices for building an AI analytics copilot, focusing on product development and decision boundaries, rather than a specific release or research.
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