Arabic document AI: a business pilot for invoices and forms

Start with one document family

Choose a repeatable queue, such as supplier invoices with known layouts. List the fields your team needs and the errors that matter: supplier identity, currency, dates and totals. Keep extraction separate from approval and payment.

What the new UAE release offers

TII describes Falcon-OCR-Arabic as extracting Arabic text and structured content from documents and images. That makes it a candidate for an Arabic document pilot, not a guarantee of accurate extraction on your scans. Compare it with the incumbent process on the same examples.

Make every extracted field checkable

Microsoft’s insurer example illustrates why extracted values need a link back to the source document. Give reviewers the page and field location alongside the answer. Test whether confidence flags concentrate errors on your own documents before using them to reduce review.

A practical pilot plan

The following is a suggested plan, not a promised implementation time or measured return.

• Define: select document types, mandatory fields and an approved, de-identified evaluation set.
• Compare: run today’s process and the candidate on the same held-out documents.
• Measure: record field accuracy, missed critical errors, reviewer time and total cost per accepted document.
• Decide: expand only if agreed accuracy and cost targets are met; retain a manual queue for exceptions.

Calculate the real saving

Compare the baseline cost per accepted document with the new workflow’s model, hosting, integration, review and correction costs. Multiply the difference by realistic volume, then subtract setup and ongoing maintenance. A high OCR score alone is not a business case.