The forecasting dilemma: predicting stability in an unstable economy

“Past performance is no guarantee of future results.”

Absolutely. So why does your finance and accounts receivable team keep looking backward to try and move forward? Makes no sense.

Maybe it’s the wiggle room implied in “no guarantee.” Maybe there’s no guarantee, but there’s still a chance, right? Sure, if you’re willing to stake the company’s budget, financial results, and your own AR team reputation on “maybe.”

Before you do, think about this. According to a McKinsey report:

• Less than half of companies surveyed use all of the data available to them when forecasting.
• 40 percent of CFOs surveyed think their forecasts are not particularly accurate and that the process takes far too much time.
• Often, operating data sit in disparate systems that aren’t well integrated with financial ERP systems, and there is no standard set of KPIs used across the organization.

That’s a very expensive “maybe.” But hey, you might get lucky.

You may have read recently that Google created a high-resolution AI model for weather forecasting that extends the accuracy out to two weeks (one week being the current limit on confidence, apparently). And it beat the current weather service results for 36+ hour accuracy over 99% of the time. So maybe you don’t need a weatherman to know which way the wind blows, but you do need AI.

Well, that’s never been truer for financial forecasting and credit management, too.

Traditional methods fail to capture the dynamic nature of today’s economy — or the vital role AR plays in the broader order-to-cash cycle. You need better tools and a more agile strategy. Last year’s spreadsheets just won’t cut it. Especially when the economy is unstable, which seems like always these days. Let’s unbox what’s going on.

The pitfalls of traditional AR forecasting

Traditional accounts receivable (AR) forecasting approaches often rely too heavily on historical data and manual processes — limitations that undercut their relevance in today’s volatile economy. These methods struggle to account for the complex, interconnected challenges facing businesses, from shifting customer payment patterns to supply chain disruptions.

One key shortcoming is the inability to provide real-time visibility into cash flow trends. Disconnected financial systems, reliance on static budgeting, and siloed departmental inputs create blind spots, slowing decision-making. For example, businesses with manual AR systems spend 67% more time processing invoices and reconciling payments compared to those using automated solutions​​. This lag directly impacts cash flow, contributing to higher Days Sales Outstanding (DSO) and increasing the risk of late payments.

In a volatile economy, static forecasts based solely on historical trends fall short. As global weirdness, from supply chain constraints to economic uncertainty, continues to reshape industries, businesses need forecasting systems that evolve in real time and anticipate future scenarios, not just reflect the past.

Shifting to dynamic, AI-driven forecasting

The solution lies in leveraging automation and AI-driven tools to build a more dynamic, predictive AR forecasting process. Here’s how businesses can upgrade their approach away from static budgeting to continuous methods that enhance both financial forecasting accuracy and credit management effectiveness.

1. AI and machine learning: precision forecasting in real time

AI and machine learning (ML) tools transform financial forecasting by analyzing vast amounts of historical and real-time data to identify actionable trends. These systems continuously adapt, learning from past patterns while incorporating external economic indicators. For example, businesses using AI-driven AR tools have seen a significant reduction in forecasting errors and faster identification of high-risk accounts​.

AI also boosts accuracy in detecting anomalies, such as duplicate invoices or irregular payment patterns, strengthening credit management and fraud prevention. By integrating natural language processing (NLP), these tools can even parse unstructured data from customer contracts to predict payment behaviors.

2. Rolling forecasts: adaptability meets agility

Here’s an eye-opener: According to Ashish Pareek, VP & Head of Financial Planning & Analysis at Jackson Hewitt Tax Service, only 1% of organizations achieve 90% forecasting accuracy 30 days out. That’s even worse than most weather forecasts. Replacing static annual budgets with rolling forecasts allows businesses to update projections monthly or quarterly based on current data. Rolling forecasts capture shifts in customer payment trends and market conditions, enabling agile decision-making. Companies adopting rolling forecasts report improved cash flow stability and faster responses to market disruptions​.

3. Breaking down silos with cross-functional collaboration

Forecasting isn’t just an AR exercise. Accurate projections require input from sales, operations, and marketing to paint a full picture of customer behavior and supply chain dynamics. For example, sales teams provide insight into upcoming deals, while operations identify potential bottlenecks. Breaking down silos creates a more integrated O2C approach, where every department’s data contributes to smarter forecasts​. Understanding requires insights, and the more insights you get from across the business ecosystem the better your forecasting inputs.

Integrating AR forecasting into the order-to-cash process

Dynamic AR financial forecasting is a vital component of the broader Order-to-Cash (O2C) cycle, which encompasses every stage of the customer journey, from order placement to payment collection. When optimized, AR forecasting doesn’t just improve projections, it enhances the efficiency and accuracy of the entire process.

1. Cash flow and liquidity improvements

Faster, more accurate AR forecasting directly impacts cash flow. Automating invoice delivery, for instance, can save $3 to $13 per invoice while ensuring faster customer payments​​. Real-time visibility into cash flow trends allows finance teams to identify opportunities for early payment discounts and avoid late fees.

2. Reduced DSO

Dynamic forecasting aligns with DSO reduction strategies by pinpointing at-risk invoices and enabling proactive collections. By combining AI insights with automated reminders, businesses can cut DSO significantly, ensuring faster access to working capital​.

3. Strengthened customer relationships

Automation simplifies payment processes and provides transparency, which boosts customer satisfaction. Offering flexible payment options — like digital wallets or ACH payments — enhances the customer experience while reducing friction in the payment process​.

Adapting to economic volatility with scenario planning

In uncertain times, financial forecasting needs to go beyond linear projections. Scenario planning enables businesses to prepare for best-case, worst-case, and likely outcomes, factoring in variables like market downturns or supply chain disruptions. By pairing AI tools with scenario planning, companies can identify key risks and adjust their strategies dynamically, but more importantly, accurately and quickly.

Proactive strategies, such as creating automated alerts for significant deviations in cash flow, help businesses react in real time rather than waiting for the next quarterly review. This agility not only mitigates risks but also positions companies to capitalize on opportunities faster than competitors​​.

Closing the loop: Corcentric’s role in AR forecasting

AR forecasting isn’t just a numbers game; it’s a competitive advantage. With Corcentric’s managed AR solutions, businesses can integrate AI-driven forecasting tools seamlessly into their O2C processes, achieving faster cash flow, reduced DSO, and enhanced customer relationships. By automating manual tasks, optimizing invoice delivery, and providing real-time analytics, Corcentric empowers finance teams to focus on strategic growth.

Ready to elevate your AR management and achieve better future results? Download Corcentric’s “Ultimate Guide to Accounts Receivable Management” and explore how predictive forecasting can transform your cash cycle and position your business for long-term success.