How to Choose AI for Accounts Payable

If you run an accounting or advisory firm and you have started researching AI for accounts payable processes, you will have found a market full of tools that all claim to solve the same problem.

Most of them solve a version of it. Very few solve it completely. Here is how to think about the difference.

The accounts payable problem in accounting firms

The AP workflow in most accounting and professional services firms looks like this:

An invoice arrives in the shared inbox. Someone reads it, decides what it is (job-related, overhead, which job code, which cost category), and files it to the right place. They may also enter it into the accounting system, match it to a purchase order, or flag it for approval.

This sounds simple. It becomes complicated when:

• The same supplier sends invoices in different formats each month
• A single invoice has line items across multiple jobs or cost centres
• The person who usually handles it is on leave
• The volume spikes at month-end

These are the edge cases that break most AP automation tools. A workflow tool follows a fixed script. It works when the input matches the script exactly. It breaks when anything varies.

Why standard workflow tools fail on complex AP

Tools like Zapier, Make, and basic RPA software approach AP automation as a trigger-action problem. Invoice arrives, match the pattern, file it.

The problem is that real AP work is not pattern-matching. It is classification. And classification requires reading a document, understanding its context, and making a decision.

Consider a TNT freight invoice. The same supplier, the same email address, every week, but each invoice has a different number of line items, each charged to a different job code, with a grouping logic that changes based on what was shipped that week.

A workflow tool cannot handle this. There is no fixed pattern to match. An AI agent reads the document, understands the line items, identifies which charges belong to which job based on context, and produces a structured output.

That is a qualitatively different capability.

How to choose the right approach

For simple, consistent AP workflows, one invoice type, one destination, fixed format, a workflow tool is often sufficient and faster to deploy.

For complex AP workflows, multiple suppliers with variable formats, multi-line invoices across jobs, classification decisions that require reading the document, an AI agent is the correct tool.

The test is this: can you describe the entire AP process in a flowchart where every branch is a fixed condition? If yes, a workflow tool will likely work. If any branch requires "read the document and decide," you need an agent.

Quick comparison

What a well-built AP agent looks like

For an accounting firm with a complex AP workflow, a properly built agent:

1. Monitors the Outlook inbox on a set schedule
2. Reads each incoming invoice PDF using an AI model
3. Classifies the invoice by type, supplier, job code, and cost category
4. For multi-line invoices: groups line items by job and produces a structured output for each
5. Files to the correct SharePoint folder with the correct naming convention
6. Logs every action: timestamp, supplier, invoice number, folder path, confidence score
7. Flags low-confidence decisions for human review before filing
8. Optionally pushes confirmed invoices to MYOB or Xero via API

The only ongoing human input is reviewing flagged items. For a typical firm, this is 5 to 10 decisions per week, each taking under a minute.

The setup question

The main barrier to building this is the integration work: connecting to Outlook via Microsoft Graph, connecting to SharePoint, connecting to MYOB or Xero. Each connection requires API credentials, appropriate permission scopes, and handling the quirks of each system.

This is where professional help pays for itself. Once the integrations are built correctly, the agent runs reliably. Getting the integrations right the first time is the leverage point.