The AI Adoption Gap: Why 27% of Health Systems Scale AI While Most Remain in Early-Stage Pilots
Explore why 27% of health systems scale AI while others remain in early pilots. Learn how AI in RCM adoption affects revenue cycle performance.
Rising claim denials, staffing shortages, and an increasing expectation of patients are all placing a growing strain on healthcare leaders. AI’s potential for revenue cycle management has created great enthusiasm, but it’s not a straightforward equation. Just 27% of revenue cycle leaders say they are utilizing AI at scale within multiple functions, with a large share still in the exploration or pilot stages. Nearly all of us have made a decision to take up something, but few are able to keep it up. It is not an adoption issue, it’s a scaling issue. Health systems are increasingly making AI in RCM a top priority to realize benefits in revenue cycle processes.
The Adoption Gap Defined
The figures give a clear picture of where the industry is and how. 38% of health systems have increased their involvement in generative AI for RCM with 80% exploring, piloting or implementing. However, almost half (47%) of enterprises are looking into AI, but haven’t put any plans into action for deployment. 18% are implementing pilots, 18% are scaling up current efforts. 5% are creating holistic plans to transform with AI.
This means that there’s hype of using AI in RCM, but most organizations are not yet making a meaningful scale. The divide in how enterprises are leveraging AI is significant, with some having already advanced to use it on a global scale and others beginning their journey.
AI Investment and Strategy Landscape
| Metric | Percentage of Organizations |
|---|---|
| Exploring AI with no deployment plans | 47% |
| Running AI pilots | 18% |
| Expanding existing AI implementations | 18% |
| Developing comprehensive AI transformation plans | 5% |
| Deploying AI at scale across multiple functions | 27% |
The Strategy Budget Disconnect
Budget limitations add to the difficulty. Nearly half of all organizations (49%) invest 10% or less of their technology budget in AI, while a staggering 35% can’t even guess what portion of their technology budget is being spent on AI. This is because AI investments are frequently integrated into existing IT budgets, and the results are not easily attributable to AI alone, further complicating ROI measurement or tracking.
The budget constraint is exacerbated by the high initial cost of infrastructure investments, integration and change management required to scale AI in RCM. Companies which view AI in RCM as merely a software solution rather than a transformation initiative can find themselves under-resourced to manage the entire AI adoption pathway, from pilot to production.
The Adoption Gap by Organization Size
The adoption gap varies significantly by organization size. Smaller health systems with revenue between $500 million and $1 billion remain in early adoption stages due to budget constraints and implementation challenges. About 20% of these organizations are piloting and implementing, compared to more than half of larger health systems at 64%. The technology divide between enterprise and independent practices is widening as vendor roadmaps prioritize health system deployment first.
This is an alarming trend. Smaller health systems that may be serving rural and underserved populations stand to be left behind as AI-powered efficiencies are gained largely by the larger, better-resourced entities. If larger organizations continue to have access to more resources for implementing AI in RCM, the digital divide could be further exacerbated.
AI Adoption by Organization Size
| Organization Size | Piloting or Implementing AI |
|---|---|
| Smaller Health Systems ($500M – $1B revenue) | ~20% |
| Larger Health Systems | 64% |
Why Adoption Stalls
To understand why some organizations are not successful at scaling beyond pilots, it’s important to look at the barriers to scaling. 51% are concerned with data security and patient privacy as a major barrier. 43% of respondents point to IT infrastructure limits as a top obstacle to integration, while 45% cite challenges gaining integration within their current systems. New AI layers add complexity to legacy billing and EHR systems, complicating the process and potentially disrupting workflow. Organizations considering AI in RCM are impacted by these barriers directly, as RCM systems rely on various integrated platforms and data sources.
The Integration Challenge
The challenge is immense when it comes to embedding AI in RCM( EHR), practice management software and payer portals. Health systems have a patchwork of legacy applications that were not designed to handle modern applications. This means that organizations have to create their own interfaces or completely overhaul their core systems, which are expensive and lengthy.
The challenge is not just technical integration, but organizational too. Teams in the revenue cycle are typically siloed and each department has to deal with a distinct piece of the process. The cross-functional coordination needed when integrating AI in RCM is difficult to achieve in many organizations.
