How to Verify AI-Extracted Data Before You Trust It: A Practical Checklist
A six-point checklist for catching AI extraction errors before they reach your books, totals math, ambiguous dates, row counts, and more.
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- **How to Verify AI-Extracted Data Before You Trust It: A Practical Checklist**
- AI extraction tools are good at turning a PDF or photo into a structured table, but "good" isn't "perfect," and the documents that matter most (invoices going into your books, statements you're reconciling, contracts with dollar amounts) are exactly the ones where a silent error is expensive. The fix isn't to distrust AI extraction wholesale; it's to build a quick, repeatable verification habit before the data leaves your review screen. Here's a practical checklist.
- **1. Check the totals math, not just the individual fields**
- If a document has line items and a stated total, add up the line items yourself (or let your spreadsheet do it) and compare against the extracted total field. A mismatch is the fastest signal that something was missed, duplicated, or misread, far faster than eyeballing every row individually.
- **2. Spot-check the fields most likely to be misread**
- Not all fields carry equal risk. Dates in ambiguous formats (03/04/2026, March 4th or April 3rd?), currency symbols mixed with commas as thousands separators, and small or faint text (footnotes, stamped amendments) are the classic failure points. Scan these specifically rather than reading the table top to bottom uniformly.
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