From Automation to Agency: Rethinking the Future of Healthcare

For years, healthcare provider organizations have pursued automation to improve revenue cycle efficiency. Rules-based workflows, robotic process automation, predictive analytics, and machine learning have each helped reduce manual intervention and accelerate the completion of repetitive tasks.

But reimbursement for specialty healthcare services, such as laboratory, radiology, pathology, and specialty pharmacy, is becoming too complex for efficiency alone to solve.

The challenge facing provider organizations today is not simply the volume of claims they must process. It is the speed at which reimbursement conditions can change, the complexity of determining why payment did not occur as expected, and the operational burden of deciding what to do next—often across thousands of claims simultaneously.

That distinction matters.

The next evolution of revenue cycle management may therefore be less about automating more tasks and more about creating systems capable of recognizing reimbursement friction, determining an appropriate response, taking action within established guardrails, and learning from the outcome.

That is the promise of agentic AI.

The Revenue Cycle Has Become an Information Problem

Traditional revenue cycle workflows were largely designed around transactions: submit a claim, receive a response, correct an error, appeal a denial, post a payment.

Today, reimbursement increasingly depends on a much broader information ecosystem.

Coverage policies change. Prior authorization requirements evolve. Coding and billing rules are updated. Documentation requests increase. Payor edits can affect entire populations of claims. A change that initially appears to be an isolated denial may actually represent a broader adjudication pattern.

The operational challenge is identifying that distinction quickly.

Consider a diagnostic organization accustomed to receiving, on average, 200 requests per month for additional information. If that volume suddenly increases to 15,000, processing each request individually is no longer scalable. Even if adding staff members were practical, it does not address the underlying problem.

The underlying questions that must be considered are strategic:

What changed?
Is the activity isolated or systemic?
Which claims, services, locations, or payors are affected?
What evidence is required to respond?
And should the organization continue managing individual transactions—or address the underlying issue?

These are not simply automation questions. They require context, pattern recognition, reasoning, and coordinated action.

From Task Automation to Intelligent Orchestration

Traditional deterministic automation generally begins with a predefined instruction based on rules or business logic: When X happens, perform Y.

That model remains extremely valuable. Many revenue cycle activities are predictable and should be automated in this way whenever possible.

AI, and specifically Agentic AI, introduce a complementary set of capabilities.

Rather than simply executing an assigned task, an agentic system can work toward an objective by gathering information, evaluating conditions, selecting among possible actions, executing approved steps, and assessing the results.

Within healthcare revenue cycle management, that could eventually mean multiple specialized AI agents working together across the reimbursement lifecycle.

For example, an intake agent can be deployed to recognize an incoming payor request. An extraction agent could identify the relevant patient, claim, diagnostic service, and documentation requirements. A strategy agent could evaluate the appropriate response using historical outcomes, payor requirements, and organizational policies. Other agents might navigate external systems, complete forms, assemble supporting documentation, or route exceptions to the appropriate subject-matter expert.

The significant change is not any one of these capabilities.

It is their orchestration.

Instead of asking employees to move between disconnected systems, identify the problem, gather information, determine the next step, and execute it manually, organizations can begin designing systems in which technology coordinates much of that activity while people provide oversight where judgment and expertise matter most.

The Goal Should Not Be an AI Arms Race

There is another important consideration as healthcare organizations adopt increasingly sophisticated technology.

The conversation about AI in reimbursement can easily become adversarial: payors automate, therefore providers must automate faster.

That framing misses a larger opportunity.

Payors have legitimate responsibilities related to appropriate utilization, coverage criteria, payment integrity, and affordability. Provider organizations have equally important responsibilities to deliver medically appropriate services, document those services accurately, and support appropriate and timely reimbursement.

Both sides also bear the cost of unnecessary administrative complexity.

A claim that generates repeated documentation requests, manual reviews, appeals, and resubmissions creates expense throughout the healthcare system. Reducing avoidable friction should therefore be a shared objective.

AI may provide an opportunity to identify where that friction originates.

For example, if thousands of claims begin receiving the same request or denial, AI can help provider organizations identify the pattern earlier. That information can support a more productive conversation with the payor: What is driving the change? Is additional information required? Has an adjudication rule changed? Is there an unintended consequence that should be addressed?

