AI Adoption Priorities for RCM Leaders in 2025

Payers are already using artificial intelligence to deny, downgrade, and audit claims in seconds, and most provider organizations are still answering with manual workflows built for a slower fight. That asymmetry is why the order in which a practice adopts AI now matters as much as whether it adopts AI at all, and for independent and community-based providers, getting the sequence wrong means falling permanently behind systems that got it right first.

Payer AI adoption at scale and the strategic sequencing of provider AI investment

Most physicians already have some access to AI tools, whether through their billing software, their clearinghouse, or a vendor pitch sitting in an inbox. The gap that matters now is speed and intent on the other side of the claim, not access. Aspirion's 2025 review of payer behavior describes systems that review and deny claims in seconds, operating at a volume no manual provider workflow can match, regardless of how experienced or well-staffed the billing office happens to be. Aspirion also documents a strategic shift among payers during 2025: investment is moving away from front-end utilization controls and into back-end payment integrity operations built on large language models, so the denial fight increasingly happens after the claim is already in the system, at a stage where providers have historically had the least automated defense.

The consequence lands on operating margins first and on patients second. Staff hours stretch thinner chasing denials that arrive faster than appeals can be written, and the financial burden that providers can't absorb often shifts downstream to patients in the form of balance bills and delayed care. The State of AI in Healthcare RCM 2026 report states the asymmetry without much ambiguity: payers use AI to deny and delay, many providers still use manual processes to submit and appeal, and rising denial rates follow from that mismatch as a predictable structural outcome rather than a surprising one. Framing this as a general technology trend misses the point. It is a competitive asymmetry, and it requires provider decision-makers to treat AI deployment as a question of where the enemy is already dug in, not as a generic efficiency upgrade to roll out whenever budget allows.

How broad the adoption-to-results gap still is

Belief in AI's potential to fix the claims process has outrun its actual deployment for that specific purpose. Oliver Wyman's 2026 Healthcare RCM Survey polled more than 200 decision-makers and 90 end users at provider organizations ranging from rural independent hospitals to academic medical centers, and found that only a minority, somewhere between one-fifth and two-fifths, report broad or enterprise-wide use of AI-enabled tools across the revenue cycle. A majority of organizations remain in earlier stages of adoption, even as a substantial share of healthcare organizations report having integrated some form of AI-powered automation into revenue cycle workflows and most health systems say they are actively exploring, piloting, or implementing generative AI tools somewhere in the process.

Those two findings sit next to each other uncomfortably. Wide exploration and piloting coexist with narrow enterprise-wide deployment: organizations are spending on AI broadly, across many workflow points at once, instead of concentrating that spending on the specific choke points where payer-side AI is doing the most damage. Smaller and community-based providers look especially exposed here, because they typically lack the capital and staff bandwidth to pilot AI everywhere and wait for results to compound. Larger systems that have already scaled their deployments capture benefits that compound further with each billing cycle, and smaller organizations are unlikely to close that lead later just by spending more.

A research brief from the University of Colorado Denver, authored by Simeon Petrov and Jiban Khuntia, adds a governance dimension to this picture: AI adoption in revenue cycle management requires alignment and transparency among patients, providers, and payers, not just better software, and without that alignment even sophisticated tools risk deepening the mistrust and inefficiency they were meant to fix. Money spent on AI without a clear view of where the return is highest is money that solves the wrong problem first. Three workflow points deserve that first dollar ahead of the rest: prior authorization, denial prediction before submission, and real-time eligibility verification.

Prior authorization as the highest-return first target for AI deployment

Prior authorization is where payer-side AI concentrates the most damage, and it's also the point where provider-side AI can stop revenue loss before it happens rather than fight to recover it afterward. The administrative weight alone justifies first-priority treatment: a leading physician trade association estimates the average physician handles approximately 40 prior authorization requests per week, consuming roughly 13 hours of physician and staff time in that same span. The resulting cost approximately $34,000 per provider, per year according to the Centers for Medicare & Medicaid Services, lands directly on practice margins regardless of how clean the underlying clinical case is.

What happens to those requests once submitted shows how much of that burden is manufactured rather than clinical. A KFF analysis of 2025 data found that insurers denied a meaningful share of standard prior authorization requests across Medicare Advantage, Medicaid managed care, and ACA Marketplace plans, with Medicaid managed care and Marketplace plans running higher than Medicare Advantage. Denial rates varied substantially across Medicare Advantage insurers, and in the ACA Marketplace, Centene's denial rate reached 25%. Prior authorization denial behavior is payer-specific, trackable, and learnable, which is precisely the kind of pattern an AI system paired with institutional billing knowledge can turn into a strategic advantage rather than a recurring cost.

The appeal data makes the strongest case that many of these initial denials never had clinical merit to begin with. A substantial majority of initially denied Medicare Advantage prior authorization requests were overturned on appeal, and for skilled nursing facility denials that were appealed, nearly all were overturned. When an initial denial gets reversed almost every time it's challenged, it wasn't a clinical judgment call. It was an administrative obstacle, one that AI deployed at the point of submission, rather than after the fact, can prevent. Provider-side AI deployed at the prior authorization gateway shifts the posture from reactive recovery to proactive defense, which is the strategic priority Altair Health emphasizes for independent practices juggling PA requests across multiple payers at once, each with its own documentation thresholds and approval logic.

