Intelligent Document Processing (IDP)

Short answer: Intelligent document processing, often called IDP, handles high-volume paperwork that staff would otherwise read and retype into another system. It combines text recognition with AI that understands layout and meaning. This lets it handle documents that vary in format, such as order forms from hundreds of different customers. The system checks the extracted data against rules. It then either sends the data on automatically or passes the document to a person for review.

Template-based tools break whenever a document's layout changes. AI-based processing can find fields such as names, reference numbers, dates, totals or contract clauses across many layouts, including scanned and photographed documents. Typical uses include purchase orders, delivery notes, onboarding forms, identity documents, claims, supplier invoices and contract review. The value comes from less manual data entry, faster processing and fewer typing errors, as long as someone handles the unusual cases well.

Documents arrive by email, upload or scan. The system reads the text, using optical character recognition for scanned or photographed documents. It identifies the type of document and pulls out the fields it needs, giving each field a confidence level. Rules then check the data. For example, they confirm that line items add up to the total, that the customer exists in the customer list or that a reference number matches an open order. Documents that pass go straight into the target system through a direct connection. Documents that fail, or that the system is unsure about, go to a review queue. The system records each correction a reviewer makes and uses it to improve accuracy over time.

A logistics company receives 3,000 signed delivery notes a month as scans and photos, and two staff spend most of their time typing them into the order system. An IDP setup reads each note, pulls out the order number, delivery date and any damage remarks, and matches the note to its open order. After tuning, 78% of notes pass every check and go in automatically. The other 22% go to a review queue. Notes that need manual handling fall from about 3,000 to about 660 a month, and the team tracks matching errors each month.

Document-heavy processes are often a practical place to start with AI, because volumes, handling times and error rates are easy to count. Leaders need to decide which documents to start with and what share should pass with no human touch. They also need to decide who owns the review queue.

Accuracy varies with document quality, language and layout. A new form version or a new customer's format can lower it without warning, so track the share of documents that pass untouched every month. Documents may contain personal data, which brings privacy obligations. Sending data into systems automatically without checking rules can spread errors quickly. Rules on keeping original documents still apply.