Used effectively, AI can help move the relationship from transaction-by-transaction dispute resolution toward data-informed problem solving.

That is a far more valuable application than simply automating one stand-alone task in a workflow.

Human Expertise Becomes More Valuable, Not Less

Agentic AI also raises an understandable question: If technology can determine and execute the next action, where does that leave the revenue cycle professional?

The answer may be that expertise becomes even more important.

AI systems still require objectives, guardrails, escalation thresholds, validated data, and accountability. They also need people capable of recognizing when an automated conclusion does not reflect clinical, regulatory, contractual, or reimbursement reality.

Experienced reimbursement professionals possess something extraordinarily difficult to replicate: context.

They understand that two denials carrying the same adjustment code may have very different underlying causes. They recognize when a payor request is unusual. They understand the difference between a coding issue and a coverage issue, or between an isolated processing error and an emerging reimbursement trend.

The objective should therefore not be to remove humans from revenue cycle management.

It should be to change where human expertise is applied.

Instead of asking highly experienced professionals to spend much of their day finding information, moving between portals, assembling documents, and repeating predictable actions, AI can potentially absorb more of that administrative work.

People can then concentrate on exceptions, strategy, policy interpretation, payor engagement, and complex reimbursement decisions.

Speed of Learning May Become the Real Competitive Advantage

Perhaps the most consequential benefit of agentic AI is not faster claim processing.

It is faster organizational learning.

Historically, diagnostic organizations may not recognize a reimbursement issue until denial volumes increase, accounts receivable ages, or payment trends deteriorate. By that point, weeks or months of claims may already be affected.

A more intelligent revenue cycle creates the possibility of compressing that timeline:

Signal → Pattern → Insight → Action → Outcome → Learning

Imagine detecting an unusual increase in a denial category within days rather than months. The system identifies the affected claim population, evaluates common attributes, compares the pattern against historical behavior, and alerts a reimbursement expert that the activity may represent an emerging payor issue.

The expert validates the finding and determines the appropriate intervention.

The outcome then becomes part of the organization’s institutional knowledge.

Over time, the advantage is no longer simply that one organization processes claims faster than another. It is that the organization recognizes changes in reimbursement patterns earlier and adapts more quickly.

Preparing for an Agentic Revenue Cycle

The transition will not happen simply by purchasing an AI tool.

Organizations considering agentic capabilities should first examine the foundations of their revenue cycle: data quality, workflow standardization, interoperability, governance, escalation protocols, and measurable outcomes.

They should also be deliberate about where autonomy is appropriate.

A low-risk administrative task may require little human intervention. A complex coverage determination, significant payor escalation, or decision involving regulatory interpretation may require considerably more oversight.

The appropriate model is not unrestricted autonomy.

It is graduated autonomy supported by strong governance and guardrails—allowing technology greater independence where the rules, evidence, and risk tolerance support it while preserving human judgment where the consequences are greater.

That distinction will become increasingly important as AI capabilities mature.

The Next Question for Revenue Cycle Leaders

The first era of revenue cycle automation asked:

What tasks can we automate?

The next era will ask:

What outcomes can intelligent systems help us achieve?

That is a fundamentally different question.

For healthcare leaders, the opportunity is not simply to process more claims with fewer manual touches. It is to build a reimbursement operation capable of detecting change earlier, understanding its implications faster, responding more intelligently, and continuously learning from the results.

Agentic AI will not eliminate reimbursement complexity. Nor will technology replace the need for productive collaboration among provider organizations, payors, clinicians, policymakers, and patients.

But it can change how effectively organizations navigate that complexity.

And ultimately, that may be the more meaningful transformation: moving revenue cycle management from a predominantly reactive function toward an increasingly adaptive one.

About Clarisa Blattner, CCS, CPC

Clarisa Blattner is the Senior Director of Reimbursement Strategy & Industry Affairs at XiFin, Inc. She serves as a subject matter expert in revenue cycle management (RCM) and payor and market strategies, focusing on achieving operational efficiencies and maximizing revenue. With over 25 years of experience in the healthcare sector, Clarisa has an extensive background in revenue cycle management, particularly within the healthcare industry. Previously, she oversaw the billing and reimbursement operations for the outsourced billing department