Extracting structured data from documents
A schema and deterministic validation can bound extraction. Expected disposition: Proceed with controls.
Task boundary
Extracting structured data from documents is assessed as a specific task and workflow, not as a general endorsement of AI for the surrounding job. A schema and deterministic validation can bound extraction.
Assumptions
- Documents come from a defined family of layouts and the required fields have a typed schema.
- Extraction writes to staging rather than overwriting the source or system of record.
- A reviewer can inspect the source image or text beside uncertain values.
Decision
- Disposition: Proceed with controls
- Pattern: Tool workflow
- Inherent risk: medium
Rules that drive the decision
- Schema-constrained extraction favors a tool workflow with deterministic validation.
- Downstream record changes require preview and review; prose output is not an adequate contract.
Why alternatives were rejected
- Direct prose output is hard to validate.
- Unreviewed writes can corrupt downstream records.
Controls
- Validate types, required fields, date formats, totals, and cross-field relationships.
- Preserve page and region provenance for every extracted field.
- Escalate unreadable, unseen-layout, and conflicting values instead of filling gaps.
Acceptance threshold
These are example starting thresholds for a bounded pilot of this workflow, not universal benchmarks. For extracting structured data from documents, the accountable owner should make them stricter when the task, consequence, or policy requires it.
- On the representative set, all required fields meet the owner-defined field-level accuracy and every failed validation reaches review.
- The set includes rotated scans, blank fields, duplicate pages, handwriting, and conflicting totals.
Reassess when
- A new document family, language, or handwriting style enters the workflow.
- Staged values begin updating a system of record without per-record approval.