Your Finance Team Isn't Slow, Your Tools Are

Finance teams often get blamed for things that aren't their fault. A slow month-end close, a backlog of unapproved invoices, a compliance question nobody can answer quickly. These get viewed as a people problem, but more often than not, they're a tool problem. Give a strong finance team legacy tools, and they'll look slow no matter how hard they work.

Every legacy stack carries technical debt

In engineering, we have a name for systems that were good enough when they were built but now slow down everything around them: technical debt. Every workaround and manual patch adds interest. Eventually, the team spends more time servicing the debt than building anything new.

Most AP stacks carry a lot of it. An ERP configured years ago, approvals that live in email, spreadsheets bridging the gaps between systems. Each piece made sense when it went in. Together, they create a system where people do the integration work by hand.

You can see it in where the hours go. In Tipalti’s global research on AI in finance, a survey of more than 2,100 finance professionals across North America and Europe, 66% say manual work across their team increased over the past year. Resolving errors and chasing invoice approvals are among the top issues slowing them down.

Digitized isn't the same as automated

Here's the part that surprises people: manual work is rising even as more teams say they've automated. In the same research, 31% of finance teams describe their AP as fully automated, up from 7% the year before. Look closer, though, and two-thirds say their automation is mainly OCR and invoice digitization. Only 6% are fully automated with integrated agentic AI.

From an engineering standpoint, that gap makes sense. Scanning an invoice into a system digitizes one step. It doesn't change the architecture. The exceptions, handoffs, and judgment calls the tool can't handle still land on the same people as before.

It's the difference between moving a legacy app to the cloud and rebuilding it to run there. You've relocated it. You haven't modernized it.

What a modern AP stack looks like

A modern stack isn't a new feature bolted onto the old one. Advanced AP automation solutions are built so work moves between steps without manual handoffs. That means automation that does more than read an invoice: agents that can execute multi-step tasks, like matching an invoice to a purchase order, flagging a discrepancy, routing it to the right approver, and checking a supplier's payment details before money goes out. People step in where judgment is needed, not to re-key data from one system into another.

The teams that have moved past legacy tools operate differently. Compared to the average respondent, the 6% running fully automated AP with agentic AI were nearly three times as likely to say they could absorb a sharp economic or policy shift and adapt quickly, and more than two and a half times as likely to be very confident they'd catch a fraudulent change to a vendor's payment details.

Trust is an engineering problem

So why aren't more teams there yet? The research is clear on the barriers: data security (36%), accuracy of automated tools (33%), and control and trust in AI (30%).

Those are fair concerns, and engineering teams should own them rather than dismiss them. Finance can't hand work to a system it can't verify. Modern AP tools need controls designed in from the start: an audit trail for every action an agent takes, defined approval thresholds, and the ability for people to see why a decision was made and override it. Trust isn't a marketing claim. It's a design requirement.

Stop asking your team to outrun bad infrastructure

If a finance team looks slow, the right question isn't "Why aren't they moving faster?" It's "What is our stack asking them to do manually?" In most cases, the answer isn't a talent or effort problem. It's a tooling problem that's been mistaken for one.

Finance professionals already see this. Ninety percent say the right tools are part of scaling AP effectively. The teams acting on it are building the infrastructure that lets them move at the speed the business now requires, instead of asking good people to outrun bad tools.