An invoice arrives from a vendor your company uses every month. The details have been captured, but someone still needs to choose the account, assign the department, and check whether any costs belong to a project.
So they open last month’s invoice to see how it was handled.
It’s a small task. Repeat it across dozens of invoices, and it becomes a regular part of someone’s day.
For AP teams, this is one reason invoice processing still takes time, even after reducing paper and manual data entry. Getting the information into the system is only part of the work. Someone still has to decide where the expense belongs.
Capturing an invoice means reading information such as the vendor, invoice number, date, and amount. Coding it means deciding how to record the expense.
That might involve assigning an account, department, location, project, or budget. An invoice with several line items may need different allocations for different expenses.
For example, an office supply invoice might include purchases for two locations. Knowing the vendor and total doesn’t tell the accounting team how much to charge each location.
Someone needs to make that decision or confirm the system's recommendation is appropriate.
A useful place to start is with the invoices your team processes regularly.
Ask the people doing the work:
Their answers can help separate repetitive work from decisions that need a closer look.
A recurring expense with consistent coding may be a good candidate for automation. An unfamiliar charge with no department or project reference may need more information before anyone can code it correctly.
Identifying these patterns helps you focus on what you can reduce and the questions your team still needs answered.
AI-assisted allocation uses invoice information and how similar expenses have been handled to help determine where a cost belongs.
Yooz’s Agentic AI Allocation, for example, considers invoice context, processing history, and previously validated allocation decisions. It then responds differently depending on its confidence in the result:
This gives routine invoices a different path from those that need more attention.
For the AP team, the experience should be easy to understand. People need to see what has been filled in, what is being suggested, and what still requires a decision. They also need a straightforward way to correct a value.
Filling in an account or department is one step in the process. Invoice approval and payment authorization are separate steps, so ask how the coding fits into your existing controls.
Past invoices provide useful context, but businesses don’t stand still.
A subscription may move to another department. A project may close. An expense previously charged to one location may now need to be shared.
In those cases, last month’s coding may no longer be appropriate.
Yooz says validated allocation decisions help inform future recommendations. That makes the team’s input important: people need to recognize when circumstances have changed and check that the new allocation reflects them.
When reviewing a solution, bring an example of a recurring expense that changed. Ask the provider to show how a user would correct the allocation and how that correction would affect future recommendations.
That can tell you more about day-to-day use than watching a familiar invoice go through without a problem.
To understand whether automation would help your team, choose three invoices for a demonstration:
Include the person who normally processes them. They’ll know which steps take time and whether the proposed workflow would make the job easier.
If you run a pilot, track time spent coding, corrections needed, and invoices waiting for clarification. Automatically populated fields help only if they let the team do the work accurately with less effort.
The next time someone opens an old invoice to check how a new one should be coded, ask how often that happens. It may be a small step on its own, but it’s worth understanding how much of the week goes into making the same decisions again.
See how Yooz handles invoice allocation with AI.