Ai AppliedDocuments are often where a business process slows down. A request arrives as an email, scan, PDF, or form; someone identifies its type, copies key details, checks them against other records, and enters the result into another system. AI can help, but extracting text is only one step. A useful document-processing system must carry the information through a dependable workflow.
Since this article first appeared in 2024, the model names have changed. The more durable design question is the same: which fields must be correct, what evidence supports them, and who handles exceptions?
Design the process around the document
- Receive and classify. Identify the document type, source, version, and whether required pages or attachments are missing.
- Read and extract. Use OCR, layout analysis, rules, or a language model to locate the fields and passages needed for the task. Keep a link to the source location for each extracted value.
- Validate. Apply business checks such as required fields, valid ranges, matching identifiers, totals, and consistency across documents.
- Review exceptions. Route missing, conflicting, or high-impact information to a person with the original document and proposed values side by side.
- Update the system of record. Send approved data to the relevant application, retain an audit trail, and make correction possible.
Cloud document tools can help with OCR, layout, extraction, and classification, but the workflow still needs business rules and review. The Google Cloud Document AI overview describes separate processor types for these tasks. Amazon Textract's guidance recommends interpreting confidence in light of the use case and sending uncertain results for greater scrutiny.
An insurance example, with the right boundaries
Consider an insurer receiving a claim form, supporting photos, repair estimate, and policy document. A system might identify the documents, extract dates and amounts, compare names and policy numbers, and prepare a case summary. That can reduce re-keying and help a reviewer see what is missing. It should not silently decide coverage or approve a claim simply because an extraction score looks high. The organization's rules, evidence requirements, and approval process determine what happens next.
The same pattern applies beyond insurance: onboarding packets, supplier invoices, loan applications, contracts, and customer service correspondence all mix structured fields with context that needs judgment.
Measure the workflow, not just the model
A demonstration can look impressive while still creating more correction work. Before a wider rollout, test representative documents, including low-quality scans, missing pages, unusual formats, and conflicting values. Track:
- Accuracy for each business-critical field, with errors broken down by document type.
- The proportion of documents sent to review and the time reviewers spend correcting them.
- End-to-end turnaround time and the rate of downstream rework.
- Whether staff can trace each result to the source and correct it later.
Security belongs in the same design. Limit access to source documents and extracted data, choose retention and data-location settings that meet your requirements, and record who approved a value before it changed a customer or financial record. Model and cloud selection should follow these constraints rather than precede them.
If your team is still copying information between documents and systems, explore our workflow automation approach or bring us one document-heavy process to assess.