Can AI really free your firm from AP work?

There’s an ideal version of your AP process where the intelligence inside your payment data is immediately accessible without any manual input.

Most firms aren’t there yet. And it’s not because the technology doesn’t exist.

On the whole, AP still works like this: someone needs to know which bills are overdue, or what was paid to a vendor last quarter, or what’s scheduled to go out next week, and someone on the team has to stop what they’re doing to find out.

The cost is real and well-documented. According to the Ardent Partners State of ePayables 2025, manual invoice processing takes an average of 17.4 days and costs between $12.88 and $19.83 per invoice. Best-in-class AP teams using automation process in 3.1 days at $2.78 per invoice. The accountant who spent twenty minutes tracking down a payment status could have spent that same time in a conversation that moved a client relationship forward.

That trade-off can happen dozens of times a week, across many clients, and it mostly goes unchanged because each individual interruption feels minor.

Add it up and it’s not minor at all.

The AI moment accounting firms are actually in

There’s been enough noise about AI transforming the profession that most firm owners have heard the pitch many times over. Some have run experiments. A few have seen real results. Most are somewhere in between – curious, already encountering AI inside the tools they use every day, but not yet convinced that any of it adds up to a meaningful change in how the firm actually runs.

That skepticism is reasonable. But the kind that serves you well is the kind that asks where AI belongs in your workflow, not whether it belongs at all.

The adoption momentum is already significant. 75% of AP departments now use some form of AI or automation, and 65% of AP leaders expect AI to have a significant transformational impact on operations within the next two years. But the work that genuinely benefits isn’t the complex, judgment-heavy work that defines what good accountants do. It’s the high-volume, low-judgment, surprisingly time-consuming questions that crowd out the work clients actually value.

The instinct when adopting new technology is usually to go big and automate everything. That’s where most AI experiments fail. The scope is too wide to prove anything, and when results don’t show up quickly, the whole effort gets quietly shelved.

Onboard AI like a new hire

Think about how you bring someone new onto your team. You wouldn’t hand them your most complex clients in week one. You’d give them a defined role, clear boundaries, and measurable expectations. You’d review their work until trust was established. Then you’d expand what they own.

AI integration works the same way.

Before you deploy anything, answer three questions: What is it responsible for? What is explicitly not its job? When does it escalate to a human?

Once you have that clarity, the path is straightforward:

1. Identify high-volume, low-judgment work. Which tasks eat time because they’re routine? What would free up expert time?
2. Define escalation rules before you go live. What needs to be automatically flagged to a human?
3. Pilot one role before you expand. Which single use case can you measure clearly enough to know if it’s working?
4. Track what changes. Are you saving time? How often is it escalating? What does that tell you?
5. Scale what works. Cut what doesn’t. What would you do with the time you get back?

This approach works because it’s honest about what AI is good at right now. It’s not good at replacing accountant judgment. It is very good at handling the routine, repetitive work that shouldn’t require that judgment in the first place.

What AI-assisted AP actually looks like in practice

AP is a good example of what this looks like in practice. Inside the typical AP workflow, there are dozens of questions that need answering throughout the week. Some are operational: Which payments are overdue? What was paid to this vendor last quarter? What’s scheduled to go out next week?

There are also strategic questions you could be asking across clients, if speed and capacity weren’t a barrier. What were the overall marketing-related vendor payments in 2025? What’s the total credit card payment volume for the month versus year-to-date?

This is exactly the kind of work AI can take on.

Agent Mel, Melio’s AI-powered feature, was built to handle these AP questions directly inside the Melio dashboard. It pulls from live payment data and answers questions in plain language, so your team gets the information they need in seconds rather than digging through records. Instead of someone pausing their work to track down a payment status, they can simply ask.

Agent Mel also surfaces patterns worth paying attention to – overdue payments, balances that have shifted, spending trends worth a conversation – giving your team visibility to catch issues before they become problems and spot opportunities before they disappear.

The right kind of caution

The hesitation most firms feel about AI in AP isn’t irrational. You’re responsible for real money moving in and out of client accounts. When something goes wrong, it’s not the AI answering the call. It’s you.

That instinct is correct. The mistake is letting it stop you from automating anything, when what it should actually be doing is helping you choose more carefully what to automate.

Control doesn’t mean personally handling every status inquiry. It means designing a system where routine work runs consistently, and anything requiring judgment lands with a human.

That’s what good AP management looks like anyway. AI doesn’t change that principle. It reinforces it.

Why AP is the right place to start with AI adoption

There’s something useful about AP as the starting point for AI adoption in accounting firms. The work is high-volume and well-defined. The questions being answered are factual, not interpretive. The risk of a poorly calibrated response is low and easy to catch. Automating AP workflows reduces processing costs by up to 80% according to Ardent Partners – bringing per-invoice costs from the $12-20 manual range down to under $3 for best-in-class teams.

And the return is immediate. Every status question Agent Mel answers is time your team gets back. 

You don’t need the most ambitious AI transformation to make this moment count. You just need one problem AI can solve better than your current process, prove it, and build from there.

In AP, that problem is already identified. The solution is already built.

To see what Agent Mel can do for your firm, visit the Agent Mel page.

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