Partly. Recording and matching an incoming invoice against a purchase order and receipt is largely automatable, but not entirely and not without human oversight. The task consists of a recognizable, repeatable part that AI can already handle today, and a smaller part where a human check remains necessary. That latter part is not optional: it concerns money going out the door and rules a bookkeeping system must comply with.
In most companies, a purchase invoice is a structured document: a fixed pattern of supplier, invoice number, amount, VAT, and lines matching a purchase order. That structuredness, combined with a high volume, makes this exactly the type of task where AI has proven itself. With hundreds or thousands of invoices per month, the gain per invoice is small, but the sum total in freed-up hours is considerable. There is also no customer contact or physical action needed: it is text processing in an ERP environment or invoice scanning system, and that is exactly the type of work AI is good at today.
An example: an invoice from a regular supplier for a hundred units of item X, with an amount matching the purchase order and a receipt confirming the delivery. The system recognizes the supplier and item codes, matches the three documents, and readies the invoice for payment. No human had to look at this until the payment is submitted for approval.
Three axes keep this at "partly": compliance, cost of errors, and room for judgment. An invoice that does not match — a deviating amount, a missing receipt, a new supplier without recorded agreements — requires an assessment that goes beyond pattern recognition. Why is the amount different? Is that an error, a price change that was not implemented, or a legitimate surcharge? That is a question with a reason behind it, and someone must be able to approve or reject that reason.
Then there is the compliance side: a bookkeeping system must be correct, must also be verifiable afterwards, and an error in the accounts payable administration costs more than the time to fix it. In case of deviations, the cost-of-error axis is therefore not low enough to leave the risk to a system alone. This is also why this differs from, for example, registering a receipt in the system, where fewer financial consequences are directly attached to the action.
The technology that makes this possible is text recognition: OCR that reads an invoice and extracts data, combined with a matching system that lays the purchase order, receipt, and invoice side by side. The quality of that recognition determines how much passes through automatically. With a clean dataset — regular suppliers, digital invoices, consistent order numbers — the automatically processed share is high. With many one-off suppliers, paper invoices, or deviating formats, that share is considerably lower, and the work shifts from "AI does it" to "AI does the groundwork, a human assesses the exception".
The outcome depends heavily on how standardized the purchasing process is. A company with a small number of regular suppliers and strict PO discipline — every order recorded in advance — can have a large share of invoices matched automatically. A company with many one-off purchases, verbal agreements, or suppliers that submit invoices in varying ways retains many more exceptions that a human must review. The setup of adjacent processes also plays a role: if orders placed with suppliers are already structured in the system, matching upon receipt of the invoice is simpler than when that information is missing or incomplete.
This is not personnel advice and not a basis for a decision about a position or employee. The question here is which part of the work, in terms of task content, is eligible for automation, not what an organization does with that outcome in its personnel policy. Decisions affecting the employment relationship are subject to their own legal requirements; more on how AI deployment and employee protection come together can be found on the page about the AI regulation and your employees. Those wanting to know more broadly how this shift occurs in office work can find that in the overview of what AI can take over in business services.
To see how this plays out for your own accounts payable process, it is necessary to look at the share of standardized invoices, the quality of the PO linkage, and the error handling that already exists today. FTE TO AI's free quickscan offers an initial direction here: twelve questions, without an account, with an indication of what share of the hours in a profile like this can be taken over by AI today. The full work scan, which calculates this down to task level for an entire company, is still under construction.
Vraag maar. Ik ken de kennisbank van deze site; wat ik niet weet, zeg ik erbij.
Answers come from this site’s knowledge base. Not tailored advice, and not a scan of your company.