Regulation is starting to build the infrastructure this kind of prevention runs on. The CMS Interoperability and Prior Authorization Final Rule, CMS-0057-F, finalized in January 2024, requires FHIR-based APIs for prior authorization from impacted payers, with operational provisions taking effect January 1, 2026, and full API implementation deadlines beginning January 1, 2027. Oliver Wyman's 2026 survey lists electronic prior authorization among the small set of "no-regret" AI investments that organizations of every size and type are making, and the reasoning holds up against the numbers above: a prevented denial costs a fraction of what a worked appeal costs, and the near-universal skilled nursing facility overturn rate confirms that most of those initial denials were preventable rather than clinically earned.

Denial prediction before submission: the second deployment priority

Once prior authorization is covered, the next highest-value intervention sits earlier in the process than most practices currently look: before the claim is submitted at all, rather than after it comes back rejected. Petrov and Khuntia's research brief cites an average rework cost for every denied claim, and notes that roughly two-thirds of denied claims are never resubmitted, so most denied revenue is written off entirely rather than recovered through appeal. Claims that do get worked lose value the longer they sit: once an unpaid claim ages past a certain point, the probability of ever collecting on it drops sharply, which turns response speed into a financial variable rather than just an operational preference.

When denial prediction aims at the pre-submission stage, it changes what's being optimized. Instead of asking "how fast can this denial be appealed," the question becomes "what in this claim will trigger a denial before it's sent." Answering that question well depends on payer-specific pattern recognition rather than generic risk scoring: knowing how a particular payer's AI system behaves, which documentation elements trigger rejection, and which claim attributes correlate with denial for that plan specifically. That knowledge accumulates with every claim cycle and compounds in value the longer a practice tracks it, which is exactly the kind of intelligence that turns AI pattern recognition into denial prevention rather than a generic flag. Denial prediction works best when paired with payer-specific institutional knowledge, knowing that one payer applies different criteria than another, or that a particular Medicare Advantage plan has tightened its medical necessity logic in the last quarter, and that pairing is what separates a prevention system from a dashboard of risk scores nobody acts on.

The specialty-level view makes the case for precision even clearer. Average denial rates vary widely by specialty, with behavioral health, cardiology, and orthopedics all running well above best-in-class benchmarks. A practice's group-level denial rate can look perfectly acceptable while one specialty line underneath it is bleeding revenue. Denial prediction has to operate at the specialty and payer level to actually be useful, not just at the organization level where problems get averaged out of view. Aspirion's 2025 review notes that clinical denials accelerated through the year, with payers continuing to shift toward AI to deny and downgrade claims at a volume and complexity that manual business office staff can no longer keep up with on their own.

Real-time eligibility verification as the third priority and foundation for the other two

Prior authorization prevention and denial prediction both depend on accurate eligibility data, and when that data is wrong or stale, both of those investments inherit the error. Eligibility mistakes at the front end of the revenue cycle are a primary driver of downstream denials. Real-time verification is the foundation the rest of the stack sits on rather than a third-tier nice-to-have.

Robotic process automation vendors like Auxis identify eligibility verification as one of the clearest areas where automation is replacing manual, time-draining tasks, and the reason is simple: a claim built on incorrect eligibility information gets denied no matter how clean the clinical documentation is or how accurate the coding turns out to be. Batch verification, the kind run the night before or the morning of a visit, misses coverage changes, lapsed authorizations, and plan switches that happen in between. Real-time systems check eligibility at scheduling, again at check-in, and once more before the claim goes out, catching the changes that batch processes are structurally built to miss.

The integration dependency is where this section connects back to the two priorities already discussed. Eligibility data feeds directly into both prior authorization workflows and denial prediction models, and a lagged or disconnected eligibility system feeds those models false inputs, degrading the accuracy of everything built on top of it. Physicians working in disconnected environments already spend meaningful time each day reconciling information across their EHR, billing system, and administrative tools by hand, and the CMS FHIR API mandates taking effect in 2026 and 2027 create the technical pathway for real-time data exchange to replace that manual reconciliation. That pathway only helps organizations that have already invested in the integration needed to use it, which is another reason eligibility verification belongs early in the sequence rather than late.

What the sequencing looks like in practice

Put in order, the sequence runs eligibility verification first, denial prediction second, and prior authorization third, each one laying the data foundation the next depends on. That order delivers its full value only when in-house billing expertise handles the exceptions AI surfaces, because no model can learn payer-specific behavior from data alone, no matter how large the dataset gets.

Aspirion's Chief AI Officer has made the case that deep subject matter expertise paired with AI-enabled platforms outperforms either humans or AI operating alone, and the firm's own data on AI-assisted clinical appeal letters backs that up: those letters showed a meaningfully higher success rate than appeals produced without AI involved. That finding lines up with Oliver Wyman's 2026 survey, where 92% of respondents agreed that no-regret AI investments exist for revenue cycle work, reasoning that such investments succeed because they integrate into existing workflows rather than demanding a full operational overhaul. Quality improves when expertise and machine capability are paired, and the sequence breaks down wherever one is deployed without the other.

The institutional memory argument ties the whole sequence together. Knowing which documentation triggers a denial from one plan but not another, or which appeal language works with a specific insurer, is a compounding asset that most practices lose whenever experienced billing staff leave, taking years of payer-specific knowledge with them. AI systems can encode that knowledge and preserve it at scale, but only where expert billers are still in the loop feeding the system what they know and correcting what it gets wrong. Altair Health, an AI-powered medical billing service built specifically for independent practices, addresses this asymmetry by letting AI handle the routine pattern-matching work, confirming eligibility, flagging a likely denial, formatting a prior authorization request, while in-house expert billers focus on the payer-specific exceptions and denial fights that require the kind of institutional memory a model can't invent on its own. That division of labor, not a wholesale replacement of either side, is what lets independent practices match the speed of payer-side AI without losing the judgment that payer behavior still requires.