BTR: AI Targets Revenue Leakage as Healthcare Systems Rework Revenue Cycle Operations
Jacob Shurbet, PwC Health Services
WASHINGTON, DC, UNITED STATES, June 9, 2026 /EINPresswire.com/ -- Healthcare providers are reworking revenue cycle operations as administrative complexity and reimbursement gaps continue to erode margins. Billing, coding, and payer interactions remain a major cost center, while denials, underpayments, and process breakdowns limit cash realization and increase cost-to-collect across the revenue cycle. Adding insult to injury, fragmentation across systems, workflows, and data sources compounds the problem, making it difficult to trace where revenue is lost or delayed.
In a recent BizTechReports executive vidcast interview, Jacob Shurbet, a principal at PwC, described the operational reality behind that pressure. “Your revenue cycle is a continuum of operations, and you have to get everything right for it to actually work,” Shurbet said.
Small breakdowns at any point in that continuum, from eligibility verification, coding accuracy, documentation completeness, to payer adjudication, will likely propagate downstream, increasing rework, delaying payment, and ultimately suppressing realized revenue.
From Cost Center to Strategic Lever
Revenue cycle performance sits at the center of margin management for healthcare organizations. Cost reduction remains a primary objective, but it no longer defines the overall opportunity.
“Most people quickly go to reduce cost…but there’s also an upside and an additional yield component that senior leaders should consider,” Shurbet said.
That upside takes the form of revenue already earned but not yet collected. It is in this context that AI can help organizations identify underpayments at scale, surface contract discrepancies, and recover dollars that would otherwise remain buried in transactional noise. Even small percentage improvements can make a big difference when applied across millions of claims, explained Shurbet.
This is because AI exposes revenue leakage embedded in billing, coding, and payer interactions. It can catch underpayments, missed charges, and contract misalignments that often accumulate across transactions. In this scenario, additional revenue capture comes from executing a more precise and intelligent lifecycle claim strategy rather than increasing patient volume.
“Payment integrity becomes a measurable outcome in which agentic systems monitor claims at a granular level and flag discrepancies in near real time,” said Shurbet.
That requirement for precision brings into focus the structural barrier that has limited prior improvement efforts.
Fragmentation Remains the Central Constraint
Because AI adoption has outpaced organizational readiness, many health systems are experimenting without establishing the structures required to scale results.
Citing data from the Healthcare Financial Management Association, Shurbet noted that most organizations are using AI in some capacity while far fewer have mature governance in place to extract value from those investments.
“Everybody wants to use AI… but there’s still a lot of fragmentation of what and how things are done. This gets in the way of achieving a coordinated outcome,” he said.
For healthcare leaders, this fragmentation translates into stalled initiatives, inconsistent outcomes, and an inability to connect AI investments to measurable financial improvement. While point solutions based on AI can generate localized gains, they will likely fail to move enterprise-level metrics, such as days-in-accounts-receivable or net-collection-rates.
This is because core systems generate large volumes of data but operate independently which results in electronic medical records, billing platforms, and clearinghouses not being shared in a unified data model.
“Data is the ground zero for AI,” Shurbet said.
Integrating this environment will determine whether AI operates as a point solution or as a system-wide capability.
Resolving fragmentation requires a shift in how automation itself is defined.
The Shift from Automation to Orchestration
When integration happens, AI can change the unit of work from task execution to outcome delivery. In practice, that means resolving a denied claim without human intervention or confirming that each service rendered is coded, billed, and reimbursed correctly the first time.
“Historically, automation was about bits and pieces of a process… we’re saying, what is the outcome you’re trying to get to? And how can we leverage AI to achieve strategic results?” Shurbet said.
The question is easier posed than answered because revenue cycle workflows contain constant variation. Each claim reflects different clinical conditions, payer rules, and contract terms. Properly implemented, AI agents can assess context, determine next actions, and coordinate across systems. The process adapts to the claim rather than forcing the claim through a fixed workflow.
That shift from task execution to outcome accountability changes how work is distributed across both technology and people.
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Airrion Andrews
BizTechReport
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