The B2B ecosystem, a once-staid environment populated by risk-averse number crunchers, is experiencing a recalibration. For chief financial officers and finance teams, AI’s most durable value lies not in grand reinvention, but in the incremental overhaul of how money moves, how risks are managed, and how decisions are supported.
PYMNTS is set to track that journey with a month-long virtual event, “B2B.AI: The Architecture of Intelligent Money Movement.” It’s a program made for CFOs, treasurers, product leads and platform builders who want the playbooks behind AI that have proven value in B2B.
In the first week of events, a plausible roadmap for forward-thinking finance leaders is beginning to emerge.
Read also: Building Inside Legacy Systems Helps CFOs Capture New Payments Value
A Strategic Pivot From Moonshots to Mundanity
Rather than rewrite the rules of finance overnight, the consensus is growing around the notion that the smarter play for B2B leaders is to deploy AI in high-volume, low-risk tasks first, reinforcing systems, data quality, governance and user trust.
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“This is a brilliant time to be doing active learning and to be getting sharp on what is going on in your industry,” Athena Capital Investment Partner Serena Dayal said in a conversation with PYMNTS CEO Karen Webster published Thursday (Oct. 9). “The smartest leaders are experimenting deliberately, testing in small, controlled ways rather than overhauling core systems too soon.”
That thinking has resonated. In finance, AI’s earliest traction has clustered around fraud detection, reconciliation, compliance, invoice coding and exception processing. These are routines where performance can be measured, models can be validated, and integrity can be consistently maintained. The broader transformation of the finance function may be slow and constrained, but it is, in many respects, irreversible.
“Modernization works best when you take out the biggest bottleneck, and the biggest bottleneck is the labor today,” Finexio CEO Ernest Rolfson told PYMNTS in an interview also published Thursday. “It’s the manual entry, the fragmented workflows, the many unnecessary inbound phone calls. That frees capacity immediately.”
The PYMNTS Intelligence report “From Bottleneck to Breakthrough: AP Transformation in 2025,” created in collaboration with Finexio, found that AI-powered targeting can achieve 90% accuracy in predicting supplier adoption of digital payment methods.
If there is a poster child for low-glamour value capture in finance, it is accounts payable (AP) and accounts receivable (AR). In an era of supply chain fragility and liquidity stress, slow AP workflows carry outsized risk. Late payments can disrupt supplier operations, damage relationships and reduce resilience across ecosystems.
“AR and AP automation is no longer a nice-to-have. These are becoming very much table-stake functions for businesses,” Pavan Krishna, vice president of B2B Payments Products and Partnerships, Merchant Services (U.S.), at American Express, said in another PYMNTS interview published Thursday.
See also: Why AI’s Transformation of Finance Function Is Incremental but Irreversible
Mapping the Sequence of Value in AI-Enabled Finance
Against a backdrop of marketplace innovation, with firms like Ramp, Coupa and Spara all launching new AI agents this week to autonomously execute B2B workflows, CFOs can’t afford to sit still.
A new playbook for capturing ROI is emerging. First, standardize, clean and catalog foundational data. AI is only as good as the inputs it ingests.
“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 interview published Tuesday (Oct. 7).
“You can’t apply AI until you have really good-quality data at scale,” she added.
Next, retrofit automation into high-volume financial operations, like invoice ingestion, payment matching, reconciliation and exception routing. These are domains where ROI is observable.
Parallel to that, layer in AI risk controls and anomaly detection, like fraud flags, compliance deviation alerts and behavioral outliers. Once those elements stabilize, consider AI-assisted forecasting, scenario design, predictive liquidity allocation or agent-based assistants for financial research. Models in those domains must earn trust gradually.
Throughout, bake in human-in-the-loop guardrails to catch errors, correct drift and calibrate models with domain judgment.
Finally, as confidence grows, the finance function becomes less about pushing numbers through processes and more about orchestrating information flows across planning, operations and capital strategy.
Register now to access all streaming and on-demand episodes from the B2B Payments 2025 event series.
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