Time waits for no one. Doesn’t change. They certainly aren’t waiting for the ERP scene to catch up with them.
The humble ERP system has been the financial backbone of large organizations for decades, but as today’s finance teams face increasing pressure to accelerate cash flow, improve forecasting, and navigate increasingly complex customer relationships, many are finding that ERP’s native Accounts Receivable (AR) capabilities are no longer sufficient.
“ERP companies are embracing AI just like a lot of other software,” Lee Ann SchumerChief Product Officer at Car confidencePYMNTS said. “But they’re not building AI specifically for end-to-end automation, including accounts receivable. It’s not intelligent, and it doesn’t scale to AR workflows.”
As a result, finance teams compensate. They are turning to spreadsheet solutions, installing other third-party tools, and even returning to manual processes to bridge the gap between what their systems produce and what they need to plan future growth.
The result is an operational paradox, where companies have access to more financial data than ever before, but with less clarity about how to act on it.
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Solving this paradox could define the next phase of enterprise finance technology.
From recording transactions to driving results
One of the clearest signs that the traditional AR and ERP model is under pressure is the increasing fragmentation of enterprise systems. The idea of a single ERP instance governing the entire organization has, in many cases, given way to a collection of platforms inherited through acquisitions or regional operations.
“On average, companies deal with about three ERP systems,” Schumer said. “You have data silos. And if the ERP system can’t handle collecting data from these different systems…it compounds 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 methods that add more complexity.
The distinction between enterprise resource planning (ERP) systems and purpose-built augmented reality (AR) platforms becomes clearer when examined at the level of day-to-day operations. Consider handling short payments, a common scenario in accounts receivable.
In an ERP system, a short payment is usually recorded as a variance, which is an exception that should be investigated. The burden is on finance teams to determine whether a discrepancy reflects a discount, dispute, or error.
In contrast, a purpose-built augmented reality platform applies contextual intelligence.
“The AR system knows what to do with short pay,” Schumer said. “He knows the behavior and can actually keep the money flowing.”
This behavioral layer, which understands how specific customers tend to pay, object or delay, is where specialized AR tools differentiate themselves. Instead of treating each transaction as an isolated event, it integrates historical patterns and predictive logic to automatically guide next steps.
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“ERP systems will always be the system of record. Their core strength is financial transactions. Augmented reality solutions are the intelligence layer,” Schumer said.
“The shift from process automation to more predictive intelligence is the next step,” she stressed.
That’s where the augmented reality intelligence layer can make an impact
The evolution of AR functionality reflects a broader trend in enterprise software. As machine learning becomes more integrated, the value of systems increasingly lies in their ability to recommend actions, not just carry out tasks.
Companies need systems that can “start forecasting cash flow, predicting conflicts, understanding where risks are, and then helping you proactively manage them,” Schumer 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 of which has its own rules and restrictions. AR platforms specifically designed to handle multiple portal formats and requirements can reduce these risks by standardizing and automating submission processes.
Collections are another area undergoing transformation. Traditional approaches prioritize accounts based on size or age, but this model can misallocate efforts.
“Don’t just take your big book of outstanding receivables and sort it based on size,” Schumer said. “You need intelligence to help you prioritize.”
For financial leaders, the case for scaling up an ERP system is ultimately based on performance. Metrics like days sales outstanding (DSO) and time to payment remain key to how we measure success, and by grouping collections and invoice delivery into a smart layer, these metrics can be improved.
“They’re seeing a 23% reduction in DSO and a 25% reduction in paydays,” Schumer noted. “And… when you have invoices and payments and you add collections, there’s an additional 34% reduction in paydays.”
Cash App, or the process of matching incoming payments with invoices, is one of the most important areas of influence in AR transformation. Machine learning models can automate much of this work, improving speed and accuracy over time.
Ultimately, as Schumer emphasized, the previous era of monolithic financing systems is giving way to something more modular and dynamic. An ERP system remains essential, but so are the layers above it that are transforming finance from a function that records the past to one that actively shapes the future.
