AI document processing: which data can you safely carry forward?

OCR, recognition and processing do different jobs

OCR (Optical Character Recognition) recognises letters and numbers in images or scans. Extraction puts information into fields such as an order number or quantity. Classification identifies the document type. IDP connects these steps with the wider processing workflow.

A digital file with structured fields can often be imported directly. Do not turn available structured input into a PDF just to read it again with AI. With variable layouts, investigate which recognition steps are needed. Not every document task requires a generative language model.

Explore how document processing fits within Intelligent Automation.

Start with document types, not every file at once

Collect examples of the document supporting one downstream action. Note senders, languages, layouts, attachments and poor scans. Keep out-of-scope documents separate. An order confirmation and a delivery note can contain the same references but mean different things.

Define each field: name, format, meaning and source. Is a date the order date or the planned delivery date? Are quantities units, boxes or pallets? Decide whether a blank is acceptable and who requests missing information. Do not let the system silently fill gaps.

For invoice approval and order handling, use the dedicated guides: automating invoice processing and processing orders from email.

Example: preparing a delivery note for a receipt check

Consider a fictional delivery note with order reference PB-204, item A17 and “2 boxes”. The purpose is to prepare information for someone checking goods received. The document itself does not prove that the goods have arrived.

On a narrow screen, scroll the table sideways. With a keyboard, focus the table and use the arrow keys.

Reading “2” correctly is not enough. Its unit, process meaning and the checks determine whether the next action is appropriate. Also record whether the same document has already been submitted.

Make uncertainty and exceptions workable

Show the original page beside the proposed field. Explain why review is needed: an unknown item, missing field or unexpected unit. The reviewer must be able to correct, return or stop a case, not just approve it.

A confidence score is an extraction service's estimate, not approval of a transaction. Assess any threshold against your examples and the consequences of errors. A good average can hide errors in an important field.

Agree who handles exceptions and what happens when that person is absent. Consider the work left over: collecting documents, requesting clearer copies, reviewing proposals and correcting mistakes. Include it when deciding whether automation is worthwhile.

What do you need for a first trial?

1. One document stream and one objective. For example, prepare delivery-note data without automatically booking goods received.
2. A varied test set. Normal documents, unusual layouts, missing fields, duplicates and the wrong document type.
3. Expected answers. A person records the known values and the conditions for stopping each case in advance.
4. An approved environment. Check which data may be used, who has access and how documents and derived data are handled.
5. An owner for the next step. Someone decides which errors block progress and who handles maintenance, checks and recovery.

Start in a separate test environment with fictional or carefully selected data. Removing a name does not automatically anonymise a document. Check which business data you can use in an AI tool.

Evaluate the whole handover, not just extraction

Compare the expected fields with the proposal and eventual processing for each document. Record missed fields, incorrect values that were not stopped, correction work and review time. Test a repeated document and a missing acknowledgement too. A small trial exposes issues; it does not prove performance for every supplier or document variant.

An integration must fit the receiving system's capabilities. Check existing import or API options before adding screen-based steps. Choose a product only after the document types, checks and handover are clear.

Use the checklist to scope your trial or compare APIs, RPA, workflows and AI.

When is a simpler solution enough?

For a small number of documents, a fixed layout or an existing import, a better form, template or software feature may be more useful than a new AI solution. Variable input and recurring investigation make a trial more relevant, but do not replace a cost assessment.

Do you mainly want to find answers in manuals rather than transfer fields to a system? That is a different task. Explore an internal AI knowledge base with source checks.