The ROI Visibility Problem
The ROI visibility problem is also very important. So far, only 15% of organizations have seen positive ROI in their AI activities, while 42% do not have the ability to show ROI. This is a workflow problem, and not a model quality problem! If there’s no clear return on investment (ROI) tracking, leaders don’t have the data they need to support expansion of pilot projects to enterprise-wide solutions.
Many organizations are using AI in RCM without setting baselines or measuring success. They don’t know basic answers: How much time did we save? How many denials did we stop? How much money did we get back? With these answers, investments in AI can continue to be at risk of budget reductions and leadership changes.
ROI Visibility from AI Initiatives
| Metric | Percentage of Organizations |
|---|---|
| Report positive ROI from AI | 15% |
| Cannot demonstrate ROI | 42% |
The Human and Cultural Factors
The human and cultural aspects must not be forgotten. Just 7% of health care finance leaders say their staff is “very prepared” for what’s next, while 26% say they lack the support or technical expertise of their staff. The biggest misconception with RCM AI is that it is a “set and forget” type system. In reality, AI in RCM complements human expertise, identifies trends in denials, queues up work, and automates repetitive tasks with little complexity, but it does not take the place of payer strategy, contract interpretation or judgment regarding escalation. If governance, retraining, and monitoring are not sustained, AI’s performance will become less effective and result in revenue leakage.
The fear of losing jobs is one of the reasons why staff members may not be open to the adoption of AI. AI adoption is far more likely to be successful when organizations view AI as a means to make work more meaningful, instead of as a means to replace their workforce. Staff satisfaction and reduced employee turnover are observed among early adopters who have trained staff in AI literacy.
Where AI Is Misapplied
Many organizations also zero in on the wrong end of the problem. It is easy to think of denials as a billing issue that begins when a claim is submitted, but most denials are set in motion much earlier, often during scheduling, intake, or documentation. Common culprits include incomplete eligibility checks, incorrect payer selection, missing prior authorization, and unclear medical necessity language that seal a claim’s fate before billing touches it.
Applying AI in RCM to downstream billing tasks while ignoring upstream revenue cycle functions is like treating a symptom while ignoring the disease. The most successful AI implementations address the entire patient financial journey, from pre-registration through final payment.
The Real Cost of the Status Quo
The financial burden of inefficiency is staggering. US health systems spend over $140 billion annually on revenue cycle operations, roughly 3 to 4 percent of an at-scale system’s revenue. Initial denial rates climbed from 10.2 percent to 11.8 percent in 2024, with 41 percent of providers now facing denial rates of 10 percent or higher. One denied claim costs $25 to $181 to rework, and around 20 percent of claims get denied on first pass while 60 percent are never appealed. For finance leaders, these figures show why AI in RCM is increasingly evaluated against measurable revenue cycle performance.
The Financial Burden of Inefficiency
Denied claims will cost a lot of staff time to rework, which means they will be away from other value added activities. The process of appealing is complicated and time-limited, and there are few resources available for entities to appeal all denials. This leads to huge revenue leakage problems.
The expenses of manual processing don’t stop at the claims. Expensive, non rewarding utilization of prior authorization equals billions of dollars wasted per year in the healthcare system. Staff are spending hours on the phone with payers, faxing out documents and waiting for requests. This is an administrative burden and part of the reason for physician burnout and delays in care.
The Opportunity Cost
The lost opportunity cost is considerable. The potential savings from full-scale AI adoption in the revenue cycle are estimated at 30% to 60% increase in cost-to-collect reduction, according to McKinsey. CAQH Index 2025 attributes $21 billion in administrative savings to automation that could be realized in 2024 without it, and an additional $21 billion in administrative savings to automation that may still be realized in 2024 as well.
Organizations that fail to scale AI in RCM are leaving millions of dollars on the table. In a period of thin margins and increasing regulatory pressure, this is not a risk any health system can afford to take.
The Cost of Inefficiency and Opportunity
| Metric | Value |
|---|---|
| Annual US health system RCM spend | $140+ billion |
| Initial denial rate (2024) | 11.8% |
| Providers with denial rates of 10% or higher | 41% |
| Cost to rework one denied claim | $25 – $181 |
| Claims denied on first pass | ~20% |
| Denied claims never appealed | 60% |
| Potential reduction in cost-to-collect (McKinsey) | 30% – 60% |
| Administrative costs avoided via automation (2024) | $258 billion |
Operational Stress Points
Operational stress points continue to mount. Sixty percent of survey respondents identified staffing as a moderate to major challenge in RCM operations, leading to backlogs in claims processing, longer prior authorization turnaround times, and higher error rates. Claims denial management at 54% and prior authorization at 47% were identified as the most burdensome functions. Documentation and coding errors remain a pressing concern, with 89% of organizations saying missed or inaccurate codes have significant impact on revenue.
