Corporate finance teams have spent decades building systems to record what customers owe. Very few of these systems tell them what customers are about to do.
This gap is what AI vendors and banks are now targeting. Accounts receivable has long been managed as a back-office chore, something that organizations assumed would sort itself out as long as invoices were sent on time. Economic pressures and rising delinquencies are forcing us to rethink.
Suppliers of goods and services are frequently chasing buyers, driven by tariffs, economic pressures and volatile interest rates, said Dave Rodda, vice president of software products at Billtrust. piments. The cost of this inaction has become quantifiable.
Working capital of Hackett Group in the United States reconnaissancebased on an analysis of the 1,000 largest publicly traded non-financial companies in the United States, found that $1.7 trillion remains trapped in excess working capital. Receivables account for the largest share of this total, a $600 billion opportunity that DSO saw decline for the second year in a row driven by customer bargaining power and extended payment terms.
This volume of trapped capital is what quickly attracts investment.
From buckets of aging to behavioral intelligence
The change in augmented reality is not just about speed. It’s about what the systems know now.
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Traditional AR reporting acts as a lagging indicator: Teams compile aging reports at the end of the month, categorize overdue bills by days past due, and assess risk using historical averages or fixed credit scores, according to PYMNTS INTELLIGENCE. AI models embedded in enterprise resource planning (ERP) systems can now predict the likelihood of a given invoice being paid late before it is sent, drawing on structured data such as payment history and invoice size along with unstructured data such as sentiment from customer emails and frequency of disputes.
Tailor-made AR platforms take this further by applying a behavioral layer on top of ERP data. Instead of treating each transaction as an isolated event, these systems integrate historical payment patterns to automatically guide next steps. A short batch is usually recorded as a difference in the ERP system, which is an exception that should be investigated.
The purpose-built augmented reality platform applies contextual intelligence: It learns customer behavior and keeps the money flowing, said Lee Ann Schumer, chief product officer at Billtrust. piments.
Track metrics. Businesses using custom-designed AR platforms that combine invoicing, payments and collections see a 23% reduction in days sales outstanding and a 25% reduction in days paid, with an additional 34% reduction when the three functions work together, Schumer said.
Banks are moving to capture the augmented reality automation market
Institutional players are now moving to own a larger share of the AR stack.
Truist Financial launched an AI-powered integrated receivables platform in February, using machine learning to automate the matching of payments to invoices for commercial and corporate clients, the company said. He said. The platform scans both traditional checks and electronic payment rails, applies business rules to automatic matching payments, and includes an intelligent transfer capture feature that extracts data directly from emails to reduce exceptions.
This launch comes as mid-market companies in North America show increasingly widening performance gaps based on the strength of their augmented reality update. Companies that integrate digital tools into their working capital strategy achieve materially stronger bottom lines than those that don’t, with AI most commonly applied in customer onboarding, identity verification and financial planning, according to the 2025-2026 Corporate Working Capital Growth Index. PYMNTS INTELLIGENCE In cooperation with Visa.
Where adoption still lags behind
The gains have not yet reached most companies.
83% of companies have yet to fully automate their AR operations, according to PYMNTS Intelligence data from a report titled “From Friction to Flow: AR Automation in 2025.” a report. Data fragmentation is the primary limitation.
On average, companies run three ERP systems, creating data silos that make it difficult to build a unified view of customer behavior, payment history and dispute patterns, Schumer said. Without standardized data, predictive models produce unreliable output. The intelligence layer is only as useful as the infrastructure beneath it.
Among mid-market companies in North America, U.S. companies still rely more heavily on traditional payment methods than their Canadian counterparts, and where card acceptance is more widely used as a receivables strategy, revenue losses are lower, the PYMNTS-Visa Index found. This gap is not due to customer behavior, but rather to company-level choices about payments infrastructure.
