Question 1

How do you recognize it?

Accepted Answer

Client pilot hits 85 to 92 percent field-level accuracy, then the rollout quietly stops before production cutover. A reviewer headcount line appears in the client's next budget draft even though the automation was sold as headcount reduction. Invoice or contract exceptions get routed to a shared inbox with no owner, so the same document type fails the same way each month. Nobody can state the cost per processed document, only the license cost per seat. The client asks for a second pilot on a different document type instead of expanding the first one.

Question 2

Why does it happen?

Accepted Answer

Accuracy is measured on a clean sample set while production traffic carries skewed scans, multi-language fields, and handwritten annotations that the pilot never saw. Confidence scoring exists inside platforms such as Instabase and Rossum, but agencies rarely wire a threshold to a human review queue, so low-confidence extractions pass through silently. The business case was written as a percentage improvement rather than hours and dollars per document, leaving no number to defend when procurement compares the line item to a seat-based alternative. Validation logic lives in the client's downstream system, not in the extraction layer, so errors surface days later in reconciliation instead of at intake.

Question 3

How do you fix it?

Accepted Answer

Re-baseline accuracy on 200 real production documents, not the pilot sample, and publish the gap to the client in writing. Set an explicit confidence threshold and route everything below it to a named reviewer with a 24-hour turnaround, then track the review rate weekly. Convert the value story to hours saved per 1,000 documents and dollars per document processed, using the client's own loaded labor rate. Ship one document type end to end, including exception handling, before adding a second type to the scope.

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