A document automation pilot should show whether a defined document family can reach a usable next step with less routine handling and an effective exception path. Extracting plausible text is only one part of that workflow.
This guide proposes a practical evaluation method. It does not report customer results or assume a universal accuracy threshold.
Define the document family and destination
Choose the documents, fields, and handoff the first release will handle. Record expected formats, scan quality, layout variation, languages, and the destination schema.
Identify who owns a document when processing fails or information is disputed. A correct extraction that never reaches the right review queue is not a completed business task.
Build a representative sample
Include ordinary documents as well as poor scans, unusual layouts, missing pages, empty required fields, duplicate submissions, and unsupported types. Use sanitised or approved samples rather than introducing sensitive material without an agreed handling policy.
Separate the examples used to improve the workflow from the set used for the release decision. Preserve the original sample and record the version of the processing configuration, so results can be compared later.
Establish verified field values
Have reviewers record the expected value for each evaluated field and agree how to treat formatting differences, missing data, and ambiguous evidence. Use a second review where an error could materially change the business outcome.
If reviewers cannot agree on a value from the source, do not label the model wrong or right without documenting that ambiguity. The workflow needs a review decision.
Use confidence as one input
Amazon Textract's documentation advises considering confidence scores together with the sensitivity of the use case. It also notes that input quality affects results. This supports choosing review thresholds for the particular task rather than treating one score as a universal acceptance rule.
Add deterministic checks for required fields, formats, allowed values, and business relationships where appropriate. A high-confidence extraction can still fail a business rule. A low-confidence result can be useful when presented to a reviewer with its source.
Measure fields and complete documents
- Field-level accuracy: how often does each extracted value match the verified value under the agreed scoring rule?
- Important-field accuracy: report consequential identifiers and amounts separately from less critical fields.
- Document-level acceptance: does the document contain everything needed for the next approved step?
- Review rate: what share of documents requires a person, and for what reasons?
- Correction effort: how long do reviewers spend finding and fixing errors?
- Downstream completion: do approved outputs reach the destination without duplicate writes or rejected records?
An average across fields can hide errors in a small set of important values. Report results by document family, layout, and input quality, with sample counts, rather than presenting only one overall percentage.
Exercise the exception path
Test missing information, conflicting values, unknown document types, failed destination APIs, and repeat submissions. Check that the reviewer receives the original document, flagged fields, validation results, and a clear decision to make.
Keep approval separate from extraction. Reading an invoice amount does not authorise payment, and classifying a document does not establish compliance. The system's allowed actions must match the operational policy.
Compare time and cost per usable outcome
Measure the current process and pilot under comparable conditions. Include intake, extraction, validation, review, retries, storage, and integration handling when calculating cost per document.
Also measure turnaround time and queue age. A workflow that extracts quickly but creates an unowned review backlog may move the delay rather than remove it.
Agree the release decision before testing
Write down required checks, acceptable review effort, important-field requirements, and operational responsibilities before scoring the pilot. Decide which failures prevent launch and which document types remain outside scope.
An expansion can be justified when the selected workflow produces usable outputs, exceptions are owned, and the measured cost is supportable. A decision to narrow the document family or improve scan quality can also be a useful pilot outcome.
Download a pilot evaluation template
Use this editable JSON template to record cases, expected checks, review evidence, and baseline/pilot measurements. Its six examples are synthetic planning fixtures; add representative approved material and agree release criteria for your own workflow.
- Download the Document Automation evaluation template (JSON).
- Download the evaluation instructions for scoring, pending cases, and measurement rules.
Sources and further reading
- AWS: Amazon Textract best practices discusses input quality and interpreting confidence in context.
- OpenAI: evaluation best practices provides guidance on representative evaluation cases and continued testing.
Related Plexnova AI services
- Document Automation describes implementation scope and human review boundaries.
- Workflow previews provides examples of the intended operating experience.
- Discuss a document automation pilot with representative samples, fields, and approval rules.