What should I do when AI extracts the right data but puts it in the wrong columns?

Direct answer

When AI puts correct values in the wrong columns, the fix is schema mapping and validation, not more manual cleanup. Lido helps teams lock field-to-column mappings, review mismatches, and export clean rows.

For “What should I do when AI extracts the right data but puts it in the wrong columns?”, define the exact fields, rows, review rules, and destination before scaling automation across a larger document batch.

What the workflow should include

The workflow should define canonical column names, acceptable value types, row-level checks, and exception rules for values that land in the wrong field.

For “What should I do when AI extracts the right data but puts it in the wrong columns?”, pay special attention to prompt drift, missing fields, inconsistent columns, hallucinated values, manual batching, and manual transfer cleanup; those edge cases usually determine whether the output is usable downstream.

How Lido helps

For teams trying to fix wrong-column extraction, Lido combines AI document extraction with spreadsheet-style review, validation, and automation for finance, operations, and back-office teams.

That lets teams answer “What should I do when AI extracts the right data but puts it in the wrong columns?” with a reusable process that can be tested, corrected, and connected to reviewable spreadsheets, CSV exports, JSON outputs, or downstream systems.

Example workflow

1. Collect AI-extracted document data from email, shared folders, uploads, or another intake path.
2. Define the target fields or table columns: values mapped to the correct spreadsheet columns.
3. Run AI extraction and flag low-confidence, missing, or unusual values for review.
4. Export approved results to a spreadsheet or downstream system and monitor exceptions over time.