3 Areas Where AI Generates ROI in B2B Payments

While stock markets fret over artificial intelligence (AI) valuations, finance teams are increasingly focused on its deployment.

The evolution of AI in payments started across existing use cases, with models embedded in fraud detection, invoice matching and risk scoring. At first, these tools sat within well-understood parameters: “assistive” solutions designed to help human analysts find anomalies in sprawling datasets. But real gains are beginning to materialize as AI crosses over into proactive, and predictive, territory.

Seen through the chief financial officer (CFO) lens, AI’s capacity to improve financial outcomes falls into three broad categories: operational, strategic and relational.

Operational return on investment (ROI) comes from reducing manual effort: fewer hours spent on invoice matching, vendor reconciliation, and payment dispute resolution. Organizations that deploy generative AI in accounts payable (AP) and accounts receivable (AR) functions can capture measurable improvements thanks to drops in human error and cycle times.

Strategic ROI emerges when AI helps finance leaders shape the company’s liquidity horizon. Treasury teams can use predictive models to dial-in working capital positions, issuing supply-chain financing at the optimum moment and rate. Early pay incentives become instruments of negotiation rather than one-size-fits-all discounts. Risk-weighted payment authorization enables the unlocking of trapped cash without disproportionate downside.

Relational ROI, perhaps the least obvious but increasingly most relevant, enhances trust between buyers and suppliers. Automated status updates, dynamic payment terms, and faster dispute resolution make it easier for vendors to stay loyal, offer better rates, and accept alternative settlement mechanisms. That has a cascading effect on supply chain resilience and enterprise agility.

Zooming out, what’s unfolding is less a story about individual technologies than about a broader trend in the CFO’s purview. Historically, innovations in payments framed themselves around process efficiency: shave a day off the invoice cycle, catch the outlier vendor, trim human error.

Artificial intelligence can amplify those outcomes.

It can clear invoices faster, spot fraud early and model cash positions without analyst intervention. Liquidity becomes a competitive advantage. Supplier influence becomes programmable. Decision windows compress from weeks to seconds. But only if models are integrated meaningfully and responsibly at the organizational and ecosystem level.

Read more: How AI Is Supercharging the Tools CFOs Already Trust

A Shift From AI Pilots to Intelligent Processes

What makes AI in payments different isn’t just the complexity of the data but the weight of decisions. Approving a consumer credit card transaction is measured in dollars; approving a cross-border supplier payment could be millions and could also impact the trust of partners.

While enterprise AI can drive measurable return on investment through process automation, data-driven decisions and even the insourcing of software engineering, as AI models become more central to payment decisions (routing supplier payments, approving early pay, flagging high-risk customers), errors or lack of transparency can lead to reputational, regulatory and financial risks.

Over the past decade, automation in finance has quietly gathered momentum. Robotic process automation in accounts payable, early iterations of machine learning in fraud detection, and punch-out integrations with legacy ERP systems have all nudged CFOs toward a more digitized function.

For CFOs under pressure to optimize working capital and extract more value from payment flows, AI is the long-awaited software unlock. Machine learning systems populating dashboards can project month-end liquidity based on live ERP data. Natural-language models can handle vendor or treasury queries in real time. Automated payment routing reduces human latency and strips cycle time out of the AP window. Compounded at scale, those seconds and minutes translate into working capital gains.

“AI gives [CFOs] cash flow management in a really active sense — real-time visibility, actively spotting trends and risks as they happen,” Eric Frankovic, president of Corporate Payments at WEX, told PYMNTS.

“All of these things were available always,” Frankovic said separately, “but what AI does is it gives the real-time ability to coordinate all of them into one message, then make decisions off those real-time messages.”

According to the PYMNTS Intelligence report “Time to Cash : A New Measure of Business Resilience,” published Oct. 24, 77.9% of CFOs see improving the cash flow cycle as “very or extremely important” to their strategy in the year ahead. That figure jumps to 93.5% among “strategic movers,” those organizations that outperform their peers on growth and digital transformation.

Read more: How 16 Industry Leaders Are Putting AI to Work in B2B Payments

The Pipes, the Plumbing and the Work Behind AI

If AI is the becoming new engine of finance, the fuel is clean, standardized, real-time data. This is where operational leadership either can accelerate or collapses under technical debt. AI’s true value in payments often lives in the “pipes,” or the infrastructure through which data travels, decisions are operationalized and money moves. In AI parlance, the pipes are everything the business needs apart from the model itself: data ingestion services, integration APIs, validation layers, compliance touchpoints and orchestration tools.

Put another way, even the most brilliantly trained AI model becomes inert without frictionless access to relevant data, consistent connectivity with ERPs and banking partners, and a resilient automation layer that programmatically executes payment decisions.

AI transformation isn’t in the model; it’s in the middleware. A vast number of finance teams still operate in fragmented environments: ERP systems running batch processes, treasury tools on 12-hour delay, supplier data living in spreadsheets, risk checks siloed in compliance software. Models running atop fractured workflows don’t just underperform but can expose the business to operational and financial risk.

Still, according to data in the September 2025 edition of The CAIO Report from PYMNTS Intelligence, “How Agentic AI Went From Zero to CFO Test Runs in 90 Days,” none of the surveyed CFOs are willing to grant full, unfettered access to internal data and action permissions to agentic AI systems, and only a slim minority (8.3%) would allow moderate access.

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