The problem
High-volume document work resists automation for a specific reason: the documents were never designed to be machine-readable. Layouts vary between senders, scans are imperfect, and the same field appears under a different label on every form. Traditional template-based extraction breaks on the first unfamiliar layout, which is why so much of this work is still done by people reading screens.
How we approached it
Treat extraction as a confidence problem rather than a correctness problem — every extracted field carries a score, and the pipeline routes on that score instead of assuming success.
Automate the highest-volume document type first and completely, so the saving is measurable before the scope widens to the long tail of rarer formats.
Keep a human review queue as a designed part of the system, not a fallback. The goal is removing the routine cases from a person's day, not removing the person.
Make the workflow rules data rather than code, because they change more often than the extraction does.
Where it landed
Routine documents are processed without a person reading them, exceptions are surfaced as a short review queue rather than hidden in a backlog, and the volume the system can absorb is no longer bounded by how many people are available to read.
No percentages or dollar figures appear above. These are our own products, and the structural changes described are checkable; a number attached to them would not be. We would rather publish the former than invent the latter.