B2B Invoice-to-Cash KPIs Get Big Boost From AI

Watch more: From Data to Decisions: How AI Is Optimizing Invoice-to-Cash and Payments

Finance is full of stubborn bottlenecks. And there are few places these bottlenecks tend to cluster more than across the invoice-to-cash lifecycle.

What should be the lifeblood of a business can often morph into a tangle of manual invoices, delayed approvals, missed payments and ballooning days sales outstanding (DSO). At many organizations, accounts receivable teams spend more time chasing down checks than steering financial strategy.

These incremental inefficiencies compound into real balance-sheet drag. But a shift is underway: more companies are realizing invoice-to-cash can be used as a lever for speed, resilience and customer loyalty.

To learn more, PYMNTS sat down with Ryan Frere, executive vice president and general manager B2B at Flywire, and Chris Couch, head of product B2B at Flywire, to explore how artificial intelligence (AI) orchestration, embedded payments and human oversight are rewriting the playbook for receivables.

“AI is an inflection point in technology, kind of like the internet was; maybe like the iPhone was … and not being involved in it is going to leave you behind,” Couch said.

While the finance function has spent the last 18 months ping-ponging between excitement and caution on generative AI, there’s been a burgeoning movement toward pilots and production use.

“What companies are starting to see is, hey, there are real applications where AI can help. It can help us be more efficient … reduce cost … maybe get paid faster … Instead of exploring it now, we’re going to start rolling out a few things,” Frere said.

The subtext: finance leaders don’t need a grand theory of everything AI. They need focused use cases with measurable outcomes.

Invoice to cash has historically been an underinvested corner of enterprise finance. While procurement, payroll and even tax compliance have seen waves of digitization, receivables often remained fragmented across enterprise resource planning (ERP) systems, PDFs and spreadsheets. The result has been predictable: clerical errors, lengthy reconciliation cycles and inconsistent customer experiences.

Beyond that, while in most enterprises ERPs are the system of record, they are commonly not systems of engagement.

“ERPs are usually really bad at customer experience and customer relationships,” Couch said.

Against this operational backdrop, DSO ultimately became the key metric, and it rarely told a flattering story. But if “cash app” is the perennial time sink, “chasing” is the hidden tax: who to contact, when, with what tone and content and how to escalate.

“The more transparent you can make this billing process … the better experience it is overall, [and] the better results you see,” Frere said. “You’re starting to see customers say, ‘Hey, wait, I want to have better insight … I want to have a better idea of what I owe, when I owe it, what I’m being billed for. I want to have a more conversational relationship around this.’”

It’s here where AI is having a measurable impact. In the AI-augmented invoice-to-cash framework, an AI system can flag overdue payments instantly, analyze a customer’s past behavior and trigger a tailored follow-up, sometimes an automated nudge, sometimes a recommended outreach for a human agent.

“Normally, a process like this … could take several hours to several days … Now you’ve got an expert at your fingertips that can create professional chasing cadences within a minute or two,” Couch said. “You can let the AI handle it on its own, or you can give it a prompt and tell it what you’re wanting to do.”

Adopting AI in finance requires a new mindset.

Couch cautioned that adopting AI in finance isn’t a copy-paste of past software rollouts. “When you bring AI into the mix … you have to start thinking in a way that you haven’t thought of before,” he said. “It’s really a completely different paradigm.”

That mindset can show up in implementation plans (define rules, limits, jurisdictions, branding), in operating procedures (where humans approve and when), and in an openness to let the machine propose a better way, but holding it accountable with data.

It’s also important to pick real problems, not grand gestures, then measure outcomes and scale what works.

The most profound change may be less visible: breaking down data silos. Traditional finance systems left accounts receivable (AR), treasury and customer success in separate lanes. AI-driven invoice-to-cash platforms unify these streams, providing a single view of AR, risks and customer behavior.

This integration allows for more than just faster collections. Workflows become not just automated reminders, but contextual actions informed by behavioral data, credit profiles and even external signals like industry downturns. The result is a system that is proactive rather than reactive.

“AI is … a team member, and not just a member, but an expert team of team members,” Couch said. In the doing mode, it can process “chaotic, undefined, non-normalized data very quickly” for cash application or for loading invoice data from contracts. In the advising mode, it can analyze the customer base to surface high-risk accounts, upsell opportunities or churn signals — patterns that would take humans days to uncover.

The takeaway: invoice-to-cash is no longer just a cost center. Done right, it becomes a growth lever. To get there, leaders should:

• Start small and measurable. Pick specific problems like chasing optimization or payment matching.
• Blend AI with human oversight. Let machines propose — but humans decide.
• Unify data streams. Break down silos so insights flow across AR, treasury and customer success.
• Scale what works. Expand pilots into broader adoption based on proven outcomes.

As Frere put it: “You want to have a use case; you want to have a real problem you’re solving for. This is not just a, ‘Hey, I’m going to drop AI on top of everything and see what happens.’”

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