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AI data analysis, done responsibly
Used well, AI removes the mechanical parts of analysis. Used carelessly, it produces confident sentences about data it never saw.
Updated September 20, 2026
What AI is good at
- Describing structure: types, grain, keys, quality issues
- Generating code and formulas from a clear description
- Proposing hypotheses worth testing
- Turning a verified result into readable prose
What it cannot do for you
- Know your business definitions unless you state them
- Compute a number it was not given data for
- Judge whether a difference matters commercially
- Take responsibility for what you publish
A prompt that works
Here is a column profile and the first 20 rows of a sales export.
Grain: one row per order line. Currency: USD.
Question: which regions drove the change between Q1 and Q2, and is
any of it explained by a small number of large orders?
Only use the data provided. If you cannot tell, say so.Validation before sharing
- Reconcile one total against a source you trust
- Re-derive one claimed figure by hand
- Check the statement still holds after excluding outliers
- Note what the data cannot tell you
Frequently asked questions
Can AI replace an analyst?
No. It compresses the mechanical work and leaves the judgement — definitions, context and consequences — to you.
Does GridMind send my whole file to a model?
No. It sends a compact aggregated summary and a small sample of rows, which is what analytical questions need.
Related reading
Analyze your data with AI
Open the GridMind workspace, bring in a CSV, Excel file or your Power BI model, and let AI build the formulas, charts and dashboards for you.