That left finance teams projecting trends from stale data and often kept liquidity management stuck in the rigid lanes of reconciling what had already happened.
Now, as global volatility continues to shape business, CFOs find themselves managing more than balance sheets and budgets. They are more and more responsible for managing uncertainty itself. The traditional cash conversion cycle, an accountant’s lens on efficiency, tells companies how well they performed, but not how to improve in the moment.
That lag is increasingly a liability. However, the PYMNTS Intelligence report “Time to Cash™: A New Measure of Business Resilience,” published Friday (Oct. 24), introduced a new metric for agility: Time to Cash™. It revealed that the legacy era of closing the books and looking backward has given way to a new paradigm, a living cash flow system shaped by 12 operational levers spanning the four dimensions of receivables efficiency, payables control, operational workflows and financial visibility.
The research’s core insight is that velocity itself has become a strategy. Enterprises that accelerate the movement of cash through automation, artificial intelligence forecasting and operational integration are outpacing competitors not only in efficiency but also in adaptability. They forecast more frequently, manage risk with greater precision, and reinvest with confidence.
Those who lag behind, bound by manual workflows and siloed systems, are increasingly becoming constrained by their inability to see what’s ahead.
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Read also: Building Inside Legacy Systems Helps CFOs Capture New Payments Value
AI and the Rise of the Predictive CFO
In the past, forecasting was largely an exercise in educated guesswork, made up of linear projections built on historical data and subject to human bias. But in an economy where shocks can cascade overnight, static forecasting has lost its relevance.
Technology sits at the center of this transformation.
Real-time systems ingest transactional data, market conditions and payment patterns, creating rolling forecasts that function as radar, detecting cash flow turbulence before it hits. This ability to model liquidity dynamically enables CFOs to shift from explaining results to influencing them. The finance office becomes less a mirror of what has occurred and more a windshield through which to navigate uncertainty.
“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 published Oct. 9. “It’s the manual entry, the fragmented workflows.”
The PYMNTS Intelligence report found that 97% of firms still use spreadsheets, but their CFOs are trying to quit.
The transformation of forecasting and cash management reflects a broader automation curve sweeping across the enterprise. In this new model, the CFO’s role is shifting from gatekeeper to orchestrator. Traditional finance operations, like approving invoices, reconciling accounts and managing cash positions, are increasingly handled by intelligent systems capable of executing tasks autonomously.
Companies aren’t asking if they should try AI. They’re asking how AI will improve their cash flow, forecasting accuracy or decision speed, Emanuel Pleitez, head of finance at Finix, told PYMNTS in an interview published Oct. 8.
Confidence is also cultural. As finance teams become more data-driven, they cultivate a sense of predictability that permeates the organization. Marketing, operations and product divisions can plan with greater assurance when liquidity is visible and predictable. This interconnected confidence becomes a form of resilience, allowing companies not merely to survive volatility but to use it as an opportunity for growth.
See also: Making Sense of the Liquidity Hub Treasury Model
Liquidity as a Competitive Weapon
The distance between a company’s receivables and its realized cash is no longer a mundane accounting metric; it is the heartbeat of competitiveness.
Manual forecasting doesn’t just slow decision-making; it obscures risk. Errors compound across versions, assumptions get outdated, and models become less about prediction and more about explanation.
The next frontier isn’t just knowing where cash is but anticipating where it might be. Predictive liquidity models can identify when receivables will convert to cash, when payables might strain working capital, and how external variables like currency shifts or seasonal trends could affect margins.
“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 during a discussion published Oct. 7 for the B2B PYMNTS 2025 event, “B2B.AI: The Architecture of Intelligent Money Movement.”
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