Cash Forecasting Frequency Is Becoming a New Corporate Fault Line

But, a quarter century into the new millennium, a more fundamental dimension of competition has emerged: the speed at which companies predict, understand and act on their future cash position.

In “Time to Cash: A New Measure of Business Resilience,” a PYMNTS Intelligence collaboration with Bottomline and FIS, the report findings reveal that against today’s dynamic and often challenging macro backdrop, cash forecasting is becoming a front-line competitive capability, no matter the sector or industry.

The survey of 375 U.S. CFOs underscores that companies naturally sort into three personas: Strategic Movers, Stable Operators and Liquidity Constrained. The differences between these groups hinge, more than anything else, on how frequently they analyze and update their view of future cash flow.

This segmentation is not theoretical but a byproduct of the past decade, one defined by increasing uncertainty and volatility. Supply chain instability, inflation cycles, geopolitical unpredictability, erratic consumer demand and, most recently, the acceleration of artificial intelligence adoption have created a landscape in which liquidity positions can shift dramatically in days rather than months.

Under these conditions, the traditional forecasting tempo of monthly, or in some cases quarterly, predictions has become increasingly insufficient. Where organizations once treated forecasting as a periodic snapshot, today’s leaders are coming to treat it as a living system, adjusting constantly to the rhythms of their business environment.

For strategic movers and leading businesses seeking to move up the persona matrix, forecasting and  Time to Cash have become a proxy for an enterprise’s data discipline, its digital maturity, and ultimately its ability to compete.

Read more: Earnings Season Signals CFO Shift Toward Time to Cash and Embedded Payments 

The 3 Personas and Their Forecasting DNA

Strategic Movers sit at the top of this Time to Cash hierarchy. They forecast weekly or even daily, leveraging AI-driven tools that continuously update underlying assumptions and model liquidity scenarios with far greater fidelity than human-generated spreadsheets.

Nearly all these firms say they trust their forecasts, a level of confidence that empowers them to make decisions about spend, investment, and risk with unusual speed. Unlike companies that wait for end-of-month reports to understand where cash is moving, Strategic Movers operate with something closer to real-time liquidity awareness. They treat forecasting as an intelligence function, not a reporting obligation.

The report found that 88% of Strategic Movers expect major Time to Cash improvements from AI within a year. Their approach stands in stark contrast to the reactive posture of many firms that refresh their cash outlooks far less frequently.

Stable Operators represent the middle tier of this landscape. They are not inefficient, nor are they unsophisticated. They simply maintain a forecasting cadence that reflects an earlier era, typically monthly, and rely on established dashboards and manual processes that produce consistent, if not always timely, insight.

For many businesses, this rhythm once made perfect sense. Markets moved more slowly, payment cycles were predictable, and forecasting models based on historical data reliably reflected future conditions.

The Stable Operator is not characterized by lack of competence; rather, it is held back by a lack of urgency.

Read more: Can Your Treasury Function Put Money to Work Immediately? 

The Emerging Competitive Divide

If forecasting is a window into organizational health, Liquidity Constrained firms are the ones peering through a fogged mirror. This group forecasts monthly or even less frequently, often relying on spreadsheets and fragmented data sources. And importantly, only 77% say they trust their forecasts, meaning their decision-making carries a high degree of uncertainty.

The divide among these personas would not be nearly as stark if not for the accelerating influence of artificial intelligence.

As more enterprises adopt instant payments, automated receivables systems, and AI-driven decisioning tools, the importance of forecasting cadence will only grow. Liquidity will continue to flow faster, not slower. Demand signals will arrive in real time. Supplier constraints will emerge with little warning. A company that reviews its cash position once a month will no longer be managing its business; it will be reconstructing its past.

The Time to Cash  study ultimately revealed that the more frequently a company forecasts, the more resilient it becomes. The more resilient it becomes, the more it can reinvest. And the more it reinvests, the more its forecasting models improve.

This cycle feeds on itself. And it suggests that forecasting frequency may become as important a differentiator as digital maturity, customer centricity, or supply chain agility.