These operational pain points are precisely where AI in RCM can provide the most relief. The organizations that are successfully scaling AI have targeted these high-burden areas first, achieving quick wins that build momentum for broader deployment.
Top RCM Operational Stress Points
| Challenge | Percentage of Organizations Citing as Burdensome |
|---|---|
| Staffing as a major challenge | 60% |
| Claims denial management | 54% |
| Prior authorization | 47% |
| Documentation and coding errors (significant impact) | 89% |
Bridging the Gap
Despite the challenges, AI in RCM is delivering measurable results where it is applied strategically. Front-end eligibility and benefit verification catches coverage mismatches and demographic gaps before claims leave the building, with CAQH putting the return at up to 70 minutes per patient visit. Prior authorization automation cuts transaction costs from roughly $3.41 to $0.05, a 98 percent reduction before counting staff hours. These applications show how AI in RCM creates measurable value when deployed around specific workflow problems.
How AI Is Actually Moving the Needle
Predictive denial prevention models trained on payer history flag claims most likely to be rejected so staff can fix them upstream. Early adopters report 30 to 40 percent reductions in denial rates. Autonomous coding engines can navigate over 72,000 ICD-10 diagnosis codes with accuracy rates of 90 percent or higher in specific clinical domains, with coding time on complex cases dropping by close to 46 percent. Generative AI also drafts payer-specific appeals with the right documentation attached, lifting overturn rates and recovering dollars that would have stayed lost.
AI Impact on RCM Functions
| RCM Function | AI-Driven Improvement |
|---|---|
| Eligibility Verification | Up to 70 minutes saved per patient visit |
| Prior Authorization | Cost reduction from $3.41 to $0.05 per transaction (98% reduction) |
| Predictive Denial Prevention | 30% – 40% reduction in denial rates |
| Autonomous Coding | Up to 46% reduction in coding time; 90%+ accuracy |
| Appeals Automation | Higher overturn rates and increased recovery |
The Three Phases of RCM Automation
The most successful health systems approach RCM automation in three distinct phases.
Phase 1 involves automating the deterministic core, starting with high-volume, rules-based tasks where variability is low and outcomes are clear. Applications include eligibility checks, demographic validation, payment posting, and standard claim submission workflows. This phase is less about innovation and more about discipline.
Phase 2 introduces AI for prioritization and prediction. After the operational foundation is stable, AI in RCM helps decide where to focus. Instead of treating all claims equally, AI introduces intelligence that highlights what matters most. This is often where efficiency gains turn into financial impact.
Phase 3 focuses on human decision augmentation. AI acts as a copilot, helping staff navigate the complexity and subjectivity of revenue cycle work. AI can draft appeal language, highlight missing documentation, or explain why a denial is likely to occur. The final decision and accountability remain with humans.
What Separates an AI Pilot from Production
Workflow-native integration means AI lives inside the EHR and billing flow, not a dashboard staff have to remember to open. FHIR-ready data exchange is essential, with CAQH tracking accelerating FHIR adoption ahead of January 2027 federal requirements. Any build that ignores FHIR R4 is signing up for rework 18 months out.
Audit-ready governance provides explainability for every coding suggestion, denial prediction, and appeal the model makes. Without it, payer disputes only get harder. Most importantly, organizations must prioritize processes first, then AI. Map the workflow. Find where handoffs break between intake and collection. Then automate the redesigned process, not the broken one. AI in RCM needs workflow-native integration because staff adoption depends on how easily technology fits existing processes.
Real World Wins from AI in RCM
| Health System | Key AI Initiative | Measurable Result |
|---|---|---|
| Cleveland Clinic | Multiple AI use cases in CDI, coding, and denials | $385M cash acceleration (2025), $65M captured (2026), AR days at 42.7 |
| Novant Health | AI targeted at tasks for elimination | AR days down ~6%, aging dropped 16% |
| Carilion Clinic | Automation across billing and collections | Unbilled days reduced, AR >90 days down 10%, call abandonment reduced 3x |
Moving Forward with the Right Partner
Scaling is not just about a technology vendor, it is about a partner that grasps how AI can be integrated into your organization’s distinctive clinical and financial workflows. A lot of organizations stall out in AI pilot mode due to a lack of direction about workflow redesign, governance arrangements, staff training, etc. A strategic partner can also share experience in implementing across multiple health systems, and can help you avoid some of the pitfalls.
