AI makes it cheap to produce an analysis. It does not make the analysis correct. A quality-control step is what separates a useful draft from a deliverable you can defend.

The 10-point AI analysis quality-control checklist

1. Question: Does the work answer the actual business question?
2. Metric: Is the metric definition explicit and consistent?
3. Grain: Do you know what one row represents before and after each transformation?
4. Source: Are you using the authoritative dataset?
5. Joins: Could any join duplicate or remove records unexpectedly?
6. Filters: Are date, status, population and exclusion rules correct?
7. Totals: Do important results reconcile to a trusted source or independent calculation?
8. Edge cases: What happens with NULLs, zero values, duplicates, late records and unusual categories?
9. Evidence: Are observations clearly separated from assumptions or causal claims?
10. Recommendation: Does the action actually follow from the evidence?

Do not run the checklist only at the end

Quality control works better as a loop. Validate the source before analysis, validate transformations while building them, and challenge the interpretation before communicating it.

A useful AI quality-control prompt

After doing your own analysis, give AI the relevant context and ask it to act as a hostile reviewer: identify assumptions, possible join problems, missing segments, alternative explanations and claims that are stronger than the evidence. Then decide which criticisms are valid.

What “done” should mean

For important work, done should mean more than “the code ran.” You should be able to explain the question, data, method, validation, result and limitations without opening the AI chat.

If you want the shorter pre-trust version, see 7 checks to run before you trust AI-generated analysis.

Build the full workflow with AI-Assisted Analyst on Amazon.