Key Takeaways from HFMA’s Revenue Cycle of the Future Report

Revenue cycle management has moved well beyond its traditional role as an operational back-office function. Across the country, health systems of every size are rethinking how claims get processed, how staff are deployed, and what role AI will play in getting hospitals paid for the care they deliver.

A new report from HFMA — The Revenue Cycle of the Future: AI Boom and Workflow Redesigns Accelerate Rev Cycle Transformation — captures where the industry stands and where it’s headed.

Revecore is proud to have supported this research. Here are the findings we think revenue cycle leaders need to be paying close attention to right now.

A market in motion

The U.S. revenue cycle management market is currently valued at roughly $90.6 billion and is projected to reach nearly $308 billion by 2030, according to Grandview Research data cited in the HFMA report. AI is a significant driver of that growth, with McKinsey & Co. estimating that AI-enabled workflows could reduce cost to collect by 30–60% while accelerating cash realization across the revenue cycle.

Yet preparedness is lagging. A February 2026 HFMA survey of 95 healthcare finance and revenue cycle leaders found that only about 7% describe their teams as “very prepared” for the revenue cycle of the future. Another 44% say “somewhat prepared,” leaving roughly half somewhere between uncertain and underprepared.

Where AI is taking hold across the revenue cycle

The HFMA survey found that 27% of respondents are actively deploying AI at scale across multiple revenue cycle functions, while 53% are still conducting pilots in select areas. Distribution across the revenue cycle varies considerably, with adoption concentrating in three areas:

• Back-end functions — including A/R follow-up, denials management, underpayment recovery, and cash posting — have been the most common entry points. These tasks are labor-intensive and rules-governed, making them natural candidates for automation.
• Mid-cycle work such as autonomous coding and ambient documentation is attracting significant investment, with AI now capable of assigning codes and preparing claims within hours rather than days.
• Front-end operations are seeing early AI applications in self-healing patient data, predictive risk flagging during scheduling, and conversational tools for patient financial engagement.

Mayo Clinic’s approach to prior authorization illustrates how far the ambition extends: the academic medical center is exploring a real-time tracking system akin to a pizza delivery app, giving clinicians, staff, and patients transparent visibility into exactly where an authorization stands and what comes next. As Nikki Harper, Mayo Clinic’s chair of revenue cycle analytics, automation, and diversified revenue, put it: “We know we have to decrease cost to collect, and technology is a major play for how we can do that.”

The workforce question goes deeper than job displacement

Revenue cycle teams are already lean. Margins for most hospitals have stabilized in a modest range of 1.3–2%, but labor expenses and Medicaid uncertainty continue to compress those gains. As automation absorbs routine work, skill transformation has become as pressing a concern as headcount.

Leaders at Northwell Health, Novant Health, and USA Health all raised similar concerns about “de-skilling”: as routine, rules-based tasks are automated, hospitals risk losing the experienced bench strength still needed to identify exceptions, navigate complex cases, and course-correct when systems fail. Brogan of Northwell noted the coding pipeline problem directly. Recruiting into coding careers is increasingly difficult when autonomous coding tools are accelerating so visibly in the marketplace.

Harper’s preferred framing is “elevate, not eliminate.” Staff who once exclusively handled claim status inquiries may shift toward payer trend analysis or upstream denial prevention. That redeployment requires deliberate investment in training, which is frequently the first budget cut in a constrained environment. Candice Powers, chief revenue officer at USA Health, was direct about the stakes: “How providers survive in the short run here, the next one to two years, will determine where we’re at five years from now.”

Three principles for navigating this well

The HFMA report distills lessons from health systems managing AI adoption thoughtfully. A few stand out:

Start with the problem, not the technology. Harper’s guidance resonates across the industry: putting automation on top of a broken process doesn’t fix the process; it scales the dysfunction. Define the operational problem first, then evaluate technology solutions against it.

Rationalize your vendor relationships. Complexity is already a problem. Among HFMA survey respondents, 37% describe their vendor relationships as “functional but increasingly complex,” and nearly one in five call them “fragmented and difficult to manage.” The report anticipates consolidation, with health systems moving toward fewer, broader vendor relationships that offer economies of scale and tighter data governance. As Kodiak Solutions’ Colleen Hall observed, vendor selection has become as much a risk decision as a capability one.

There is a cost of inaction. When it comes to timing, don’t wait. The cost of delayed adoption continues to increase, especially as payers are already deploying AI at scale against providers. To put this in perspective, denial rates reached 11.8% in 2024, and net revenue leakage from denials grew 25% year-over-year in 2025. McKinsey’s Sanjiv Baxi put it directly: waiting for the market to “settle” carries a price that quietly compounds.

A note on the hardest end of revenue cycle

The HFMA report focuses largely on AI’s potential to transform high-volume, transactional workflows. Complex claims, underpayments, medical necessity denials, and non-standard payer disputes are harder to automate. Resolving them requires deep clinical, legal, and payer-specific expertise working in concert with intelligent systems, including accountability that extends through final resolution. That’s precisely where Revecore operates.

Serving more than 1,300 hospitals and health systems, Revecore combines our AI-powered platform with specialized clinical, legal and reimbursement expertise to recovery revenue and reduce risk on the cases that tend to fall through the cracks of standard automation.

To read the full HFMA report, visit hfma.org.