How to Use AI to Extract Data from Customer Order Documents

To use AI to extract data from documents, first define the order details you need, choose a tool that suits the files customers send, then check the extracted values against your customer and product records. Keep uncertain details out of the order system until staff have resolved them.

A tidy set of fields is not proof that the document was read correctly. A product code can look convincing but refer to nothing in your catalogue. A quantity can be a valid number and still be wrong.

Use AI to propose what a document says, not to decide which customer or product it must mean. The workflow below applies to customer order forms and PDF order requests, from arrival through to an approved order.

Start with the order documents you actually receive

I would first collect a representative set of documents and ask the staff who enter orders to mark the details they copy, the checks they make and the questions they send back to customers. Include awkward examples, not only clean forms from regular customers.

Separate the files into three groups:

• Fillable PDFs: values are stored in form fields and may be readable directly, without AI.
• PDFs with selectable text: text can be extracted, although connecting a quantity to the correct product row still needs care.
• Scans and photos: the document must first be read from an image. Optical character recognition, or OCR, turns visible characters into text.

Also note how often layouts change, whether orders span several pages, and whether customers add handwritten changes. These differences determine the extraction method and the review work.

Choose the simplest extraction method that fits

If customers send the same fillable PDF, start by reading its fields. If the text and layout are stable, fixed rules may be sufficient to identify the order number and line items. Both approaches avoid asking an AI model to interpret information that already has a dependable structure.

AI becomes useful when customers express the same order details in different layouts or wording. It can help turn varied documents into a consistent set of fields. Scanned documents may need OCR alongside that interpretation.

For example, Google Document AI's extraction documentation describes tools for key-value pairs, tables and selection marks, as well as custom extractors with user-defined fields. Those capabilities are a starting point for testing, rather than evidence that a particular customer form will be read correctly.

The useful boundary for AI in document workflows is interpretation: identifying what appears to be a customer reference, requested date or order line. Exact record matching and permission to create an order should be separate checks.

Define the order fields before configuring extraction

Write an extraction brief that tells the tool what to return and tells staff what an acceptable result looks like. This field structure is often called a schema. It should reflect what your order system actually needs.

• Order details: customer name as written, customer account reference, customer order number, delivery address and requested delivery date where needed.
• Each order line: product code as written, description, quantity and unit, such as each, box or case. Keep each quantity attached to its original line.
• Missing or unclear information: return a missing or uncertain value rather than completing it with a guess.
• Source evidence: retain a link to the original document and, where supported, the page and highlighted area for each extracted value.
• Separate values: keep the text read from the document distinct from any customer or product record selected later.

Do not silently turn “two boxes” into a quantity of two individual items. If a conversion is needed, it should use an approved product rule. Likewise, an absent delivery date should not become today's date unless the business has explicitly agreed that default.

Document text should be treated as order content, not as instructions that can change the extraction rules or bypass approval.

Build the workflow from receipt to approved export

Receive and register the document

Give each incoming submission a tracking reference. Record its arrival, source file and processing status so staff can tell whether it is waiting, under review or already exported.

Check for repeated submissions before creating anything. A file fingerprint can flag an identical attachment; customer account and order number can help flag the same order arriving in a different file. A repeat may also be an amendment, so route unclear cases to staff rather than deleting or importing them automatically.

Extract, then validate against business records

Check required fields, date formats, quantities and units. Then look up customer references and product codes in the existing records. A unique, exact match can pass the matching check; missing, unknown or multiple matches need review.

A valid code does not prove it was read correctly. A misread character might produce another genuine product code. Compare the description where useful, show the source to reviewers and test for these errors during the pilot. Any suggested alternative should be clearly labelled as a suggestion, not silently substituted.

Confidence scores can help prioritise review, but they are not proof of correctness. SAP's extraction guidance describes field-level confidence and recommends mass testing before important or time-sensitive use. Test review thresholds on your own documents and critical fields.

Give staff a review queue that holds back unsafe orders

The reviewer needs to see the document beside the proposed order, with missing values and failed checks clearly marked. They should be able to correct a value, select a verified record, ask the customer a question or reject the submission.

Review must stop export, not merely send someone a warning after the order has been created. Salesforce's document extraction action, for example, documents a flow that pauses while human review is completed. Whatever tool you choose, demonstrate this behaviour before relying on it.

Name the person or team responsible for the queue, including who handles customer queries. Record corrections and approvals so staff can explain how the final order differs from the initial extraction.

Export once, and record the outcome

Only approved information should create or change an order. Store the resulting order-system reference against the submission. If the connection fails or times out, check whether the order was created before retrying; a retry must not create a second order.

Start with draft orders or staff-approved exports. Consider automatic release only for document types and checks that have passed the pilot, with unresolved exceptions still held for review.

A worked example: unclear quantity, unknown code and a repeat

Illustrative example: a customer sends a scanned order with account reference C104 and order number PO-782. One line contains product AB-410 with a quantity that could be 6 or 8. Another contains code ZX-19, which does not exist in the catalogue.

The customer reference matches one account. That check passes, but the order remains blocked. The first line needs a confirmed quantity. The second needs a verified product reference, even if its description resembles a stocked item.

A staff member checks the scan, confirms the first quantity where it is legible, and asks the customer to clarify anything the document cannot establish. They obtain confirmation of the second product rather than accepting the model's plausible alternative. The approved values and the reasons for correction are recorded.

If the same attachment arrives again, the workflow flags the repeated submission and links it to the existing order. If a different document arrives with the same order number, staff determine whether it is a duplicate or a revision. Neither event should automatically create another order.

Set document-handling rules before the pilot

Before uploading customer documents, check the proposed service's terms and settings for retention, deletion, access, processing location and whether submitted content may be used for model training. Include any stored files, extracted results and processing logs in that check. Do not assume every product or account setting handles them alike.

Decide who may view originals and corrections, how long documents are needed for order queries, and how deletion will work across the extraction tool and your own storage. Use redacted examples for initial experiments where practical.

Test errors and review effort before allowing live orders

Run a pilot against documents that staff have already checked. Include varied layouts, poor scans, multi-page orders, units, unknown codes, amendments and duplicates. Keep a separate test set that was not used to adjust the extraction setup.

Measure errors by field, not simply by whether the document produced an output. Track incorrect customer matches, wrong product codes, missed lines, quantity and unit errors, and missing values that were filled in incorrectly. Also measure staff review time, how often customers need clarification and whether duplicates are caught.

Agree acceptance criteria before seeing the results:

• Which errors always block export, regardless of confidence?
• What observed error level is acceptable for each field and document type?
• How much review work can the responsible team handle?
• Can every exported order be traced to its source, checks and approval?
• Do repeated submissions and failed exports recover without creating duplicate orders?

If the pilot reads most fields correctly but leaves quantities unreliable, keep quantity review mandatory. If one customer's stable form works well, release that route first rather than treating all documents as equally safe.

Successful extraction means the tool returned data. A usable order means the required details have been checked, uncertainty has been resolved and the export can be traced. Build and test both parts before removing the retyping step.