From automation to orchestration: The next phase of revenue cycle transformation
Roughly two-thirds of healthcare organizations say AI has significantly or fully automated their revenue cycle. Only 3.8% call their systems a "fully unified, orchestrated environment." The gap between those two numbers is where the next phase of the industry will be decided.
Healthcare has a reputation for taking its time with new technology, but AI is the exception. According to new IDC research sponsored by R1, 67.7% of healthcare organizations now report significant or fully AI-driven automation in revenue cycle operations. The pilot phase is behind us and AI now runs inside core revenue cycle operations.
The same study shows what’s hiding behind that headline number. Adoption is up, but maturity isn’t keeping pace. More than two-thirds of organizations (68.7%) run six or more distinct RCM vendors or platforms and the tools are multiplying faster than the strategy to connect them. Healthcare’s challenge is no longer getting AI in the door. It’s building a coordinated operating model that turns AI into measurable outcomes. From here, progress will be defined less by how many workflows are automated than by how well the whole system works together.
Automation isn’t integration
Most healthcare organizations built their technology footprint one problem at a time, adding a point solution whenever a new need arose. Each tool may perform well in isolation. Picture a single denied claim: one system flags it, another routes it, a third handles the appeal and a fourth holds the payer response. Every tool did its job and no one saw the whole story. When dozens of systems, vendors and workflows run independently, leaders are left without a clear view of overall performance.
In conversations with health systems, one theme surfaces repeatedly: the industry isn’t short on data. What’s missing is a way to connect that data across fragmented systems and turn it into coordinated, actionable intelligence. That’s because revenue cycle performance is a system-level challenge. Optimizing a single workflow while the rest stay disconnected may improve one metric without moving overall financial performance.
One consequence shows up in the data. Of the respondents, 88.2% say unexplained revenue leakage is at least a moderate issue. Fixing it takes more than tuning isolated workflows. It takes a connected operating system that can spot opportunities and act on them across the revenue cycle.
More tools, more complexity
As a physician and product leader, I tend to see the revenue cycle as a translation problem. Healthcare organizations must turn clinical care into reimbursement and that translation keeps getting harder as documentation requirements, coding frameworks and payer and regulatory expectations grow more complex. Now add the vendors. Every point solution speaks its own dialect and with six or more in the mix, a claim can be translated, re-translated and occasionally mistranslated before anyone sees the result.
AI is uniquely well suited to this challenge. Advanced models can process information at a scale and level of detail no human team could match. But another AI application doesn’t automatically improve financial performance. Tools that don’t share a data foundation, governance model and operating framework add new layers of operational complexity. So the question for providers isn’t whether to use AI. It’s whether their revenue cycle is built to deliver what they hoped AI would achieve.
Trust starts with a connected foundation
A revenue operating system (ROS) connects an organization’s revenue cycle data, tools and AI so they function as one system instead of many. A mature one rests on five elements: trusted data, enterprise intelligence, interoperability, governance and human oversight. Together, they let healthcare organizations move past isolated automation and toward coordinated decision-making across the revenue cycle.
That connected foundation is also what builds trust. Just 19.8% of respondents say they are “very confident” in AI output reliability. Closing that gap takes trustworthy data, transparent and auditable decisions and clear human accountability across the whole operating environment and not only within individual tools. A true ROS unites clinical and payer data in one intelligence layer, so AI and the people who oversee it make decisions from the same complete picture.
Without those foundations, AI is just another set of disconnected tools. With them, it becomes something far more powerful and it’s exactly what providers say they want: Three-quarters of survey respondents believe a mature ROS would bring significant value to their organization.
Modernizing doesn’t mean replacing every system at once. A true ROS lets providers tackle immediate priorities with solutions built for specific revenue cycle workflows, while tying each one into shared data, intelligence and operational controls. R1’s Phare OS is built on this model. Organizations can begin with their highest-priority workflows and expand incrementally, with each new capability strengthening the intelligence of the whole system.
From adoption to orchestration
Healthcare leaders increasingly see AI and revenue cycle transformation as one priority, not two, and they want to bring their investments together without ripping out what already works. The organizations that pull ahead will be the ones that untangle their fragmented environments and replace a patchwork of tools with a true ROS, one built for the complexity and scale of modern reimbursement.
Healthcare doesn’t need more translators. It needs a shared language.
Transform reimbursement with Phare OS, healthcare’s first revenue operating system.
IDC White Paper, sponsored by R1, The Revenue Operating System: From Automation to Orchestration, doc #US54991926, November 2026.
