Review between established facts and published action

Automation can collect, aggregate, compare, and draft. People should verify completeness, definitions, anomaly context, and high-risk recommendations. A practical flow generates a draft, applies a fixed review sheet, publishes, and feeds rejection reasons back into rules.

Check whether data is publishable

Display the data cutoff, successful batches, missing sources, and records still pending. Orders, ads, inventory, and refunds often arrive at different times. Define minimum completeness and latency by report purpose rather than hiding differences under “today.”

Separate evidence from recommendations

Review metrics, filters, comparison periods, and rounding, then investigate promotion, stockouts, one-time orders, or collection failures. AI can propose hypotheses but should not present correlation as confirmed cause. BI0 report and alert boundaries require project confirmation.

Measure the review operation

Track generation time, review time, rejection reasons, delayed-data share, and action completion. If every report needs new extraction, improve data contracts; if reviews pass consistently, expand gradually.

Public references

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