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L1 · AI use cases across finance8 min read

AP, AR and procurement: AI in the transaction engine

Procure-to-pay and order-to-cash are high-volume, document-heavy and full of exceptions: ideal ground for AI, and also where fraud and payment errors live.

Accounts payable

  • Capture and coding: extract header and line data from invoices in any format; suggest GL codes, cost centers and tax codes.
  • Matching: two- and three-way match against POs and receipts; explain why an exception happened.
  • Duplicate and fraud detection: near-duplicate invoices, changed bank details, unusual vendors.
  • Supplier queries: draft answers to "where is my payment?" from the ERP status.

Accounts receivable

  • Cash application: match remittances to open items, even with messy references.
  • Collections: prioritize accounts by risk and value; draft reminders and summarize disputes.
  • Credit: summarize customer financials and payment history for a credit decision (made by a person).

Procurement finance

  • Spend analytics: classify spend into a clean taxonomy; find tail spend and off-contract buying.
  • Contract terms: extract payment terms, price escalators and rebates; compare against invoices.

Controls to keep

  • Bank-detail changes always go through out-of-band verification, whatever the AI says.
  • Payment approval limits stay with people (or with agents only under explicit, logged limits).
  • Segregation of duties: the same agent should not create a vendor, approve an invoice and release payment.
  • Sample AI-coded invoices every month and track the error rate.

Measures

Cost and time per invoice, touchless rate, duplicate payments recovered, days sales outstanding, cash applied automatically, dispute cycle time.

What this means for the CFO

For the CFO, P2P and O2C AI is a working-capital and fraud story as much as a cost story. Ask for business cases that include duplicate payments avoided, early-payment discounts captured and DSO movement, not only hours saved.

Hold a hard line on two controls: bank-detail changes verified out of band, and segregation of duties for any agent that can create, approve or pay.

Ask for the monthly error rate of AI-coded invoices and the touchless rate side by side. A rising touchless rate with a stable or falling error rate is the evidence that the process is ready for more automation; a rising error rate is a reason to slow down.

Real example

Microsoft announced Copilot for Finance in public preview on February 29, 2024, built around finance scenarios including reconciliation, collections (suggesting priority accounts, summarizing conversations and proposing payment plans) and variance analysis, connecting to financial systems such as Dynamics 365 and SAP from Outlook, Excel and Teams. This is a vendor's description of capability, not evidence of results. Source: Microsoft — Introducing Microsoft Copilot for Finance (Feb 29, 2024) ↗

Use it

Quick check · 3 questions

  1. 1.Which AP control must never be delegated to AI alone?

  2. 2.Which measure best shows AI value in collections?

  3. 3.Why should one agent not create vendors, approve invoices and release payments?

0 of 3 answered

Sources

Last reviewed 2026-10-01.