The revenue cycle has always been a battleground between payers and providers, but AI is raising the stakes in ways that hospital finance leaders say are becoming increasingly difficult to absorb.
Payers are automating denials faster than ever.
Providers are scrambling to catch up.
And somewhere in the middle, patients are feeling the friction.
Across the country, health system CFOs and revenue cycle leaders are grappling with the same fundamental tension: AI is simultaneously the problem and the most promising solution available. How hospitals navigate that paradox — and how quickly they can do it — may define the financial health of their organizations for years to come.
‘We’re in catch-up mode’
A growing divide in AI investment between payers and providers is a key concern for hospital finance leaders, with payers leveraging their scale to move ahead early.
“It’s become an arms race, and payers are definitely ahead,” Aron Klein, vice president of finance operations and supply chain at Urbana, Ill.-based Carle Health, said during a recent episode of Becker’s “CFO + Revenue Cycle Podcast.” “As larger organizations, they have deeper pockets and the ability to invest in AI and technology — which, in some cases, has added burdens for health systems. Right now, we’re in catch-up mode as an industry. But I don’t think we have a choice.”
The numbers at some systems illustrate just how acute the pressure has become. Dennis Laraway, executive vice president and CFO of Cleveland Clinic, told Becker’s the system still sees more than 15% of claims initially denied — only to bring that figure down to below 2% after a lengthy, resource-intensive appeals process.
“But all of the time, energy and resources consumed to get it from 15% to sub-2% is unsustainable,” Mr. Laraway said. “That’s arguably also unnecessary and unsustainable for the payers and the plans. If they’re watching the same report card that we are — an initial 15% ends up being sub-2% — that doesn’t seem to be productive friction in healthcare for either side of the table.”
For Sunitha Reddy, chief revenue officer and vice president of operations at Ontario, Calif.-based Prime Healthcare, the dynamic is playing out in real time.
“Both payers and providers are investing heavily in AI, but with very different objectives,” Ms. Reddy said during an upcoming episode of the Becker’s “CFO + Revenue Cycle Podcast.” “Payers are using AI to scale claims; they’re automating denials and increasing payment scrutiny at much higher speed and volume. Providers, on the other hand, are using AI to improve accuracy and efficiency, particularly in areas like documentation, coding and denial prevention, to ensure appropriate reimbursement.”
The result is a system under mounting strain. More denials generated at machine speed, more appeals required to recover legitimate reimbursement and more administrative burden layered onto provider teams that are already stretched thin.
At Carle Health, an eight-hospital system, workforce constraints compound the technological gap.
“We have to leverage technology wherever possible. Adding people isn’t always realistic: We consistently face a limited candidate pool, especially when hiring for revenue cycle roles,” Mr. Klein said. “We’re always competing for coders, follow-up reps and others who are willing to do tough, often thankless work. So we need to invest in tech that supports and strengthens the teams we do have.
“At the same time, there’s a real opportunity to improve provider-payer relationships, especially by sharing data and discussing the burdens these administrative processes create. Partnering more effectively with payers could help reduce denials and overall friction.”
From reactive to predictive: What the next phase looks like
If the current moment is defined by providers playing defense, the next phase is about getting ahead of the problem entirely. Revenue cycle leaders describe a meaningful shift underway: from a reactive, transactional function toward one that is predictive, analytic and deeply integrated with clinical operations.
“Instead of reacting to issues after the fact, we can try to identify documentation gaps up front, predict denial risk before submission and resolve authorization issues earlier,” Ms. Reddy said.
“We’re seeing a potential shift toward a more predictive, and eventually more autonomous, revenue cycle,” Ms. Reddy said. “Instead of reacting to issues after the fact, we can try to identify documentation gaps up front, predict denial risk before submission and resolve authorization issues earlier.
“That said, there’s still a long way to go for providers to seamlessly adopt these tools. AI in healthcare is not plug-and-play — it requires thoughtful integration, strong operational teams and ongoing governance, especially with the introduction of agentic AI.”
At Prime, that evolution is happening in phases. The first is expanding robotic process automation to take on manual, transactional work. The second is more ambitious: embedding AI more broadly across the revenue cycle continuum in ways that surface insight, not just automate tasks.
For academic health systems, the build-versus-buy question adds another layer of complexity. Craig Collins, senior vice president and CFO of Lexington, Ky.-based UK HealthCare, said his organization is trying to leverage its university infrastructure while staying disciplined about where to invest, and where to wait.
“We’re watching in some ways, and also being very proactive,” Mr. Collins told Becker’s. “We’ve actually stood up within the university an AI solution. We’ve invested significant resources and are trying to leverage our position as an academic enterprise by pulling on our colleges and other internal capabilities.”
