AI in Spreadsheets and No-Code Databases
Classify, extract, and enrich rows at scale inside spreadsheets and Airtable-style databases — with spot checks so errors don't spread silently.
You have 1,200 free-text survey responses and a meeting on Thursday where someone will ask "so what are customers complaining about?" Reading them all takes two days. With an AI formula in your spreadsheet, you can tag every row by theme, pull out the feature each customer mentions, and flag the quotable ones before lunch.
The catch is that spreadsheets make errors invisible. A wrong label in row 847 looks exactly like a right one, and once it feeds a chart, nobody will ever look at row 847 again. In this lesson you'll learn which jobs AI columns do well, how to write column prompts that produce consistent output, and a pilot, validate, and spot-check routine that tells you how accurate your column actually is before anyone makes a decision from it.
What AI columns are good at, and what they aren't
An AI column is a column whose values come from a model: you write a prompt that refers to other cells in the row, and the tool runs it once per row. Four kinds of jobs work well:
| Job | Example | Why it works |
|---|---|---|
| Classify | Tag each survey comment as Billing, Bugs, Performance, Feature request, Praise, or Other | A fixed list of labels is easy to check |
| Extract | Pull the product feature mentioned in each comment, or the company name from an email signature | The answer is in the row; you can verify it's there |
| Normalize | Turn "NYC", "new york", and "New York City" into one consistent value | Small, well-defined transformation |
| Generate | Write a one-line product description from a name and attributes | Useful when a person reviews the output |
And three jobs they do badly:
- Math and counting. Don't ask an AI column to total, average, or count. Spreadsheets are perfect at arithmetic; models aren't. Have the model label each row, then count labels with ordinary formulas.
- Facts that aren't in the row. "Find this company's employee count" invites a plausible, invented number. Unless the tool explicitly searches the web and shows its sources, treat any fact not present in the input as a guess.
- Judgments that need the whole sheet. Each row is processed on its own. "Is this the most common complaint?" can't be answered one row at a time.