From inbox to system of record: designing AI document workflows.

A convincing demo can read an invoice. A dependable document workflow must also identify the document type, extract the required fields, validate them against business rules, find duplicates, route exceptions, update a destination system, and preserve the original evidence.

Design from the final system of record backwards. Define exactly which fields are needed, their types, and what makes a value acceptable before choosing the model or OCR layer.

Structured outputs reduce formatting ambiguity by requiring the model response to follow a schema. Business validation still belongs outside the model: totals should reconcile, dates should be plausible, suppliers should match known records, and identifiers should satisfy format checks.

A workflow that automates 80 percent cleanly and presents the remaining 20 percent well can be more valuable than one that claims full autonomy and silently writes errors. Reviewers need the source, extracted value, validation failure, and recommended correction in one place.

Track straight-through processing, correction rate, time per exception, duplicate prevention, and downstream posting errors. Those metrics reveal whether the complete operation improved.