How PE-Backed Healthcare Companies Can Turn AI Into Revenue Cycle Value

Private equity is now a material part of U.S. healthcare delivery, with firms reportedly owning nearly a quarter (22.5%) of for-profit hospitals. As PE invests more in the sector, sponsors have considerable influence over how healthcare companies operate and invest in technology.

AI-enabled healthcare is appealing to sponsors for its value-creation potential. A company with structured data and repeatable workflows has more room to expand margins through automation as well as scale across locations or portfolio companies.

To attract PE investment, healthcare providers are increasingly evaluating how AI can improve two critical determinants of overall revenue performance:

• Cost to collect: What the provider spends to collect each dollar it is owed
• Net collections: How much of that owed it actually brings in

Revenue cycle management (RCM) creates a direct line between AI capability and these outcomes. It offers a clear view of whether and where AI is creating measurable financial value and ultimately whether sponsors wish to pursue an investment.

PE sponsors are backing assets across providers, services firms, and technology vendors alike. But adoption alone doesn’t answer the questions sponsors need to resolve. They’re asking whether savings last beyond the pilot, whether they scale across portfolio companies with different systems and workforces, and whether they improve EBITDA margins and cash flow over time, rather than just headcount metrics.

Administrative labor expense is the largest controllable cost in the revenue cycle, which makes it central to the value-creation case. Small gains in claims processed per staff hour, coding accuracy, denial management, and collection speed can produce measurable improvements in margins and cash flow. Those are the results sponsors need to see.

Demonstrating Revenue Cycle Value

To demonstrate revenue cycle value, healthcare organizations must treat AI as an enabler of workflow improvement rather than layering it onto existing processes. That starts with two basic questions: What exactly is AI changing, and what’s the end goal? PE firms want to assess how healthcare companies are leveraging AI to drive measurable, lasting value creation.

While a tool may be easy to demo, it’s more difficult to defend three years into an investment if it hasn’t changed the economics of the business. Redesigned workflows, however, can turn early pilot savings into recurring improvements on the P&L. This requires changes in two areas: The work itself and how leaders manage it.

Healthcare companies should consider the following AI use cases to move work upstream and help improve financial outcomes.

Denials Management

Denials management is one of the fastest levers for improving net collections and cost to collect, two metrics that PE investors scrutinize closely when evaluating the durability of a healthcare asset's revenue engine.

An LLM can help draft an appeal letter from clinical documentation, faster than writing it from scratch, and identify which denials are worth appealing. It can also draft the letter and route to a human for review before it goes to the payer, enabling healthcare providers and firms to complete the work with fewer people—and the ones who are involved are being more judiciously deployed and leveraging their expertise.

Prior Authorization

Because prior authorization delays, which historically has been a manual, fax-and-portal-driven bottleneck, directly stall both care and cash, implementing agentic AI is a tangible way to compress time in A/R and signal to PE sponsors that a platform can scale revenue without scaling headcount.

Agentic AI can now pull payer requirements, assemble clinical documentation, and flag missing information before a request is even submitted, catching what would have become a denial weeks earlier. West Monroe's work helping payers and providers prepare claims operations for prior authorization reform reflects how much of this workflow is shifting from reactive processing to upstream readiness.

One healthcare organization embedded AI-enabled capabilities—including intelligent IVR, dynamic call routing, self-service, and agent assist—directly into its existing member and provider service workflows. Rather than creating another standalone AI tool for employees to learn, the organization redesigned the workflow itself. The result: reduced call volume, faster resolution times, and more consistent experiences across member and provider interactions.

Read the full client result

Coding

Healthcare companies should also consider AI-enabled coding, which can help coders work more quickly and accurately, thereby speeding up cash flow.

Rather than reviewing every case, these tools enable staff to focus on exceptions, which improves clean claim rates and accelerates cash. As coding errors are one of the most common root causes of denials, leveraging AI to identify potential errors also prevents rework further downstream in the revenue cycle. That means fewer resources tied up to chasing corrections and more of the organization's coding staff focused on complex, high-value cases where expertise matters most.

Demonstrating that level of operational efficiency is precisely what investors want to see when they underwrite a healthcare asset's growth potential.

Eligibility Verification

Using AI to prevent denials before they occur is one of the highest-leverage ways to lower cost to collect and signal operational maturity to PE buyers.

Traditionally, a downstream check that surfaces problems after a claim is already in motion. Moved upstream and automated at the point of scheduling or registration, it prevents entire categories of denials from ever entering the cycle in the first place.

For a PE-backed organization, this is a low-cost, high-visibility fix. It requires no new clinical judgment or complex change management, yet allows healthcare companies to demonstrate a quick, impactful win.

Cash Posting and Collections

Enabling real-time, always-on monitoring of the cash postings and collections can help identify missed revenue sooner and shorten the cash cycle.

Agentic AI, specifically, can coordinate multiple steps in a workflow, pulling information from multiple sources to determine what should happen next. Rather than replacing the existing processes, AI-enabled cash posting and collections builds onto them, and hands work to a person when judgment is needed. While these systems ask more of the technology and workforce upfront, they can help organizations generate more predictable cash conversions.

The Real Value of AI in RCM

Across all five use cases, the pattern is consistent: AI creates financial value when it helps an organization catch problems earlier. That’s always been the goal of effective RCM: Address issues before they become denials, write-offs, or delayed collections. AI makes that possible at a scale that was previously difficult to achieve.