AI in AP Automation: The Shift Your Finance Team Can’t Afford to Watch from the Sidelines
Blog written by Dooap
Accounts payable automation is now the second most common place finance teams put AI to work, behind only knowledge management, according to Gartner’s 2025 AI in Finance survey. Thirty-seven percent of finance functions running AI have already pointed it at AP. That puts the function near the front of the finance AI wave, not the back, and Deloitte’s Q4 2025 CFO Signals survey sharpens the urgency: 87 percent of CFOs expect AI to be very or extremely important to their finance operations, with only 2 percent saying it will not matter.
Adoption is not the same as intelligence. Even among teams that consider themselves automated, roughly two-thirds still manually enter invoice data into their ERP, according to the 2025 AP Automation Trends Report. The tooling is in place. The intelligence is not. That gap between having automation and having automation that learns is where AI in AP automation is doing its most important work, and it is wider than most finance leaders admit.
Rules-based automation hit a ceiling, and everyone can feel it
The first generation of AP automation was built on rules. You mapped an invoice template, configured tolerance thresholds for matching, and defined approval routes for each vendor and dollar amount. When an invoice looked exactly like the one before it, the system flew. When it did not, it kicked out an exception and waited for a human.
This worked until invoice volume and vendor diversity outgrew the rules. Every new supplier format, every edge case, every one-off credit note added another branch to maintain. The exception queue grew faster than the team could clear it, and the promise of touchless invoice processing started to feel like something that happened in someone else’s finance department. Industry benchmarks tell the story plainly: even best-in-class AP teams reached only about 53 percent touchless processing in 2025, while teams still leaning entirely on static rules showed no year-over-year improvement at all.
Here is the part worth being direct about. Adding more rules does not fix a rules problem. It compounds it. Each rule you write is another thing that breaks when a vendor changes their layout or your business signs a new supplier. AI changes the equation because it learns what your invoices look like and how your approvers behave, then acts on that pattern instead of waiting for you to describe every scenario in advance.
Where AI earns its keep
Strip away the marketing and AI delivers value in a handful of concrete places inside the AP process.
The first is data capture. Modern machine learning models read an invoice the way a person does, recognizing the relationship between a vendor name, a line item, and a total without needing a pre-built template for that specific supplier. A new vendor’s first invoice gets processed correctly, not routed to a queue because the system has never seen its format. That alone removes one of the most persistent sources of manual keying.
The second is matching. Rule-based three-way matching gives you a pass or a fail against fixed tolerances and then stays frozen. An AI matching engine watches which invoices your team approves, learns which vendors clear at which variance levels, and raises the touchless rate month over month instead of holding it flat. The system gets sharper the longer it runs.
The third is exception handling, and this is where the operational relief becomes obvious. Not every exception deserves the same queue. A missing PO reference and a suspected duplicate payment are not the same problem, yet static systems drop them into one undifferentiated pile. AI can route by type and urgency, send the simple coding question to the right approver, and flag the genuinely suspicious invoice for review before it becomes a payment.
That last capability matters more every year. Invoice fraud detection has moved from a nice-to-have to a board-level concern as payment fraud grows more sophisticated. AI models learn the normal rhythm of your vendor behavior and payment patterns, then surface the anomaly a tired human scanning a hundred invoices would miss. A vendor bank detail that changed last week. An amount that sits just under an approval threshold. A duplicate wearing a slightly different invoice number.
The Dynamics 365 question every finance leader is asking
If you run Dynamics 365 Finance, the practical question is not whether to adopt AI in accounts payable automation. It is how the pieces fit together. Microsoft has moved quickly here. Copilot capabilities in the finance and operations apps now bring embedded intelligence and AI agents directly into the D365 experience, and Finance in Microsoft 365 Copilot connects ERP data to the Excel and Outlook workflows finance teams already live in. The direction is unmistakable, and it is accelerating.
What Dynamics does natively and what a purpose-built AP solution adds are two different layers, and it helps to be clear about the boundary. D365 Finance gives you the ledger, the controls, and a growing set of native AI agents. Deep AP-specific capability comes from a dedicated solution: high-touchless invoice capture, predictive coding, and configurable approval intelligence, all validated against Dynamics business logic in real time without adding weight to the ERP itself. The strongest setups pair the two rather than forcing one to do the other’s job. This is also where a partner that lives inside the Dynamics ecosystem earns its value, and it is the kind of AI-ready finance work Ellipse Solutions writes about often on its own blog.
Where to start over the next twelve months
The finance teams pulling ahead are not the ones with the biggest AI budget. They are the ones who started with a focused, high-volume slice of the process and expanded once accuracy earned their trust. Pick the workflow where manual effort hurts most, usually invoice capture and coding, and let the system prove itself there before you widen the scope.
Data quality is the quiet prerequisite. AI learns from your history, so a messy chart of accounts and inconsistent vendor records will cap what it can do. Cleaning that foundation is unglamorous work that pays off within the first two or three months of a serious deployment.
Governance belongs in the plan from day one, not bolted on after the first surprising decision. When the system codes an invoice or clears a match on its own, your team should be able to see why. Confidence scores, a clear rationale, and a full audit trail are what turn AI from a black box your controller distrusts into a colleague your controller relies on. The goal was never to remove people from AP. It is to move them off data entry and onto the judgment calls that need a human.
There is a reason agentic AI in accounts payable has become the phrase of the moment. The newest wave of tools does not just extract and predict; it can carry a multi-step task from coding through matching to routing, explaining each decision as it goes. Whether your organization is ready to hand over that much autonomy today is a fair question, and the honest answer depends on how clean your data and controls already are. But the ceiling of what rules-based automation can reach is no longer in doubt.
The teams that treat AI in AP automation as a genuine shift in how the function operates, rather than one more feature to switch on, will spend the next year processing more invoices with fewer people, catching more fraud, and closing the books faster. The rest will keep working the exception queue.
If your team is weighing what AI-ready AP looks like on Dynamics 365 Finance, the most useful first step is a clear-eyed look at where your current process leaks time. Dooap builds AP automation purpose-built for Microsoft Dynamics 365 Finance, and pairing that with an experienced Dynamics partner is usually what turns an honest assessment into a plan worth funding.