By Inger Sivanthi, CEO, Droidal
LinkedIn: Inger Sivanthi
LinkedIn: Droidal
Revenue cycle management has never lacked for investment. Over the past decade, hospitals and health systems poured resources into better billing platforms, smarter clearinghouses, denial management tools, and more recently AI-powered coding engines. The infrastructure grew. The underlying problem did not shrink.
Claim denials remain one of the most persistent failures in healthcare operations. Roughly 15 to 20 percent of all claims are denied on first submission, and a significant portion of that revenue is never recovered. Spending more on the same layer of the workflow has not changed that trajectory in any meaningful way.
The reason, increasingly clear to those watching this space, is that the industry has been automating the wrong layer.
What Automation Actually Solved
The first wave of RCM automation addressed volume. Eligibility checks, remittance posting, and claim scrubbing were repetitive, rules-based tasks. Automating them reduced manual workloads and helped billing teams handle higher volumes without proportional headcount growth. That was real progress.
But automation never reached the judgment-heavy work. Denial management still required someone to investigate root causes. Prior authorization still demanded staff to navigate payer portals, gather clinical documentation, and follow up repeatedly. A claim could pass every automated scrubbing check and still get denied because a documentation gap three steps upstream made the medical necessity case indefensible.
The AMA’s 2024 Prior Authorization Physician Survey found that physicians and their staff spend an average of 14 hours per week on prior authorization tasks alone, completing roughly 45 requests per physician per week. Automation never seriously touched that burden. It worked around it.
A Different Kind of Capability
What is emerging now is not faster execution of defined rules. It is systems that operate with enough contextual awareness to make workflow decisions and catch what human reviewers sometimes miss.
Predictive denial models trained on payer-specific adjudication histories can now assign risk scores to claims before submission, surfacing documentation gaps or coding inconsistencies that correlate with rejections. The intervention point moves upstream. Problems get addressed before a claim goes out rather than after it returns denied.
Prior authorization is seeing a parallel shift. AI systems can review clinical documentation before submission, checking whether the elements required for approval are actually present in the record. Agentic workflows then handle submission routing, reducing human touchpoints without removing clinical accountability. The decision about which requests need escalation is made by the system rather than by a staff member working through a static checklist.
That distinction matters. It is the difference between a tool that follows instructions and one that interprets context.
Where Implementation Gets Complicated
The technology has moved ahead of organizational readiness in several areas. That creates a specific kind of friction in revenue cycle work.
Data quality is a constraint that rarely gets adequate attention. Predictive models are only as reliable as the historical data feeding them. Organizations with inconsistent coding practices or fragmented EHR configurations will find that AI surfaces the inconsistency rather than correcting it. The output quality reflects the input quality. That is not a flaw in the technology; it is a diagnostic finding about the underlying workflow.
Workflow integration is the other constraint. Deploying an AI tool without redesigning the surrounding steps tends to produce partial adoption. Staff use the tool selectively, override recommendations when results look unexpected, or maintain parallel manual processes as a fallback. The difference between implementations that deliver results and those that plateau early is usually in how the operational change was scoped, not in the AI itself.
What Autonomy Actually Requires
Autonomous AI in RCM does not mean unattended processes. It means a reallocation of human attention away from routine execution and toward exception handling, escalation, and process refinement.
Billing staff with deep payer expertise do not immediately defer to model outputs, especially when a recommendation conflicts with their own pattern recognition. That skepticism is reasonable. Demonstrating accuracy over time and making model logic visible tends to matter more for long-term adoption than benchmark performance.
The audit trail question is equally important. CMS and commercial payers are increasing scrutiny of AI-generated coding and prior authorization decisions. Deterministic architectures, where the same input produces the same output every time, are easier to defend in an audit than probabilistic systems that can return different results for the same claim on different runs. That architectural difference does not often come up in vendor evaluations, but it should.
The organizations building durable capability in AI-driven RCM are not necessarily those with the most advanced technology. They are the ones that asked harder questions before deployment, about data quality, workflow redesign, staff readiness, and auditability. Those are the questions that determine whether the shift from automation to autonomy actually changes the financial picture.