To automate document intake, start with a list of what arrives and where each document has to go. Then classify every document before extracting anything, extract only the fields you need, check them against your own systems, and send anything that fails a check to a person with the problem highlighted. Measure accuracy on your own documents rather than trusting a vendor's figure.
Why intake is worth fixing
Document intake sits at the front of most operational processes: orders, invoices, onboarding packs, claims, certificates, delivery notes. When it backs up, everything behind it waits. It is also where people spend hours re-keying information from one place to another, in a working day that Microsoft's telemetry shows is interrupted, on average, every two minutes by a meeting, email or notification.
Paper and email are not going away. In an AIIM study of 600 enterprises published in August 2025, 61% of document processing workflows still included paper, and 48% of organizations expected paper volumes to rise.
Step 1: List what arrives and where it has to go
For each inbox, portal or mailroom, write down what comes in. For example:
| Document | Arrives via | Goes into | Monthly volume | Handled by |
|---|---|---|---|---|
| Supplier invoice | AP inbox, PDF | ERP | 3,000 | AP team |
| Customer order | Sales inbox, PDF and email body | ERP | 1,200 | Order desk |
| Proof of delivery | Carrier portal, scan | Logistics system | 4,500 | Operations |
Pick one document type to start with: high volume, fairly consistent, and with a clear destination. Get that right before adding the next.
Step 2: Classify each document before you extract
The first source of errors is treating a document as something it is not. Before extracting anything, decide what each document is: an invoice, a credit note, a statement, a purchase order, a certificate, a letter.
Split multi-document PDFs into separate documents. Treat email bodies and attachments as separate items. Catch duplicates here, before they create two records downstream.
Step 3: Extract only the fields you will use
For each document type, list the fields the destination system needs, their format and whether they are required. Ignore everything else. A tight field list is faster to check and easier to measure.
Keep a reference to where each value came from, down to the page. When someone later asks why a field says what it says, you can show them.
Step 4: Validate against your own systems
Extraction gives you values. Validation tells you whether to trust them. Check each document against what you already know:
- Does the customer or supplier exist, and do the name and tax ID match?
- Is the purchase order or account open?
- Do the lines add up to the total, and is the tax plausible?
- Are dates in range, and is this document already in the system?
Set a confidence threshold. Anything below it, or anything that fails a check, is an exception.
Step 5: Route exceptions and measure accuracy
Send each exception to a person with the document and the problem highlighted: which field, which check failed, and what the system expected. They should be able to fix it without opening three other systems.
Then measure, every week:
- Field accuracy on a random sample checked by a person
- Straight-through rate: the share of documents that needed no human touch
- Turnaround from arrival to posted in the destination system
- Exception causes, ranked, so you fix the biggest one first
Vendors often quote accuracy of 99% or more, measured on their own test documents. There is no independent benchmark across vendors, so your own sample is the only number to rely on.
Where AI agents fit
Agents can do Steps 2 to 4 for any layout: read, classify, extract, validate and file. Our exception desk handles what fails, and your team decides anything that needs judgement. In our business operations service, document intake is measured on turnaround and hours returned, and every extracted value is recorded in the Evidence File with the page it came from.