Agentic AI has moved from theoretical promise to operational reality. This whitepaper examines why the back end of the revenue cycle is the highest-leverage entry point — and how health systems can capture 30–60% cost reductions before competitors reach scale.
Healthcare providers have spent decades and billions of dollars chasing a deceptively simple vision: a revenue cycle that manages itself. What they have built instead is a patchwork of loosely connected point solutions, globally distributed billing staff, and legacy IT systems that create friction at every handoff. The result has been incremental efficiency gains in isolated areas — nowhere near the systemic transformation the administrative burden demands.
Revenue cycle management (RCM) covers nearly every patient interaction outside the clinical episode: scheduling, registration, insurance verification, prior authorization, coding, claims submission, denials management, underpayment recovery, and collections. At scale, it costs the average health system 3–4% of total patient revenue[5] — a figure that translates to tens of millions of dollars annually even for mid-market systems. Collectively, US health systems spend more than $140 billion on RCM each year,[2] with manual processes, fragmented vendor landscapes, and outdated technologies the primary contributors to that cost.
The Revenue Leak
Nearly 20% of claims are denied on average.[3] Of those, roughly 60% are never appealed,[4] meaning a substantial share of legitimate earned revenue is written off entirely — not because the claim was invalid, but because staffing constraints prevented follow-through. For a health system with $500M in annual patient revenue, that translates to an estimated $10–15M in avoidable write-offs every year.The compounding pressures of 2024–2026 have made inaction increasingly untenable. Reimbursement rates have lagged inflation, labor costs for skilled billing staff have surged, and payer complexity has grown as insurers deploy their own AI-powered denial engines. By 2025, more than 30% of providers had prioritized AI and automation implementation across seven specific RCM use cases — up from just four to five use cases in 2023 and 2024.[1] Health systems that delay modernization are not holding steady — they are falling behind structurally.
Previous automation investments in RCM — robotic process automation (RPA), predictive analytics, machine learning-assisted coding — delivered real but bounded value. They accelerated specific manual tasks but remained fundamentally passive: they flagged issues for humans to resolve, generated recommendations that still required human execution, and broke down when encountering edge cases their rules weren’t built to handle.
Agentic AI represents a qualitative departure from this paradigm. Where generative AI advises, agentic AI acts. It autonomously executes complex, multi-step workflows across systems, makes intermediate decisions within defined guardrails, handles exceptions dynamically, and escalates to human operators only when genuinely required. In practice, it functions more like a highly capable coworker than a decision-support tool.[1]
For revenue cycle specifically, this distinction is profound. McKinsey’s analysis projects that AI enablement of the revenue cycle could yield a 30 to 60 percent reduction in cost to collect, faster cash realization, and a workforce refocused on patient value rather than administrative tasks.[1] Back-end RCM work — denials management, accounts receivable (AR) follow-up, underpayment identification, cash posting — is governed by dense but learnable rules. It involves high-volume, pattern-driven tasks where staffing has historically been the primary throughput bottleneck. These are precisely the conditions where agentic AI delivers its highest leverage.
The current state of RCM AI adoption is fragmented. As of early 2026, most deployments of agentic AI in healthcare have been with third-party healthcare technology vendors; few health systems have deployed agents independently.[1] Those that have done so use them in discrete point solutions rather than integrated across tasks. The technology exists to move beyond this fragmentation. The constraint now is organizational readiness and implementation strategy, not the AI itself.
| CAPABILITY | RPA / LEGACY AUTOMATION | GENERATIVE AI | AGENTIC AI |
|---|---|---|---|
| Task Execution | Rules-based, brittle | Advisory output only | Autonomous end-to-end |
| Exception Handling | Fails; requires human | Suggests; human acts | Resolves dynamically; escalates minimally |
| Cross-System Navigation | Single system, fragile | Limited | Multi-system, adaptive |
| Learning / Improvement | Static unless re-coded | Model-dependent | Continuous from feedback loops |
| RCM Best Fit | Repetitive single steps | Documentation, drafting | Full back-end workflow automation |
The instinct for many health system leaders when confronting enterprise-scale AI transformation is to wait for a comprehensive solution — one that spans the full RCM lifecycle from patient scheduling to final cash posting. This is a strategic mistake. Waiting for end-to-end completeness before deploying has historically produced two bad outcomes: indefinite delay, or rushed full-scale deployments that fail catastrophically.
