Healthcare has spent years trying to optimize a revenue cycle operating model that no longer works. Every new wave of technology has promised to make work queues more efficient. Better analytics. Better automation. Now AI.
We’ve spent years asking, “How do we work every claim faster?” The better question is, “Why are people working so many of these claims at all?”
The problem isn’t that revenue cycle teams can’t keep up. The problem is that we’ve built operating models where people are expected to touch far too many claims. Human attention has become the scarcest resource in the revenue cycle, yet we continue to spend it on routine work where human judgment adds little or no value.
AI is a critical part of the solution, but AI alone won’t fix RCM. Replacing people with AI inside the existing work-queue model simply automates the same flawed operating model.
The future of the revenue cycle ensures that only the claims requiring human judgment ever reach a person. That requires an exception-driven operating model where people, workflows, and AI are continuously orchestrated around the work that matters most.
Stop Working Every Claim
Health systems are being squeezed from both directions. Payers are denying more claims while the cost of recovering revenue continues to rise. Automated payer decision-making, evolving documentation requirements, and increasing prior-authorization complexity have dramatically increased administrative workload. At the same time, providers can’t simply hire their way out of the problem.
It’s time to stop working on every claim and only work on the claims where human judgment actually changes the outcome.
An exception-driven revenue cycle requires organizations to rethink three questions:
- What actually qualifies as an exception?
- Where does human judgment create financial value?
- How should success be measured?
Technology is a critical enabler of this transition, but AI alone is not enough. The real challenge isn’t simply automating more work. It’s continuously deciding which claims deserve attention, which can be resolved automatically, and when human judgment will actually change the financial outcome. That requires intelligent orchestration. Revenue cycle platforms should use intelligence to identify the claims that matter most, automate routine administrative work, and route the remaining exceptions to the right person with the right context. As payer policies, documentation requirements, and denial patterns continue to evolve, that orchestration must continuously adapt alongside them.
Restructuring Around the Exception-Driven Model
It may be counterintuitive, but technology is actually the easier part of shifting to an exception-driven RCM model. The harder part is organizational: defining what an exception should be, aligning people and roles, and redefining success.
Leadership must be bought in and directly own the definition of what a true exception looks like and where a claim actually requires human judgment. From there, teams need to be aligned, evaluated, and compensated around resolving the cases that actually move money, instead of just volume. This may mean retraining employees and changing the way their jobs are measured. The focus shifts from things like claims touched, tickets closed, and queues cleared to cost to collect, leakage, yield, and ultimately margin.
This new model also requires a different kind of team: people who understand traditional accounts receivable but are also skilled with the technology around it, including APIs and data analytics. That may mean looking outside traditional healthcare backgrounds or offering more training internally.
Getting Started
Shifting to an exception-driven model won’t happen overnight, but here’s how to start thinking about it today:
- Decide where humans actually belong. Define which set of claims actually needs to be routed to a person. Everything else should be automated.
- Measure where human judgment changes outcomes. In the old work-queue model every claim was worked by a person but today it’s clear that only a few actually need one. The amount of claims that can be automated is almost always bigger than you’d expect.
- Judge technology by how well it orchestrates, not how much it automates. The real test of any system is whether it can continuously and intelligently decide what to automate, what AI should handle, when to involve a person, and what context to attach.
- Start small, then expand. Prove the model in one small but troublesome area before scaling it across the revenue cycle.
The next generation of revenue cycle leaders won’t process more work. They’ll eliminate the work that never needed to happen in the first place.
Team size and agent count won’t dictate success. The organizations that come out ahead will be the ones that treat human attention as their scarcest resource, orchestrating people, automation, and workflows around the small number of claims where human judgment still changes the outcome.
About Akash Magoon
Akash Magoon is the co-founder and CEO of Adonis, an AI orchestration platform for healthcare revenue cycle management. He has focused his career on building AI that helps health systems detect revenue cycle issues, recommend action, and resolve claims autonomously, so people can focus their attention where it matters most. Under his leadership, Adonis has grown to serve health systems across the country, including Mount Sinai, achieved more than 4x revenue growth in 2025, and raised over $95 million to date.
