What AI Delivers Versus What It Doesn’t for B2B Payments

 And that’s becoming situation critical for the enterprise software space.

As of Wednesday (Feb. 4), more than $800 billion in market value was wiped out of the enterprise technology sector after Wall Street analysts pointed to the disruptive potential of new enterprise AI tools from providers such as Anthropic designed to automate processes like contract reviews and legal briefings.

The growth of enterprise AI is emerging at a key juncture in the enterprise software landscape where, after years of software lockups across inflexible and monolithic solutions, corporate customers are increasingly demanding more from their B2B vendors because they know that more is possible.

For B2B payments, this moment is especially consequential. Payments sit at the intersection of finance, operations, risk and trust. They are repetitive, data-rich and historically manual, representing exactly the sort of environment where AI should shine. At the same time, they are unforgiving when it comes to workflow failures and downtime.

The challenge for C-suite leaders is distinguishing between artificial intelligence applications that genuinely improve decision quality and resilience, and those that simply accelerate existing inefficiencies or may prove ultimately to be too fragile for the security-critical heavy-lifting many enterprise systems perform.

See also: The Classic ERP Model Is Dying. What Comes Next?

Where AI’s Early Wins Are Accumulating

If there is one area where AI has already proven its value in B2B payments, it is fraud detection. For enterprises processing millions of invoices or payments, the impact of AI’s capabilities to counter modern fraud techniques are tangible: fewer blocked legitimate transactions, faster resolution times and lower fraud losses.

The same dynamic applies to cash forecasting, long a resource bottleneck in corporate finance. Forecasts often depend on manual inputs from business units and spreadsheet-based assumptions that can age poorly. AI-driven models, by contrast, can update in near real time, learning how customers actually pay rather than how contracts say they should. They can frequently incorporate seasonality, behavioral drift and macro signals automatically.

According to the PYMNTS Intelligence report “Time to Cash: A New Measure of Business Resilience,” 77.9% of chief financial officers (CFOs) see improving the cash flow cycle as “very or extremely important” to their strategy in the year ahead.

“There’s a continuous evolution and … dynamic disruption in finance that requires CFOs to harness data and AI to make finance more efficient, more effective and substantially more strategic,” Raj Seshadri, chief commercial payments officer at Mastercard, told PYMNTS in an earlier interview.

Perhaps the least visible but most powerful impact may be in automating accounts payable and receivable. AP and AR are still riddled with manual steps: matching invoices to purchase orders, resolving exceptions, chasing discrepancies, prioritizing collections. AI excels at this kind of probabilistic, pattern-heavy work, due to its capability for learning which mismatches resolve themselves, which customers respond to which nudges and which invoices may be likely to become overdue.

The PYMNTS Intelligence report “Smart Spending: How AI Is Transforming Financial Decision Making” found that more than 8 in 10 CFOs at large companies are either already using AI or considering adopting it.

See also: Vibe Coding Comes to Finance as CFOs Embrace Conversational AI

Where AI Still Falls Short Across the Finance Function

The market’s recent unease around AI-driven enterprise software may reflect a broader concern around over-automation of mission-critical processes before governance frameworks are ready. Automating contract reviews or payment approvals at scale can deliver efficiency, but it also concentrates risk. A flawed model, a data drift issue or a misaligned incentive can propagate errors faster than any human team ever could.

For all its strengths, artificial intelligence remains limited in areas that require contextual judgment, ethical reasoning or accountability. B2B payments decisions often involve trade-offs that extend beyond data patterns. Should a delayed payment be escalated, renegotiated or quietly absorbed to preserve a strategic relationship? Should a borderline transaction be approved to avoid operational disruption, or blocked to enforce policy consistency?

At the same time, buyers are becoming more discerning. They are asking where models are trained, how decisions can be explained and what happens when the system is wrong.

It’s against this backdrop that the shift underway is increasingly not about whether AI belongs in B2B payments. The real question facing CFOs, same as with any other digital transformation initiative, is how thoughtfully and effectively it can be deployed.

For all PYMNTS B2B coverage, subscribe to the daily B2B Newsletter.