Automating invoicing with AI: how it works in practice

In many SMEs, invoicing still looks like this: someone pulls data from one system, retypes it into another, checks it against a contract in a third, and then creates the invoice. Every month again. It's work nobody enjoys, it takes a lot of time and errors creep in.

That is exactly the kind of work an AI agent can take over. In this article we explain how that works in practice.

What is an AI agent, actually?

An AI agent is software that carries out a task on its own: fetching data, interpreting it, making decisions within clear rules and putting the result into your systems. The difference from classic automation is that an agent can also handle information that isn't neatly structured, like a supplier PDF, an email with an unusual request or a contract with exceptions.

Important: a good agent works within limits you set. Whatever it isn't sure about, it hands to a person.

What does automated invoicing look like?

A typical invoicing setup has four steps:

1. Fetch the data. The agent pulls the delivered work from your operational system: deliveries, hours, projects or orders.
2. Check against agreements. It compares that data with the contracts or price agreements per customer, including discounts and exceptions.
3. Create the invoice. The invoice is created automatically in your accounting software.
4. Flag exceptions. If something doesn't add up, such as a delivery without a contract price or an unusually high amount, someone on your team gets an alert instead of the invoice simply going out.

Your team goes from doing everything themselves to handling only the exceptions.

Which systems can you connect?

Almost anything with an API. In Belgian SMEs we often see:

• Accounting and ERP: Exact, Odoo, SAP, Microsoft Dynamics
• CRM: Teamleader, HubSpot, Salesforce
• Operational systems: a TMS in logistics, planning tools, web shops
• Communication and documents: Microsoft 365, Google Workspace, Slack

If a system has no API, there is often another route, such as an export, a shared mailbox or a database. And sometimes the honest answer is that it can't be done.

An example from logistics

One of our clients, a logistics company with 80 employees and 45 vehicles, created invoices by copying data between three different systems. We deployed an invoicing agent that pulls delivery data, matches it against contracts and generates invoices automatically. It syncs with their accounting software every night.

The result: invoicing errors are virtually gone. Together with agents for dispatch, customer communication and reporting, the company saves around €120,000 a year, and it grew from 45 to 60 vehicles without adding operations staff. Read the full case.

What should you sort out before you start?

• Know where your data lives. Which systems hold the information that ends up on an invoice?
• Write down your rules. How do you set a price, discount or exception today? What lives in someone's head has to become explicit.
• Decide who handles exceptions. An agent in doubt needs someone to turn to.
• Think about GDPR and access. Invoices contain customer data. Make clear where that data is processed and who has access to it.

How long does it take?

We usually build a focused automation like invoicing in 2 to 4 weeks, depending on the number of systems and exceptions. Read what drives the cost in our article what does AI cost for an SME?

Want to know whether your invoicing is a good fit? Tell us how it works today and you'll get an honest answer.