Revenue Cycle Management: Healthcare’s Next Margin Defense

Revenue cycle management was once treated as a back-office function: submit the claim, chase the denial and collect the payment. That model is becoming increasingly difficult to sustain.

Payer friction, regulatory changes, rising labor costs and greater patient out-of-pocket exposure are putting pressure on healthcare providers from multiple directions. At the same time, healthcare organizations are being asked to improve margins without compromising access, quality or the patient experience.

This is creating a structural shift in the role of RCM. The function is moving from administrative infrastructure to a strategic financial capability — one that sits directly at the intersection of reimbursement, operating margins and technology adoption.

The opportunity is therefore larger than simply automating billing. As the cost and complexity of getting paid increase, the ability to capture revenue efficiently becomes a competitive advantage.

The Revenue Cycle Is Under Pressure

The financial case for modernizing RCM starts with a simple problem: providers are facing more ways to lose revenue.

The pressure is not isolated to one part of the revenue cycle. 74% of surveyed leaders expect a negative impact on net patient service revenue, while 76% expect denial rates to increase and 56% expect days in accounts receivable to rise. Meanwhile, 56% expect an increase in self-pay bad debt and uncompensated care, and 73% expect more patients to experience higher out-of-pocket costs.

In other words, providers are being squeezed both before and after the claim is submitted. They are expected to collect more efficiently while navigating increasingly complex reimbursement dynamics and a patient population increasingly exposed to direct healthcare costs.

That makes RCM less about simply collecting revenue — and more about protecting the revenue providers have already earned.

The AI Opportunity Is Clear. The Infrastructure Is Not.

AI is increasingly being positioned as the solution to RCM’s structural inefficiencies. But adoption is not happening at the speed implied by the hype.

The bottleneck is not a lack of potential use cases. 51% of respondents cite data security and patient privacy concerns, while 45% point to a lack of integration with existing legacy systems. Other barriers include insufficient proof of ROI, regulatory uncertainty and implementation costs.

This is an important distinction. Healthcare does not need more AI demos. It needs AI that can operate inside the systems that already run healthcare.

In RCM, workflows are fragmented across EHRs, billing systems, payer portals, coding infrastructure and third-party vendors. A model that performs well in isolation is not necessarily useful if it cannot access the data, systems and workflows required to resolve a claim.

The investment opportunity is therefore shifting from standalone AI features toward integrated platforms that can connect fragmented revenue-cycle infrastructure and automate workflows across it.

The RCM Stack Is Fragmenting — and Consolidation Is Coming

The problem with RCM is not simply that too many tasks are manual. It is that the workflow itself is distributed across too many systems and vendors.

A typical revenue cycle can involve EHRs, practice management software, clearinghouses, coding platforms, claims management, denial management, patient payment systems, payer portals and outsourced billing companies.

Each layer may solve a specific problem. Together, however, they can create a more fragmented system. The result is a paradox: the more technology providers add to the revenue cycle, the harder it can become to manage the revenue cycle as a single system.

This creates an opportunity for platforms that can become the orchestration layer across the stack. The most valuable RCM companies may not be the ones that automate a single task. They may be the ones that connect the workflow from patient registration to payment — and use AI to determine what happens next.

That is also where the investment case becomes more compelling. RCM combines several characteristics investors typically value: mission-critical workflows, recurring revenue, high switching costs and measurable ROI. AI adds a potential margin-expansion layer on top.

The question is no longer whether providers will use AI in RCM. They almost certainly will. The question is which companies will own the infrastructure through which that AI operates.

The New RCM KPI: Cost to Collect

The financial opportunity is ultimately straightforward. Healthcare providers do not need to create an entirely new revenue stream to benefit from better RCM. They need to capture more of the revenue already generated by the care they provide.

At scale, even small improvements in the revenue cycle can have an outsized impact. Reducing denials, accelerating collections or lowering the labor required to process claims can directly improve margins. This makes RCM particularly attractive for AI adoption: unlike many AI use cases where the return on investment can be difficult to quantify, the financial impact of RCM automation can be measured in dollars.

That creates a clear economic hierarchy. The most attractive use cases are likely to be those that combine high transaction volume, repetitive workflows and a direct link to financial outcomes. Denial management, coding, accounts receivable follow-up and authorization are therefore natural starting points for automation.

The key metric may increasingly shift from how many tasks AI can perform to how much it costs a provider to collect each dollar of revenue.

From Automation to Autonomy

This is where agentic AI enters the picture.

Traditional automation performs predefined tasks. Generative AI can assist employees with specific activities. Agentic AI aims to go further: it can make decisions, execute multi-step processes and coordinate workflows with limited human intervention.

In RCM, that could mean moving from an employee manually reviewing a denial to an AI system that identifies the root cause, retrieves the relevant documentation, determines the appropriate action, submits an appeal and escalates only cases that require human judgment.

Revenue cycle leaders are increasingly planning to implement AI and automation across more RCM use cases
 Source: McKinsey, Agentic AI and the Race to a Touchless Revenue Cycle.

The direction of travel is clear: the industry is moving beyond individual point solutions and toward a more integrated, AI-enabled revenue cycle. McKinsey reports that in 2025, more than 30% of providers prioritized AI and automation implementation across seven specific RCM use cases, compared with four to five use cases in 2023 and 2024.

The economics explain the interest. RCM typically costs an at-scale health system 3% to 4% of revenue, while nearly 20% of claims are denied on average. McKinsey estimates that AI-enabled RCM could reduce cost to collect by 30% to 60%.

For a health system generating $6B in patient revenue, reducing cost to collect by one to two percentage points could represent $60M to $120M in annual savings.

That is why the most attractive opportunity may not be replacing the entire revenue cycle overnight. It may be progressively automating the highest-volume, most repetitive and most financially consequential workflows.

Bottom Line

The RCM market is entering a period of structural change. Providers are facing greater reimbursement pressure, more complex payer interactions and rising administrative costs. At the same time, AI is becoming capable of automating increasingly complex workflows.

But the winners will not necessarily be the companies with the most impressive AI demos. The strongest platforms will likely be those that can combine deep workflow integration, proprietary data, measurable ROI and the ability to operate across fragmented healthcare infrastructure.

The long-term prize is a touchless revenue cycle. The near-term opportunity, however, is more pragmatic: automate the workflows where human labor is expensive, errors are costly and the financial impact can be measured.

Healthcare’s AI opportunity may be clinical. Its first truly scalable financial opportunity could be administrative.

Sources

• Inside RCM: Payer Pressure, Visibility Gaps, and the Reality of 2026 — Adonis
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• Revenue Cycle Management Market to Reach US$291.19B by 2033 — DataM Intelligence
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• Healthcare Revenue Cycle Management at a Strategic Turning Point: Survey Insights — McKinsey & Company
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• Where Artificial Intelligence Actually Helps in Medical Billing and Revenue Cycle Management — and Where It Doesn’t — Medical Economics
  Read the full article

• Healthcare IT Investment: AI Moves From Pilot to Production — Bain & Company
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• AI and Revenue Cycle Management: Strategy and Budget — Modern Healthcare
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• Agentic AI and the Race to a Touchless Revenue Cycle — McKinsey & Company
  Read the full analysis

• What Differentiates Winning Healthcare IT Investments? — Bain & Company
  Read the full analysis

• Global Healthcare Private Equity Report 2026 — Bain & Company
  Read the full report