IKS Health Vitalize Summit Unveils Oliver Wyman Research: The Rise of the Adaptive Revenue Cycle
At the recent IKS Health Vitalize Summit, an exclusive gathering of senior healthcare leaders, it was revealed that traditional boundaries between payers and providers are evolving, fueled by AI-driven innovation. This structural shift was a central theme of the summit, anchored by the launch of new research from Oliver Wyman titled “AI in Revenue Cycle is Delivering Results Across Healthcare.”
Today, AI-driven intelligence is replacing manual back-office tasks to create a new “front door” for patient care. The research, unveiled for the first time at Vitalize, shows that while AI is already reclaiming provider time and streamlining workflows, hurdles remain: less than 20% of providers have successfully scaled these tools across their enterprises.
In this overview, we reveal key insights from Oliver Wyman’s 2026 survey of over 200 decision-makers and 90 end users at US provider organizations*. This data highlights the emergence of a new “adaptive revenue cycle,” validating solutions healthcare organizations need most today.
From promise to proof: AI’s decisive move into scalable RCM operations
Oliver Wyman reports that AI in healthcare is transitioning from a period of “promise” to a period of “proof” with revenue cycle management (RCM) leading the way. This evolution delivers much-needed efficiency and scalability to a traditionally rigid revenue cycle.
The market is moving decisively beyond experimentation: roughly 20% to 40% of surveyed organizations now report broad use of AI across the revenue cycle value chain. These deployments span the majority of sites, departments, or clinicians, rather than being confined to isolated pilots.
Broader market data supports this momentum, with 80% of health systems now actively exploring, piloting, or implementing generative AI tools specifically for RCM-a 38% increase in under two years. Furthermore, 63% of organizations have integrated AI-powered automation into their revenue cycle workflows, signaling a decisive shift from isolated use cases to integrated, day-to-day operations. Ultimately, the data leaves no doubt: AI is delivering tangible, repeatable impact at scale within RCM.
The shift upstream: Prior authorization and denials remain the greatest friction points in RCM
The research highlights a clear “shift left” trend, moving error prevention directly into clinical documentation and prior authorization to resolve issues before they ever reach the back office. With 1 in 3 providers identifying denials as their top pain point and 1 in 4 struggling with prior authorization, the most complex stages of the reimbursement cycle remain the industry’s biggest hurdles.
A “preactive” denial management strategy shifts the focus from reactive troubleshooting to upstream prevention. By managing the end-to-end workflow and addressing documentation or authorization gaps at their source, healthcare organizations can eliminate the root causes of financial friction before they impact the bottom line.
Integrating intelligent solutions—such as AI-driven scribing and autonomous coding—directly into the clinical workflow embeds real-time insights at the point of care. This proactive approach ensures errors are intercepted and corrected before a claim is ever generated, rather than simply resolved after a denial occurs.
Strategic RCM investments and the widening performance gap
Oliver Wyman’s research indicates that between 70% and 90% of decision-makers expect to increase spending on AI-enabled RCM over the next three years.
The report identifies a “core demand stack” of “no-regret” investments, specifically highlighting ambient documentation, clinical documentation improvement (CDI), coding automation, and electronic prior authorization (ePA). These tools are succeeding because they integrate seamlessly into existing workflows, address documentation burdens, and produce measurable financial impact.
In practice, the research highlights how leading health systems have achieved coding accuracy rates of 90% or higher for complex cases. By ensuring accurate coded data through AI, these organizations have reduced coding time by nearly 46%, ultimately recovering millions of dollars annually.
Yet despite rising investment, Oliver Wyman notes that enterprise-wide AI adoption remains uneven, creating a widening performance gap across the industry. For example, while AI use in RCM is scaling fast at a 67% overall adoption rate, fewer than 20% of organizations have achieved full, enterprise-wide scale.
This divergence suggests that leading organizations will continue to capture compounding benefits, while smaller or community-based providers risk falling behind due to budget and integration hurdles.
Market shift: From fragmented point solutions to unified platforms
The research shows that the market is moving toward an upstream, integrated, and ROI-proven RCM landscape, favoring unified platforms over fragmented “point solutions.”
An end-to-end RCM platform provides a cohesive workflow backed by deep EHR integration. Instead of relying on siloed tools, a platform partner connects across the front, mid, and back office creating an outcome-oriented architecture that streamlines the entire revenue cycle and removes the burden of manual system integration from the provider.
RCM: The rise of “CFO-led” buying and ROI-driven mandates
Lastly, Oliver Wyman’s research reveals that providers are strictly prioritizing ROI, typically requiring a minimum 2:1 return on investment. Consequently, RCM purchasing is shifting from “innovation-led” to “CFO-led,” where solutions must accelerate cash flow or reduce denials to secure approval. For payers, the report suggests that increased provider coding accuracy is altering cost-of-care trends, necessitating rapid investments in payment integrity.
This strategic approach aligns directly with organizational goals by delivering clear Net Economic Value Added (NEVA) through a combination of cost reduction and revenue lift. Moving beyond basic technology implementation, accountable outcomes and guarantees the precise financial results that modern finance leaders demand.
Closing the gap between AI adoption and enterprise scale
Ultimately, the research concludes that while AI adoption is rising, many organizations have yet to achieve enterprise-wide scale. An intelligent care enablement platform bridges this gap by intentionally embedding human-in-the-loop (HITL) expertise into AI workflow, keeping the technology “honest.”
This approach seamlessly connects clinical, operational, and financial workflows, removing friction and creating measurable impact across the entire care journey. Rather than merely delivering technology, this model guarantees the precise financial results that finance leaders demand to support their clinical mission, allowing health systems to move beyond isolated pilots and achieve the enterprise-scale accountability that the market requires.
Read Oliver Wyman’s comprehensive study: https://hubs.la/Q04fMBCx0
Source: 2026 Oliver Wyman Survey “AI in Revenue Cycle is Delivering Results Across Healthcare.”
*Spans local/rural independent hospitals, large regional/multi-state health systems, academic medical centers, medical groups, and other outpatient care facilities, including ambulatory surgery centers and urgent care.
