AI Spreadsheet Cleanup Without Changing Its Meaning

Before an AI agent cleans a spreadsheet, define what each column means and which transformations it may apply. Preserve the original and ask for uncertain cells separately so the owner can resolve exceptions before accepting the cleaned copy.

The following examples are hypothetical. A semantic change alters what a value represents, rather than only how it is displayed. Use the table to decide which column rules the agent can apply automatically and which cases should enter an exception record.

W3C’s tabular data model distinguishes a cell’s string representation from its semantic value, which can depend on annotations such as datatype. This supports recording interpretation rules alongside the cells rather than assuming the text is self-explanatory. The standard does not define the business meaning of your spreadsheet.

Python’s CSV documentation provides a concrete loss example: its writer converts None to an empty string and notes that the transformation is not reversible. That is a specific library behavior, not a claim that every export treats missing data the same way.

Record each column’s type, unit or locale, missing-value convention and allowed transformations. A customer identifier remains text even when all its examples contain digits; numeric conversion can discard meaningful leading zeros.

For a hypothetical date column containing “03/04/2026,” neither the agent nor a spreadsheet’s default locale can establish the intended order from that cell alone. Use the source specification or ask the owner. If evidence remains absent, keep the original text and flag the cell rather than choosing the more familiar interpretation.

For units, distinguish a display label from a conversion. Adding a currency symbol does not perform an exchange-rate conversion. A conversion needs an explicit rule and provenance; this article does not recommend rates or financial assumptions.

An exception record should identify the source row, original cell, proposed change, reason for uncertainty and owner decision. Stable row identifiers are important because sorting can change row positions. If the sheet lacks a suitable identifier, preserve a source-row reference before rearranging it.

For duplicates, define the entity and the evidence required to merge rows. Two identical names can describe different people; two different spellings can describe the same organization. Treat the matching decision separately from removing repeated whitespace.

The ask-instead-of-guessing guide helps set escalation thresholds. A known, authorized column rule can run without repeated questions. Missing evidence about the meaning of a cell belongs in the exception record.

Compare the original and output by stable row reference. Check which cells changed, why they changed and whether the rule was authorized. Row counts and totals can catch some mistakes, but equal totals do not prove that values stayed attached to the right entities.

Where formulas matter, inspect both the expression and the result in the intended application. Replacing a formula with its displayed value can preserve today’s appearance while removing future recalculation. Our file-output acceptance reference covers checking the actual delivered copy.

Use the test-data reference to build a small controlled example with leading zeros, ambiguous dates and explicit missing values. Passing that example checks the specified cases. It does not prove the full workbook is clean; retain the output comparison and unresolved exceptions for the real acceptance decision.