66% of AP Teams Still Key Invoices by Hand.
The Number Is Going Up, Not Down.

66% of AP Teams Still Key Invoices by Hand. The Number Is Going Up, Not Down.

The IFOL Accounts Payable Automation Trends 2025 report surveyed nearly 200 AP and finance professionals — 78% of whom work directly in accounts payable. The findings don't fit the narrative.

66% still manually enter invoice data into their ERP or finance system — up from 60% in 2023. 63% spend more than 10 hours a week on invoice processing — up from 52% in 2024. 73% of finance teams are not fully automated. 27% have no automation at all.

Meanwhile, Vic.ai's 2025 AI Momentum Report found that 37% of AP professionals still rank manual data entry as their number one pain point — ahead of high processing costs, slow approvals, and every other operational headache.

This isn't a story of progress. It's a story of a problem that looks solved from the outside but isn't. The automation tools exist. The ROI cases are clear. So why is the manual entry rate increasing?

The answer isn't "companies haven't bought tools yet." It's that the tools most companies bought don't solve the root problem. And the root problem isn't that data entry is slow — it's that every invoice arrives in a different format, and template-based automation breaks exactly at that point.

What "Manual Data Entry" Actually Looks Like Day to Day

If you work in AP, you don't need this section. But if you're a CFO, a founder, or someone who approved the automation budget and assumed the problem was fixed — this is what your team is actually doing.

An invoice arrives as a PDF attachment in a shared inbox. Someone opens it. They read the vendor name, invoice number, date, due date, line items, subtotal, tax, and total. They type every one of those fields into NetSuite. Or SAP. Or QuickBooks. They match the invoice to a purchase order — manually, by eye. They route it for approval via email. Then they chase down the approver who hasn't responded in three days.

Now multiply by 200. Or 1,500. Or 5,000 — the volume 36% of IFOL respondents handle monthly.

One AP professional on Reddit's r/Accounting described it plainly: "We get maybe 1,500–2,000 invoices a month from suppliers. I keep hearing AP automation is basically solved at this point, but our process is still: invoices hit a shared inbox as PDF attachments, someone opens each one, types the header info into NetSuite, matches to PO manually, routes for approval via email, chases down approvers when they ignore it. We tried the OCR thing built into NetSuite but it chokes on half our invoices because every machine shop and raw materials supplier formats theirs differently."

This post has over 200 upvotes and dozens of comments from people saying the same thing. These aren't small companies. These are mid-market manufacturers, distributors, and service businesses processing hundreds or thousands of invoices a month — and the person doing the typing is a trained accountant, not a data entry clerk.

Ardent Partners' 2025 research puts the cost at $15.97 per manually processed invoice, compared to $2.36 for best-in-class automated processing. For a company processing 3,000 invoices a month, that's a $490,000 annual gap — not counting the cost of errors, late payments, or missed early-payment discounts.

The Format Problem: Why Every Vendor Invoice Breaks Your Template

If invoices all followed one standard layout, template-based OCR would have solved this a decade ago. They don't. And they never will.

A vendor in Germany places the tax breakdown in the footer. A supplier in Texas puts it in a column to the right of the line items. A contractor in Singapore doesn't show tax at all — it's included in the unit price. A local machine shop sends a scanned PDF with handwritten notes in the margin. Another emails a phone photo of a paper invoice on a desk.

Every one of these format variations breaks template-based automation. Template OCR works by remembering where fields are on a known layout: "Invoice number is at coordinates X,Y. Total is at coordinates A,B." The moment a vendor changes their invoice template — or you add a new vendor — those coordinates stop being correct.

This isn't an edge case. It's the default. The average mid-market company receives invoices from 200–500 different suppliers, each using their own layout. ERP systems, accounting packages, and vertical SaaS tools generate invoices in formats their developers chose — not formats your AP team can control.

And it gets worse. Vendors change their invoice formats. A supplier upgrades their billing system. A restaurant chain redesigns their template. A freelancer switches from one invoicing app to another. Each change silently breaks the template that was working last month. Your AP team discovers the break when the data comes out wrong — or when they notice they've been manually fixing the same field for weeks.

The Template Trap: When "More Automation" Means More Maintenance

Here's the structural paradox that most automation vendors won't tell you.

Template-based invoice processing scales linearly with vendor count. Every new supplier means a new template to build, test, and maintain. At 20 vendors, you have 20 templates. Manageable. At 200 vendors — which is normal for a mid-market manufacturer or distributor — you have 200 templates. Each one needs to be updated when the vendor changes their format, which they do, regularly, without telling you.

