Rejections are a diagnostic
Post-AI rejections almost always come from the same handful of causes. Diagnose them with the four-technique lens.
Extraction is failing
Check the source documents. Photographs of paperwork, mixed scans, and non-standard layouts kill extraction accuracy. Send suppliers a template. Push for digital-native invoices.
Validation is thin
If your platform validates format but not TSS-specific consistency and caps, you will see server-side rejections. Ask your vendor for the full rule catalogue and add missing checks.
Classification is being auto-accepted
Model suggestions with high confidence are still suggestions. If nobody is confirming first-time products, expect challenges. Use BTI or ATaR where legal certainty is needed, as covered in the BTI vs ATaR classification guide.
Reuse is carrying forward stale data
Products drift. A material change or a component swap can move the commodity code. Version product records and force review on material changes.
People are being bypassed
If low-confidence fields are auto-submitted because the review queue is under-staffed, you have a workflow problem, not an AI problem.
Data quality upstream is unresolved
"Assorted parts" will fail every time. Fix the description at source, then reprocess.
Peak surges are unmanaged
Deadline weeks expose fragile queues. Pre-warm reviewers and run rehearsals.
Run a diagnostic on your own flow. Watch a demo.
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