CFOs Ditch AI Features to Fix Broken Payment Flows

The enterprise software landscape has sold finance leaders a simple promise: better automation would come from smarter machines.

Optical character recognition would read invoices more accurately, machine learning models would classify expenses faster, and large language AI models (LLMs) would interpret messy vendor communications with near-human fluency, with each capability combining in aggregate to provide frictionless accounts payable (AP) and accounts receivable (AR) operations.

And on paper, artificial intelligence adoption in finance looks like a success story. Invoice capture accuracy has improved dramatically, data extraction rates have skyrocketed and exception detection models are flagging anomalies faster than manual reviewers ever could.

But as intelligence improves, finance teams are increasingly finding that their workflows have not. The root cause is a structural one. Many finance organizations have layered AI capabilities onto legacy workflows rather than redesigning the workflows themselves. An invoice may now be captured perfectly, but it still enters a fragmented process involving multiple systems, inconsistent data and messy exception handling.

As a result, the biggest bottleneck in modern AP and AR operations isn’t one tied to OCR accuracy or LLM intelligence, but everything that sits in between. Against that backdrop, the smartest CFOs today are no longer asking, “Does this tool have AI invoice capture?” and instead are starting to ask, “Is my invoice lifecycle fully autonomous end-to-end?”

See also: The CFO Checklist for Data Readiness in Automation Projects

Moving From Feature Thinking to System Thinking

The dominant mindset in finance transformation has traditionally been an additive one. Accordingly, each incremental AP and AR improvement still relies on a chain of handoffs: invoices are captured, then validated, then routed, then approved. Exceptions cascade through email threads and shared inboxes. In AR, predictive insights may inform collections strategies, but execution still depends on human intervention.

Each step, despite its fundamental interdependency, can often counterintuitively involve a different system, owned by a different team, governed by slightly different rules. This fragmentation can introduce friction that no amount of AI at the front end can resolve.

“We see inconsistent and incomplete data structures, bad data, dirty data,” Michael Younkie, VP of Product Management at Billtrust, told PYMNTS. “We see challenges around legacy ERP systems with limited AR API capabilities.”

New research from PYMNTS Intelligence’s “The Enterprise AI Benchmark Report” reveals that more than 7 in 10 executives (71%) at companies with $1 billion or more in annual revenue believe that organizational readiness is the primary limitation on AI performance. Meanwhile, just 11% think that AI technology itself is the main barrier.

What the emerging architecture suggests is that value no longer lies in optimizing individual steps. It lies in collapsing the steps altogether.

See also: CFOs See Month-End as the Front Line of Finance Automation

Why Coordination Beats Raw AI Power

Technology alone cannot deliver autonomous finance operations capable of scaling in step with the rising demands on enterprise finance functions. PYMNTS Intelligence found in December that 66% of accounts payable teams saw an increase in manual workload over the prior year.

Ultimately, the goal is not to deploy more artificial intelligence, but to build systems where it is seamlessly integrated into the fabric of operations. After all, system coordination beats raw AI horsepower in today’s business landscape, and the foundation of system coordination is standardization. Inconsistent invoice formats, fragmented vendor data and varied payment terms can introduce complexity that no amount of intelligence can fully overcome.

Overcoming this, in practical terms, means that invoice ingestion, validation, approval and payment are not discrete events but states within a unified system. Similarly, order-to-cash processes shift from reactive cycles to dynamic flows that adjust in real time. The system does not wait for a user to trigger the next action; it advances autonomously based on context, reliant on data, rules and decision logic that operate continuously.

The logical endpoint of these trends is what might be called touchless finance. In this model, the majority of transactions flow through the system without human intervention. AP processes invoices and executes payments automatically. AR applies cash, prioritizes collections and resolves routine disputes.

Humans remain essential, but their role changes. They focus on exceptions, relationships and strategy. This is not a distant vision. Elements of it are already in place in leading organizations. What is changing is the feasibility of achieving it at scale.