Infrrd's analysis of invoice parsing automation put it bluntly: "Each new vendor added to a template-based system requires configuration work before a single invoice can be processed accurately. When supplier counts grow or onboarding happens fast, the template backlog becomes a bottleneck. Teams spend more time managing templates than processing invoices — which is the opposite of what automation is supposed to accomplish."

At scale, the math flips. If maintaining 200 templates takes one full-time person and entering 3,000 invoices manually takes two people, you haven't automated — you've just replaced data entry labor with template maintenance labor. The cost moved, it didn't disappear.

This is why 66% of teams still manually enter data despite having "automation" in place. They bought a tool that automates 80% of invoices from their top 20 vendors — and still manually handle the remaining 180 vendors whose formats the tool can't parse. The tool is there. It's running. But the majority of the work still falls on human hands.

The Exception Spiral: 20% of Your Invoices Eat 80% of the Time

Even in partially-automated AP departments, there's a category of work that almost nobody measures: exception handling.

Research from the 2025 State of ePayables report shows that roughly 22% of invoices require exception handling — missing PO numbers, incorrect amounts, format anomalies, duplicate submissions. In a manual process, these exceptions are just part of the flow. In an automated process, they become roadblocks.

The automation flags the exception. Stops processing. Generates a notification. And then... a human has to step in. Open the original PDF. Compare it against the flagged data. Email the vendor. Wait for a response. Re-enter corrected data. Re-route for approval. Each exception can take 15–30 minutes to resolve.

Now do the math: 3,000 invoices a month × 22% exception rate = 660 exceptions. At 20 minutes each, that's 220 hours — more than a full-time employee's monthly capacity — spent just on exceptions. And that's after the automation already "processed" the invoice.

This is the semi-automation trap. Companies invest in automation, reduce the easy 80%, and discover the remaining 20% is harder than the original 100% because exceptions break the workflow rhythm. The team isn't doing less work — they're doing more fragmented, context-switching-heavy work. Vic.ai calls it "the last mile of AP automation still walked by hand."

Why Template-Based OCR Hits a Ceiling — and What Gets Past It

The previous three sections describe one root problem: format dependency. Template OCR, rule-based extraction, and traditional machine learning models all share the same limitation — they need to know where data is, not what it is.

A template system sees coordinates. It asks: "Where on the page is the invoice number?" The answer is different for every vendor. A vision language model (VLM) sees semantics. It asks: "What on this page functions as an invoice number?" The answer is the same regardless of where it sits.

This is not a marginal improvement. It's a category difference. When you tell a VLM-based system to extract "Invoice Number," "Vendor Name," "Due Date," and "Total," it doesn't scan for those words in expected positions — it reads the document the way a person would, understanding that "INV-2025-08472" next to a label that says "Invoice #" or "Reference" or "Document No." is the invoice number, regardless of where on the page that pairing appears.

Three implications follow directly from this:

1. No templates to build or maintain. A new vendor's invoice is processed the same way as a vendor you've worked with for ten years. The system reads each document fresh, understanding its structure independently.

2. Format changes don't break the pipeline. When a supplier redesigns their invoice layout, the VLM still finds the fields by their semantic role, not their position. You don't need to update anything.

3. Cross-vendor data lands in one unified table. You define the column names you want — "Vendor," "Invoice Date," "Line Description," "Qty," "Unit Price," "Total" — and every invoice, regardless of its original layout, outputs data into those columns. One spreadsheet, every vendor, no template maintenance.

This is not theory. The architectural shift from "where is the data" (template OCR) to "what does this data mean" (VLM) is what makes the format problem solvable at scale.

What Actually Changes When You Switch to AI Extraction

Let's ground this in numbers. For a mid-market AP team processing 3,000 invoices a month across 300 vendors:

The workflow on the VLM side is straightforward: upload your invoices (drag-and-drop, batch upload from a folder, or share a collection link for vendors to submit directly), specify the column names you want extracted, and download a single Excel file with all data merged. If you use Google Sheets, the add-on lets you do this without leaving your spreadsheet — extracted data appends directly to your active sheet.

And for teams that need more than extraction, computed columns let you define calculations that run during extraction. Need line item totals computed from quantity × unit price? Want to flag invoices where the billed total doesn't match the sum of line items? Define the calculation once, and the AI applies it to every invoice automatically. For a deeper walkthrough of the batch processing workflow, read our guide on batch extracting invoice data from multiple PDFs into one Excel sheet.