AI Power: The Technology Transforming Accounts Receivable

Persistent economic challenges—from lingering inflation to ongoing tariff uncertainty—continue to place pressure on business operations and cash flow. For finance leaders, this volatility has intensified the need to optimize accounts receivable (AR), turning a traditional back-office function into a strategic priority. As companies seek to unlock working capital and fuel growth, the demand for faster payments and real-time visibility has never been more urgent.

However, many organizations, especially small to mid-sized businesses (SMBs), still rely on manual processes and paper checks, limiting transparency and prolonging delays. In this environment, artificial intelligence (AI) is gaining traction as a transformative force—replacing outdated workflows with predictive, data-driven automation. Autonomous AI ecosystems in AR are now continuously scanning financial signals, adapting in real time to flag emerging risks and optimize receivables with minimal human intervention.

The stakes are high: Middle-market firms lose an average of 3.1% of revenue—approximately $14 million annually—to payment collection uncertainties. Meanwhile, AI’s ability to deliver personalized, behavior-driven outreach is also improving customer satisfaction. As finance departments face rising expectations to do more with less, AI in AR is evolving into autonomous finance, where intelligent agents orchestrate collections, customer engagement and forecasting in real time.

• Outdated Systems, Growing Risks
• From Reactive to Predictive
• From Collections to Connections
• Future Vision: Relationship Intelligence

Outdated Systems, Growing Risks

Rising payment delays and outdated AR processes are eroding cash flow and increasing risk, with many firms failing to spot the early warning signs of financial trouble.

Cash flow can’t wait.

What is clear is that manual AR systems are no longer fit for what is becoming an increasingly complex job. As transaction volumes rise and finance leaders are asked to play more strategic roles, outdated processes and siloed reports are leaving teams in the dark. As PYMNTS Intelligence notes, AR reports are often lagging indicators, compiled monthly and based on past data. As such, they offer limited predictive value and fail to reveal why customers are delaying payments—critical knowledge in today’s environment.

Falling behind can mean trouble ahead.

The financial toll of poor AR processes is staggering—lost revenue, higher write-offs and wasted time. PYMNTS Intelligence research found that firms with more than 6% of invoices overdue lose an average of 3.5% of revenue annually. Amplifying the issue, firms often chase the same overdue invoice four to six times, wasting precious staff hours and delaying cash recovery.

Worse yet, erratic customer payment behavior can signal deeper financial trouble ahead. A Creditsafe study reveals that 86% of businesses regularly deal with up to 30% of monthly invoiced sales coming in late, often without realizing that this pattern may predict a customer’s future bankruptcy risk. Only 3% of companies are accurately identifying red flags in customer payment behavior, despite evidence showing such trends often precede insolvency. These findings underscore a harsh reality: Businesses operating with fragmented, outdated AR tools are not just inefficient—they’re vulnerable.

From Reactive to Predictive

AI-powered AR solutions are enabling finance teams to forecast delinquencies, reduce collection costs and automate interventions—marking an ongoing journey from reactive processes toward predictive, value-driven relationship management.

Middle-market CFOs are responding accordingly. According to PYMNTS Intelligence, 55% of these executives are willing to pay a 3% fee to automate invoice approval and payment, up from the current 2% average—demonstrating the growing urgency to improve AR. The payoff is unmistakable: Firms using AI report three- to five-day reductions in days sales outstanding (DSO), 30% lower collection costs and more than 12% fewer delinquencies and write-offs.

At its core, AI-driven AR management enables earlier intervention. ML models track both structured data (such as invoice size or terms) and unstructured inputs (including email sentiment and dispute history) to generate risk scores per customer or invoice. Finance teams can then act before issues escalate—offering new payment terms, adjusting credit limits or initiating tailored communications based on predicted behavior.

The result? Collect faster. Waste less.

Advanced segmentation also enables more intelligent collections strategies. Customers can be grouped by behavioral archetypes—those who consistently pay slightly late, frequently dispute or are unresponsive until prompted—informing customized messaging and payment policies.

These advanced systems also scale seamlessly. AI embedded into enterprise resource planning (ERP) systems analyzes payment patterns, macroeconomic conditions and behavioral signals to deliver dynamic, always-current AR insights. With this visibility, finance teams can align collections with customer financial health—rather than relying on outdated metrics.

As highlighted by FIS’s recognition in the IDC MarketScape, leading providers are turning AI into a critical differentiator in financial operations. With the right solutions, AR becomes a driver of business resilience and growth.

From Collections to Connections

Intelligent AR ecosystems are transforming collections into a customer-centric discipline, using behavioral insights and personalized outreach to encourage timely payment and strengthen relationships.

Smarter outreach makes for happier customers.

Early detection of risk means companies can engage proactively—not reactively—when customers signal distress to dramatically improve long-term retention and reduce bad debt. AI models ideally flag changing behaviors in real time, offering firms the chance to revise terms or open a dialogue before delinquency escalates. The reality, however, is that everyone is hindered by older ERPs and other back-office systems. The objective is for AI agents to detect micro-shifts in customer behavior milliseconds after they occur, triggering prescriptive actions that preempt delinquencies before they materialize.

Behavioral science plus machine learning equals better collections.

AI also enables behavioral segmentation to guide both incentives and messaging. Businesses can experiment with targeted loyalty rewards or test urgency-focused language to see what works best—applying behavioral economics at scale.¹ These experiments, supported by machine learning, help optimize outreach tactics over time.

Moreover, AI-driven AR environments can function as de facto customer relationship management (CRM) systems within the finance function, tracking engagement and preferences to tailor communications. High-value clients may respond better to high-touch messaging, while small accounts may benefit from simple, automated outreach.

As seen in FIS’s launch of its Revenue Insight solution, finance leaders are recognizing that customer satisfaction and AR performance are not mutually exclusive. By embracing AI-powered features that serve both goals, they can transform a legacy pain point into a source of competitive differentiation. A recent article reported that AI-powered AR automation reduced dispute resolution times by three weeks—a strong indicator of smoother customer interactions.

Future Vision: Relationship Intelligence

The challenges facing AR teams today—payment delays, write-offs and fractured customer relationships—demand smarter solutions. Manual workflows simply cannot keep pace with modern finance. AI-powered AR systems offer predictive insights, automated outreach and real-time risk assessments that help businesses stay ahead, paving the way for a future defined by relationship intelligence—where AR combines financial optimization with deeper customer engagement.

PYMNTS Intelligence recommends the following actionable roadmap for companies looking to optimize their receivables by adopting AI-driven automation:

• Assess current processes and define objectives.
  Map invoice generation, collections and reconciliation workflows. Set clear goals such as reducing DSO or improving forecast accuracy.
• Choose the right AI systems and pilot smartly.
  Evaluate vendor solutions (from modular platforms to full-service providers), then run a pilot program using a subset of invoices or customer segments.
• Integrate with existing systems.
  Ensure seamless application programming interface (API)-based integration with ERP, CRM and accounting platforms to centralize payment data, improve visibility and enable automation.

With these steps in place, companies can transform AR from an administrative burden into a strategic asset—optimizing cash flow, mitigating risk and enhancing customer relationships through intelligent automation.

The future of finance isn’t just faster—it’s autonomous. At FIS, we’re building intelligent AR ecosystems where AI agents don’t just automate workflows; they anticipate needs, adapt to behavior, and improve every customer interaction. This is more than transformation—it’s the dawn of relationship intelligence in finance.”

^{1. FIS. Unlocking liquidity and flow of funds. Leverage artificial intelligence and machine learning to transform accounts receivable management. White Paper.↩}