Finance Teams Want More Than ERPs Can Give Accounts Receivables

Watch more: Need to Know With Billtrust’s Lee An Schommer

Time waits for no one. Neither does change. And they certainly aren’t waiting for the enterprise resource planning (ERP) landscape to catch up with them.

The humble ERP has served as the financial backbone of large organizations for decades, but as today’s finance teams face growing pressure to accelerate cash flow, improve forecasting and navigate increasingly complex customer relationships, many are finding that their ERP-native accounts receivable (AR) capabilities are no longer sufficient.

“ERP companies are embracing AI just like lots of other software,” Lee An Schommer, chief product officer at Billtrust, told PYMNTS. “But they’re not building AI specifically for end-to-end automation, including accounts receivable. It’s not intelligent, it’s not adaptive to AR workflows.”

As a result, finance teams are compensating. They turn to solutions that layer on spreadsheets, bolt-on other third-party tools and even revert to manual processes to bridge the gap between what their systems are outputting and what they need to chart tomorrow’s growth.

The result is an operational paradox where companies can have more financial data than ever, but less clarity on how to act on it.

Solving this paradox could well define the next phase of enterprise finance technology.

From Recording Transactions to Driving Outcomes

One of the clearest signals that the traditional AR and ERP model is under strain is the growing fragmentation of enterprise systems. The idea of a single ERP instance governing the entire organization has, in many cases, given way to a patchwork of platforms inherited through acquisitions or regional operations.

“On average companies are dealing with about three ERPs,” Schommer said. “You have data silos. And if the ERP can’t handle pulling together the data from these different systems … it just adds to the problem.”

Without a unified view of customer behavior, payment history, and dispute patterns, finance teams can be forced to rely on manual reconciliation or approaches that add more complexity.

The distinction between ERP systems and purpose-built AR platforms becomes clearer when examined at the level of day-to-day operations. Consider the handling of short payments, a common scenario in accounts receivable.

In an ERP system, a short payment is typically recorded as a variance, an exception to be investigated. The burden falls on finance teams to determine whether the discrepancy reflects a discount, a dispute or an error.

A purpose-built AR platform, by contrast, applies contextual intelligence.

“An AR system knows what to do with a short pay,” Schommer said. “It knows the behavior and can actually keep the cash moving.”

That behavioral layer, understanding how specific customers tend to pay, dispute, or delay, is where specialized AR tools differentiate themselves. Instead of treating each transaction as an isolated event, they incorporate historical patterns and predictive logic to guide next steps automatically.

“The ERPs are always going to be the system of record. Their core strength is financial transactions. AR solutions, they’re the intelligence layer,” Schommer said.

“Shifting from process automation to more predictive intelligence,” she emphasized, is the next step.

Where the AR Intelligence Layer Can Have an Impact

The evolution of the AR function reflects a broader trend in enterprise software. As machine learning becomes more embedded, the value of systems increasingly lies in their ability to recommend actions, not just execute tasks.

Companies need systems that can “start predicting cash flow, predicting disputes, understanding where there’s risk, and then helping you proactively manage it,” Schommer said.

Invoice delivery, often overlooked, is another area where purpose-built AR systems address gaps in ERP functionality. Many large buyers require suppliers to submit invoices through proprietary portals, each with its own rules and constraints. Purpose-built AR platforms, designed to handle multiple portal formats and requirements, can reduce such risks by standardizing and automating submission processes.

Collections is another area undergoing transformation. Traditional approaches prioritize accounts based on size or aging, but that model can misallocate effort.

“Don’t just take your big book of outstanding receivables and rank them based on size,” Schommer said. “You need intelligence that helps you prioritize.”

For finance leaders, the case for augmenting ERP is ultimately grounded in performance. Metrics such as days sales outstanding (DSO) and time to payment remain central to how success is measured, and by wrapping collections and invoice delivery in an intelligence layer those metrics can be optimized.

“They see a 23% reduction in DSO and a 25% reduction in days to pay,” Schommer noted. “And … when you have invoicing and payments and you add in collections, there’s an additional 34% reduction in days to pay.”

Cash application, or the process of matching incoming payments to invoices, is also one of the most significant impact areas of AR transformation. Machine learning models can automate much of this work, improving both speed and accuracy over time.

Ultimately, as Schommer emphasized, the previous era of monolithic finance systems is giving way to something more modular and dynamic. ERP remains essential, but so too are the layers atop it that are turning finance from a function that records the past into one that actively shapes the future.