That university-wide perspective also shapes how UK HealthCare approaches its EHR vendor relationship, particularly when it comes to avoiding redundant investment. Mr. Collins said he stays in close contact with Epic’s product roadmap specifically to avoid building capabilities the vendor may be already developing.
“My best friend at Epic cringes anytime I call him, because I’m wanting to know their roadmap, so that we don’t go and develop something or buy a bolt-on, only for them to have it a year and a half later,” he said. “Otherwise, we’re just throwing good money at bad investments.”
Instead, UK HealthCare is working with Epic to co-develop solutions — participating in what Mr. Collins described as an emerging “AI sandbox” within the platform — while reserving independent development for capabilities specific to academic health system needs.
“We want to take advantage of their resources, but we also want to be able to focus on some of the things that would be specific for our needs as an academic enterprise. We could even partner with other academics to try to build our strengths and develop them together,” he said.
Pat Leonard, CEO of CorroHealth, a revenue cycle services company, said the speed of such buildouts have accelerated dramatically.
“The AI arms race isn’t just about models getting smarter. It’s about automation becoming more accessible and far more pervasive,” Mr. Leonard said. “With what’s commercially available today through Azure, AWS, and other platforms, we can now build in three to six months what used to take two to three years, and that naturally intensifies the level of activity in the market.”
But he was quick to caution that speed of development does not equal readiness to deploy.
“Just because you can automate, and you know how to automate, doesn’t mean you’re ready for broad-scale automation,” he said. “The real constraints for many organizations are around integration and complexity — things like [fast healthcare interoperability resources]-based access to the full clinical record, and the layers of nuance in payer rules and provider contracts.”
Mr. Leonard’s advice for systems trying to get ahead: stop layering AI on top of legacy complexity and start designing operating models around automation from the ground up.
“Leaders who focus on simplification and design their operating models around automation, instead of layering AI on top of legacy complexity, will be the ones who pull ahead,” he said.
A new kind of revenue cycle leader
The technological transformation underway is reshaping not just workflows, but the skills and profile of the people needed to lead revenue cycle functions. The next generation of revenue cycle leaders will look very different from the last.
“We’re already seeing a meaningful shift in how revenue cycle teams operate,” Ms. Reddy said. “Historically, the work has been very transactional, focused on billing and collections. But as automation and [robotic process automation] take on more of that work, the function is becoming much more analytic and strategic. Teams are now focused on understanding what’s really driving performance — whether it’s denials, payer behavior or documentation — and addressing those issues more proactively.”
At Prime, that shift is visible in three areas: a stronger focus on data-driven decision-making, deeper alignment with clinical teams given the direct link between documentation and revenue cycle performance, and growing emphasis on technology leadership, specifically the ability to evaluate and implement AI tools in ways that actually improve outcomes.
“I believe that shift in the use of AI is driving a different skill set for leaders,” Ms. Reddy said. “You need data literacy, cross-functional leadership and adaptability, because the environment is changing so quickly.”
Mr. Leonard offered a similar view from the vendor side, emphasizing that the premium going forward will be on people who can identify automation opportunities, adapt workflows and understand where technology creates genuine leverage, not just those who can execute the existing process efficiently.
“I see revenue cycle teams needing stronger skills in process re-engineering and a baseline level of technical understanding around what automation actually can and can’t do,” he said. “Leaders should be able to identify good opportunities, understand how to adapt workflows, and know where automation will really drive measurable impact.
“We’re going to place a premium on flexibility and a willingness to be retrained or upskilled; having the desire and want for more responsibilities. AI will take on more of the routine work, but the complex, specialized problems will still rely heavily on people. Organizations shouldn’t fear AI, but instead utilize it as a way to surface new opportunities, understand where automation can add the most leverage, and thoughtfully reshape the workforce.”
Ms. Reddy framed the stakes in terms that go beyond operational efficiency.
“Ultimately, this isn’t just about efficiency — it’s about access to care and trust in the system,” she said. “AI has the potential to reshape the revenue cycle, but it has to be implemented thoughtfully to truly deliver on that promise.”
At Carle Health, Mr. Klein is already looking past the current moment toward what the pressure may ultimately produce: new care and business models that don’t yet exist. He anticipates that employers, payers and health systems will respond to growing complexity with novel hybrid arrangements, including proposals already appearing at his system in which payers have offered to waive prior authorization and patient cost-sharing in exchange for lower contracted rates.
“Depending on the proposed rates, that can be appealing,” he said. “Reducing administrative burden and eliminating patient financial barriers could improve the overall experience, and reduce the need for collections or chasing down payments.”
Whether those experiments gain traction broadly remains to be seen. What is clear is that the revenue cycle — long one of the most labor-intensive and friction-laden functions in healthcare — is in the early stages of a transformation whose full implications are still coming into view.
To be featured on the Becker’s “CFO and Revenue Cycle Podcast,” please contact me at acondon@beckershealthcare.com.
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