The Strategic Partnership Advantage
You have the right partner on your side who can help you overcome the roadblocks that most AI projects encounter during integration and ensure it enhances your current systems, not replaces them. Partnerships that seek and deliver measurable results, and not just technology deployment, are the ones that result in sustainable scaling. The best partnerships are formed through transparency. A partner who is in it for both the good times and the bad times, can give you transparency on how the software is doing, and can work with you to solve problems, will beat a vendor that just installs software and leaves you to fend for yourself.
What to Look for in an RCM AI Partner
Consider how well an RCM AI partner understands the revenue cycle as more than just technology, payer dynamics, so complex coding, and the regulatory landscape. The ability to share relevant case studies from health systems with similar mixes of specialties and size from AI pilot to production is critical. It is essential to have clear ROI frameworks with a way to measure and prove progress.
Enabling software deployment is only part of the equation when it comes to change management and workforce training. Protecting your investment through interoperability focus that respects your existing technology ecosystem, instead of a rip and replace approach. The right partner should also know the impact of AI in RCM on the daily revenue cycle operations and financial performance. The right partner should also understand how AI in RCM affects daily revenue cycle operations and financial performance.
What to Look for in an RCM AI Partner
| Attribute | Why It Matters |
|---|---|
| Deep RCM expertise | Understands payer dynamics, coding, compliance beyond just technology |
| Proven track record | Can help you move from AI pilot to production with referenceable success |
| Transparent ROI frameworks | Enables you to track progress and justify continued investment |
| Change management commitment | Addresses cultural barriers and builds staff trust |
| Interoperability focus | Respects your existing technology ecosystem |
Why Billing Care Solutions Is Built for This Moment
Billing Care Solutions doesn’t view AI as a replacement strategy for RCM, but as an augmentation strategy for RCM. Our solutions are not designed to replace but to complement your existing team bringing them to a higher level of performance. Technology is not the starting point in the planning process, it is the end. We guide you through the specific areas where AI in RCM can yield the most benefits, given your unique denial trends, payer profile and operational pain points.
We’re integration first, meaning we work within your EHR and billing systems, minimizing disruption and speeding up time to value. We offer straightforward ROI measurement frameworks to keep track of performance and to make informed decisions about scaling. Our change management process spans multiple levels of training to create AI literacy within your revenue cycle team, while also mitigating the cultural obstacles that can hinder successful implementation.
We understand that each health system is different. The value of AI applications varies depending on the factors mentioned above, such as your payer mix, specialty composition, geographic presence, and organizational culture. We don’t have a size that fits all solutions. We work with you to create a personalized plan that fits your strategic goals, instead.
A Partnership Model, Not a Vendor Relationship
We see ourselves as an extension of your revenue cycle team. Our experts collaborate closely with your team to facilitate seamless and sustainable adoption, governance, and outcomes with AI in RCM. We offer continuous model monitoring and retraining to prevent models from falling into degradation to ensure that your AI investment continues to pay off. We are committed to FHIR-ready interoperability and ongoing compliance with changing regulations, ensuring your investment is protected for the long term.
We recognize that the journey of implementing AI is continuous and never-ending. Payer policies evolve, coding standards change, and new capabilities with AI appear; your solution needs to evolve. We are always striving to improve, so you will never be left behind. AI for RCM should therefore be part of an on-going operational strategy and not a single deployment of the technology.
Conclusion
This isn’t the capacity to AI pilot versus scale, it’s about execution, integration and partnership. If you are part of the majority who are still exploring or AI piloting, it’s time to find a partner that can help close that divide.
The organizations that are able to scale up AI will have a lot of competitive advantages to them: They will have reduced cost to collect, faster cash flow, higher satisfaction of the staff and better financial experiences of patients. If they are still in the AI pilot stage, they will still have to face the looming problem of higher denial rates, staffing shortage, and low efficiency in their operations.
It is clear which option to take. But this isn’t about the question of whether or not to go with AI, it’s about how fast you can get from exploring AI to deploying it across the enterprise. Talk with Billing Care Solutions about how we can work together to create a clear path and a mutually beneficial relationship for future success.
