AI for data analysis is one of the most misunderstood AI use cases. AI is excellent at narrative analysis and synthesis. It is mediocre at large-scale data processing. Knowing the difference saves you from buying the wrong tool. Here is the practical guide.
AI excels at: Narrative analysis of structured data ("explain what these numbers show"), pattern synthesis across qualitative + quantitative data, summarizing reports, writing the narrative behind dashboard data.
AI is mediocre at: Large-scale data processing, complex SQL generation, statistical modeling, real-time data manipulation.
For deep data work, use dedicated tools. Hex, Mode, Looker, Tableau, Excel. AI complements them, does not replace them.
1. Narrative around dashboard data. "Here are this month's metrics — write the executive summary." Claude excels.
2. Spreadsheet formula generation. Describe what you want; AI writes the formula. Excellent for non-technical analysts.
3. Survey data synthesis. Open-ended survey responses → themes + counts → narrative.
4. Cross-source pattern detection. "Here are NPS scores + ticket volumes + churn data — what patterns connect them?"
Asking AI to do real statistical analysis without verification. AI can sound confident with bad math. Verify the actual calculations.
Treating AI like a database. "How many customers do we have in Texas?" — that is a CRM query, not an AI prompt.
Relying on AI for large dataset processing. Context windows have limits. Use proper tools for big data.