The useful way to think about AI in analytics is as part of a workflow rather than a prompt box.
1. Frame
Define the business question, decision, metric, population and constraints.
2. Prepare
Understand sources, grain, definitions and data-quality risks before generating analysis.
3. Build
Use AI to accelerate SQL, Python, exploration, documentation and alternative approaches.
4. Verify
Test calculations, joins, assumptions, edge cases and outputs independently. Important numbers should reconcile to something you trust.
5. Explain
Separate facts from interpretation. Communicate what changed, why you think it changed, how confident you are and what should happen next.
6. Systematize
Save useful queries, checks, templates and decision rules so the next analysis starts faster without repeating the same mistakes.
The workflow in one line
Question → context → AI-assisted execution → validation → decision.
For a detailed implementation, see the question-to-insight workflow.