Intelligent Document Processing: Turning Invoices, Forms and Reports into Data

Walk through any operations team and you'll find people retyping information from documents into systems: invoice totals into accounting, delivery notes into inventory, application forms into a CRM. It's slow, error-prone and nobody's favourite job.

Intelligent Document Processing (IDP) combines OCR with AI that understands document structure, so the data can be extracted automatically.

How it works

1. Capture — documents arrive by email, upload, scan or a shared folder.

2. Classify — the system recognises the type: invoice, purchase order, ID, delivery note.

3. Extract — key fields are read: supplier, dates, amounts, line items, reference numbers.

4. Validate — values are checked against rules and existing records (does this PO exist? do totals add up?).

5. Route — clean data goes straight into your system; uncertain cases go to a person for review.

Why human-in-the-loop matters

No extraction model is perfect, especially with poor scans or unusual layouts. The goal isn't zero human involvement — it's people reviewing exceptions instead of typing everything. Each correction also helps improve the model over time.

Good first use cases

• Supplier invoices and purchase orders

• Delivery notes and goods received notes

• Customer onboarding and KYC forms

• Inspection and audit reports

• Archived paper records that need to become searchable

Choosing the tooling

For Microsoft 365 businesses, AI Builder and Azure AI Document Intelligence integrate directly with Power Apps, Power Automate and SharePoint. For high volumes or complex layouts, custom models may be the better fit. The right choice depends on volume, document variety and where the data needs to end up.

Start small

Pick one document type with high volume and clear fields. Measure the time spent today, run a pilot on real documents, and compare. AltAIEnture builds document processing workflows end to end — from capture to the system your team already uses.