The far more effective approach is to start with the back end of the revenue cycle, capture demonstrable value, build institutional confidence, and expand sequentially. There are four compelling reasons for this sequencing.
Not all back-end workflows are created equal. The highest-value entry points share a common profile: high volume, clear rule structures, measurable outcomes, and current performance constrained by staffing capacity rather than decision complexity. Here are the four areas where agentic AI deployments are generating the earliest and most significant returns.
Denial management is the canonical high-priority target. The average health system sees roughly 20% of submitted claims denied on first submission. Of those, up to 60% are abandoned without appeal — often simply because the AR team lacks bandwidth to work every denied claim. Agentic AI changes this calculus entirely. Agents can autonomously identify denial reasons, cross-reference payer-specific appeal requirements, draft and submit appeal letters, and track resolution across unlimited claim volumes simultaneously. Early production deployments are reporting denial overturn rate improvements of 15–25 percentage points, with corresponding reductions in write-off rates.
AR follow-up is a volume problem masquerading as a complexity problem. Human billers working aged AR queues must navigate payer portals, IVR systems, document requests, and status tracking across dozens of insurance carriers with inconsistent processes. Agentic AI excels here — it can work a claim queue 24/7, navigate multi-step payer workflows, and prioritize based on real-time probability-of-collection modeling. Health systems using agentic AR tools are reducing days in AR and improving cash realization timelines.
Underpayment identification is often entirely invisible to manual AR processes. When a payer remits 92 cents on the dollar compared to the contracted rate, human reviewers rarely catch it in the volume of EOB processing. Agentic AI can audit every remittance against contracted rates, flag underpayments automatically, and initiate recovery workflows — converting what was effectively invisible revenue leakage into recoverable cash.
While cash posting is increasingly automated through basic tools, exceptions — unmatched remittances, bulk payments, complex contractual adjustments — still require significant manual intervention. Agentic systems can handle the full exception queue, dramatically reducing the manual burden on cash posting teams while improving posting accuracy and audit trails.
Compounding Effect
These use cases are not independent — they compound. Improved denials management reduces the volume entering the AR queue. Cleaner AR follow-up improves cash posting accuracy. Underpayment recovery builds the payer contract intelligence that makes future denials more preventable. The returns from back-end automation accelerate as each use case feeds the next.
To translate McKinsey’s headline figures into operational reality: for a health system generating $6 billion in patient revenue, reducing cost-to-collect from 3.5–4.0% to 2.5–2.0% represents $60–$120 million in annual savings.[1] These are not speculative projections — they are based on the documented performance of production agentic AI deployments at healthcare technology vendors currently operating at scale.
Beyond the headline cost reduction, the financial picture includes several additional dimensions that CFOs should model explicitly. AKASA’s industry survey data shows that hospitals and health systems leveraging automation in the revenue cycle already achieve a cost-to-collect nearly 0.25 percentage points lower than non-automated peers[5] — a margin that widens significantly as agentic capabilities replace first-generation automation. The Harris Williams 2024 healthcare RCM market analysis further documents that fragmented vendor landscapes are a primary structural driver of the $140B annual cost burden,[2] indicating that consolidation through integrated agentic platforms compounds the savings beyond what any single point solution can achieve.
It is worth emphasizing that “cost reduction” is only part of the financial story. Faster cash realization — driven by reduced days in AR — directly improves operating cash flow and reduces financing costs. Denial overturn rate improvements represent recovered revenue that was previously written off. Underpayment recovery converts what was invisible leakage into captured revenue. The combined effect on net revenue per adjusted discharge is material.
The technology is mature. The business case is compelling. The primary risk for most organizations is not whether agentic AI will work — it is whether the implementation approach will generate durable value or stall in a cycle of inconclusive pilots. Based on McKinsey’s analysis and the experience of early production deployments, four principles separate organizations that capture value from those that do not.
