Three New Year’s Resolutions CFOs Are Making About Data and AI

As the calendar turns, chief financial officers are once again sketching out New Year’s resolutions.

Unlike the personal pledges to exercise more or read less email, however, these commitments are shaped by a clear view of cash and striking the appropriate balance between opportunity and risk.

CFOs sit at the nexus of cash flow, risk and insight. Payments data is among the richest, most real-time sources of truth in the enterprise, but it is also fragmented across ERPs, banks, payment processors and third-party platforms.

Applying artificial intelligence to this data can promise faster closes, better forecasting and smarter working capital decisions. Applying it carelessly can invite regulatory exposure, model risk, and financial and reputational damage.

The result for CFOs is a new set of resolutions about making data trustworthy, AI explainable and insights actionable.

The common thread is trust. CFOs want data they can trust, AI they can explain and insights they can act on in real time. That requires investment, not just in technology, but in governance, integration and talent. It also requires knowing when not to automate, when to slow down, and when to insist on transparency over novelty.

If there’s one foundational shift for the New Year, it may be that CFOs are moving from reactive financial management to predictive intelligence systems that can fundamentally change how their businesses allocate capital and manage risk. But beneath that one shift are three key resolutions CFOs are making as they operationalize payments data and AI across accounts payable and receivable.

Read also: What 2025 Taught CFOs About Cash, Control and Resilience

Making Payments Data a Strategic Asset, Not a Byproduct

Payments data has traditionally been treated as exhaust, or something generated as transactions move through AP and AR, then stored for compliance, and rarely revisited unless something breaks. CFOs are now reframing that mindset. Payments data is becoming a strategic asset that can inform liquidity planning, vendor negotiations, fraud detection and customer behavior.

“Our biggest advantage is the depth and diversity of the payment data that we manage … having that consolidated into a single governed environment allows us to create actionable insights,” Boost Payment Solutions Chief Technology Officer Rinku Sharma told PYMNTS in December.

“All of the capabilities needed for a future-state Boost are reliant on having good data governance,” he added.

The shift starts with consolidation. Many finance organizations still run multiple ERPs by geography or acquisition, each with its own chart of accounts and payment workflows. Bank portals, treasury systems, procurement tools and billing platforms add further layers of fragmentation.

CFOs are resolving to create a unified view of payments data across these systems, often through cloud-based data layers or payment orchestration platforms, and frequently with the assistance of AI tools.

One central takeaway from PYMNTS Intelligence’s December “Invoice-to-Pay Automation Tracker Series” is the way in which enterprises are using AI to modernize AP. AI addresses lingering manual gaps by standardizing data fields, improving accuracy and creating a single view of obligations and supplier activity.

When that data is visible and reliable, finance teams can move beyond transaction processing toward analysis that informs working capital strategy, payment timing and supplier management.

The goal is real-time visibility into cash movements spanning what is owed, what is paid, what is delayed, and why. All in real time.

“One thing that all treasury organizations are looking for is visibility into their global activity,” Sebastian Sintes, director of transactional FX at Bank of America, told PYMNTS in September.

“For the corporate organizations that have been making some heavy investments into their system infrastructure, that return on that investment is going to start to be felt in the upcoming years…,” he added.

See also: The CFO’s Real-Time Crystal Ball Turns Liquidity Into Strategy, Not Accounting

Integrating Ecosystems Instead of Building More Silos

One of the paradoxes CFOs face is that innovation in payments has exploded across new rails and mechanisms like real-time payments, virtual cards, embedded finance, blockchain, and buy now, pay later, while visibility has often declined. Each new partner or payment rail can introduce yet another data silo.

According to the PYMNTS Intelligence report “Virtual Mobility: How Mobile Virtual Cards Elevate B2B Payments,” 73% of businesses have yet to automate supplier payments, limiting their ability to gain a comprehensive view of money movement.

A key resolution for the new year is to integrate ecosystems more intentionally. CFOs are re-evaluating their payments and finance technology stacks with an eye toward interoperability. Instead of point solutions that optimize a single workflow, they are prioritizing platforms and partners that can share data seamlessly across AP, AR, treasury and procurement.

The PYMNTS Intelligence report “Time to Cash: A New Measure of Business Resilience” introduced a new metric for agility: Time to Cash. The research found that the legacy era of closing the books and looking backward has given way to a new paradigm, a living, highly integrated cash flow system shaped by 12 operational levers spanning the four dimensions of receivables efficiency, payables control, operational workflows and financial visibility.

The payoff is end-to-end insight. In AP, integrated data can reveal how procurement decisions affect cash flow and supplier risk. In AR, it can link invoicing accuracy, customer experience and payment outcomes. The resolution is to stop layering new tools on top of old ones and instead rationalize the ecosystem around shared data and outcomes.

Read also: Building Inside Legacy Systems Helps CFOs Capture New Payments Value

Unlocking the AI Opportunity Across Financial Operations

As the new year unfolds, the most successful finance organizations may not be those that deploy the flashiest AI tools, but those that systematically turn their payments data into a source of durable advantage to power those same AI tools.

“Folks are just starting to understand that AI isn’t just automation with kind of sexier marketing,” Finexio CEO and founder Ernest Rolfson told PYMNTS in December. “Embracing it as infrastructure lets you use your data as a strategic asset.”

The PYMNTS Intelligence report “Smarter Spend: AI-Powered AP for Data-Based Decision-Making” found that 79% of organizations that use AI report measurable performance improvements, including faster invoice processing, quicker approvals and improved employee satisfaction.

Per the report, 72% of companies said they have adopted AI in AP within the past two years, yet only 22% reported full, at-scale usage, indicating that most deployments remain limited in scope.

“It’s no longer a nice-to-have,” Steve Wiley, vice president of product management at FIS, told PYMNTS in May. “Artificial intelligence is a must-have, and that’s happened very, very quickly.”

“Now, instead of using traditional historical-based models, [finance] departments are expecting generative AI to project cash flows,” he added. “And that’s already the new normal.”

The PYMNTS Time to Cash report found that 83.3% of surveyed CFOs are planning to use at least one AI tool to help with cash flow cycle improvements.

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