Launching with a proof of concept is necessary — but the design of the pilot determines whether it translates into enterprise impact. Too many RCM AI pilots are structured as bounded experiments with success criteria that measure technical capability rather than financial performance. A pilot that demonstrates AI can identify denial patterns is less valuable than one that measures whether overturning those denials faster than the current process closes the gap in AR days.
Define financial and operational success metrics at the outset: denial overturn improvement, reduction in cost per claim, days-in-AR trajectory. Set realistic time horizons — meaningful financial impact typically becomes visible over a 90–180 day period, not weeks. Build the pilot scope large enough to generate statistically meaningful signal, but focused enough to execute cleanly. And establish a clear decision threshold for scaling: “when we see X, we expand to Y.” Without this, pilots drift into what McKinsey aptly terms “pilot purgatory.”
Avoid Pilot Purgatory
These use cases are not independent — they compound. Improved denials management reduces the volume entering the AR queue. Cleaner AR follow-up improves cash posting accuracy. Underpayment recovery builds the payer contract intelligence that makes future denials more preventable. The returns from back-end automation accelerate as each use case feeds the next.
The RCM AI vendor landscape has expanded rapidly, and the pressure from vendors, consultants, and internal champions to move quickly can push organizations toward reactive procurement decisions. The build vs. buy vs. partner question deserves rigorous analysis against the organization’s specific circumstances.
Health systems with sophisticated IT organizations, proprietary data assets, and long-term strategic ambitions in RCM technology may find that building core agent infrastructure preserves valuable intellectual property and creates defensible competitive advantage. Systems prioritizing speed and scale — or those without the technical depth for sustained AI development — may find that enterprise platform partnerships deliver better risk-adjusted returns. Most large health systems will ultimately pursue a hybrid approach: enterprise platforms for commodity workflows, custom development for proprietary processes or unique payer relationships.
What matters is that this decision is made deliberately against strategic objectives, not by default toward whichever vendor reaches the C-suite first.
Within back-end RCM, the sequencing of use cases matters. The highest-value initial targets share a specific profile: extremely high claim volume, clear and learnable rules, quantifiable outcomes, and documented current-state underperformance. Denials management and AR follow-up at commercial payers typically meet all four criteria — making them the near-universal first deployment recommendation.
Resist the temptation to tackle interesting but niche use cases early. An agentic system working 100,000 commercial claims per month delivers more organizational learning and more measurable impact than a technically sophisticated but low-volume specialty use case, even if the latter appears more innovative. Build scale and momentum first; sophistication follows.
The organizations achieving the strongest agentic AI outcomes are not those with the most sophisticated models — they are those with the most deliberate human infrastructure surrounding the AI. This means three things in practice.
First, active exception management. Agentic systems work autonomously on the pattern-governed majority of cases, but they generate exceptions — claims with unusual payer behavior, non-standard denial reasons, coding edge cases — that require human judgment. The quality of exception handling directly determines the quality of the overall system. Staffing and training exception queues deserves as much organizational attention as the AI deployment itself.
Second, AI training governance. Agentic systems improve through feedback loops. Establishing clear processes for how human decisions on exceptions are used to retrain and refine agent behavior — and who owns that process — is critical infrastructure. Without it, the system does not improve at the pace its technical architecture permits.
Third, workforce transition management. RCM staff understanding that agentic AI automates the high-volume routine work while elevating human roles toward exception resolution, payer relationship management, and strategic analysis is not just good change management — it is operationally necessary. Staff who understand and embrace their evolving role become active contributors to AI improvement. Staff who fear displacement become passive resistors who undermine implementation quality.
The back-end entry point described in this paper is not the final destination — it is the on-ramp. McKinsey’s analysis points toward a longer-term vision that is worth keeping in clear view even as organizations focus on near-term execution: an interconnected network of AI agents spanning the full revenue cycle, from patient scheduling through final collections, operating continuously and improving through unified learning loops.
In this vision, agents at each stage of the revenue cycle share information and coordinate: scheduling agents passing verified eligibility to authorization.
