Artificial intelligence is emerging at a time when health systems are navigating sustained financial pressure, rising denial costs, workforce challenges, and increasing payer complexity.
For much of the past two decades, the answer to increasing revenue cycle complexity was relatively straightforward: add capacity. Hire more specialized staff. Create more teams. Add technology to support growing volumes. Standardize processes wherever possible.
Today, the equation is changing as AI is beginning to automate more of the routine work that has traditionally required significant human effort. This creates an opportunity for revenue cycle leaders to reevaluate their current operating model.
The fundamental question is shifting from who should perform the work to what capabilities an organization needs to sustain financial performance as the work itself continues to change.
The opportunity is not simply to do today’s work faster or with fewer people. It is to rethink how revenue cycle operations can perform as financial pressure increases, payer requirements change, and more of the work becomes automated.
Five structural forces are reshaping revenue cycle economics and driving that shift.
Economic Force #1: Margin Compression Is Elevating Revenue Cycle to a Strategic Function
The first force is the simplest: there is less room for error.
Health systems continue to operate under sustained financial pressure. Kaufman Hall’s National Hospital Flash Report found that adjusted hospital operating margins ended 2025 at approximately 1.3%. The American Hospital Association also reported that workforce expenses represented 56% of total hospital costs in 2024.
With rising labor costs, reimbursement pressures, and capital requirements, organizations have less room to absorb reimbursement delays and avoidable administrative costs.
That makes revenue cycle increasingly important to enterprise financial performance. Measures such as cost to collect and days in accounts receivable remain critical. But revenue cycle leaders are increasingly focused on a broader set of questions:
- How much earned revenue is being protected?
- How quickly is cash being realized?
- Where is unnecessary administrative expense being created?
- Where are operational problems affecting financial performance before they become visible in traditional financial metrics?
Continuous operational improvement is becoming part of financial strategy.
This makes the cost of inefficiency, and the ability to identify its root causes, more consequential than ever.
Economic Force #2: The Rising Cost of Denials
Few areas illustrate the connection between operational performance and financial performance more clearly than denials.
HFMA estimates that approximately 10–15% of claims are initially denied, with many requiring costly manual intervention before payment. Crowe’s Revenue Cycle Analytics benchmarking also shows denial volumes climbing across health systems, driven in part by growing payer scrutiny and documentation complexity.
Each denial can generate additional work, delay reimbursement, and consume specialized staff resources. At scale, denial patterns can also point to broader operational issues, including documentation inconsistencies, workflow variation, payer policy changes, and technology limitations.
Viewing denials as indicators of operational performance gives organizations an opportunity to identify the conditions creating them and address underlying issues before they affect future claims.
Economic Force #3: Administrative Labor Scarcity Is Changing Workforce Strategy
Revenue cycle depends on specialized expertise across coding, patient financial services, denial management, reimbursement, authorization, and other functions. Maintaining and expanding that expertise through additional headcount can be difficult and expensive.
This is changing how health systems approach workforce investment.
Rather than responding to administrative complexity primarily by adding labor, health systems have an opportunity to rethink where human expertise creates the greatest value.
Reducing repetitive work and standardizing routine processes can create capacity for experienced employees to focus on activities that require judgment, problem-solving, and specialized knowledge.
That distinction becomes especially important as AI begins to make routine transaction work less expensive to execute.
Economic Force #4: Artificial Intelligence Is Reducing Transaction Costs
For decades, revenue cycle performance depended heavily on the ability to execute high volumes of repetitive, rules-based work.
AI is changing the economics of that model. McKinsey has identified administrative activities as one of healthcare’s largest opportunities for automation, with significant potential to reduce manual effort while enabling employees to focus on higher-value decision-making.
Activities including documentation review, coding support, prior authorization workflows, claim status inquiries, payment variance analysis, and work queue prioritization can increasingly be supported or automated through AI.
As routine transactions require less manual effort, the value of human expertise shifts toward work that is harder to automate: managing exceptions, optimizing workflows, responding to changing payer behavior, and making operational decisions.
As automation becomes more widely adopted, it becomes increasing important for revenue cycle leaders to understand the work technology and people are performing.
Automation can execute a process; however, it cannot tell a leader that a payer has changed its behavior, that a workflow is creating unnecessary touches, or that a seemingly minor process variation is beginning to affect financial performance.
And that challenge becomes even more significant as payer requirements continue to evolve.
Economic Force #5: Payer Complexity Is Increasing Information Requirements
Technology may be reducing the effort required for some administrative work, but payer requirements continue to evolve and become more complex.
Health systems must navigate changing reimbursement methodologies, prior authorization requirements, documentation standards, policy updates, and claims editing processes across hundreds of payer relationships.
The CAQH Index estimates that the U.S. healthcare system still performs billions of manual administrative transactions each year, representing billions of dollars in avoidable costs that could be reduced through greater automation and process standardization. The gap is stark at the transaction level: by the most recent year in the index, the average manual transaction cost more than three times as much as an automated one, $8.03 versus $2.65, and that gap has widened over the prior two years.
This creates an information challenge as much as an execution challenge. Knowing that reimbursement performance changed is only the beginning. Leaders also need to understand:
- Which payer behaviors changed?
- Where are those changes affecting workflows?
- Which teams or processes are being impacted?
- Are those changes creating additional work or financial risk?
- Where would intervention have the greatest impact?
Faster access to operational insight can help organizations respond to these changes before financial performance deteriorates.
How Changing Revenue Cycle Economics Are Transforming the Operating Model
Payer complexity is only one part of the equation. Across all five forces, the same pattern is emerging.
Margins are tighter, making operational inefficiency more expensive.
Denials are creating more downstream work, making the ability to identify root causes more valuable.
Labor is harder to scale, increasing the importance of how specialized expertise is deployed.
And payer complexity is increasing the volume of information leaders need to understand and act upon.
At the same time, AI is changing how much of the underlying work actually requires human intervention.
That creates a fundamental shift in the revenue cycle operating model.
As labor becomes more expensive, automation becomes more valuable. As automation takes on more routine work, the allocation of human expertise becomes more important. And as more processes become automated, understanding what is happening across those processes becomes increasingly critical.
The question is no longer simply how health systems can execute more work.
It is how they can use technology to execute the work, while giving leaders the ability to understand what is changing, where it matters, and what to do about it.
That is where the impact of AI begins to extend beyond the cost of execution.
AI Changes More Than the Cost of Execution
AI plays a unique role in that shift. As technology reduces the manual effort required for routine transactions, organizations have an opportunity to move from scaling labor to scaling expertise.
Experienced staff can spend more time managing exceptions, improving workflows, responding to payer changes, and applying their knowledge where it can have greater impact.
But reducing manual work is only part of the opportunity.
As transactional work becomes more automated, organizations need greater visibility into how that work is being performed. They need to recognize emerging payer trends, identify workflow variation, understand how operational decisions affect financial performance, and determine where intervention will create the greatest value.
The limiting factor begins to shift from the capacity to execute work to the ability to continuously understand, adapt, and improve how that work is performed.
Rethinking Revenue Cycle AI Strategy
For health systems evaluating revenue cycle AI, automation is an important consideration. But it should not be the entire conversation.
The more important question may be what the technology enables the organization to see, understand, and improve.
- Can leaders see how work is moving through the revenue cycle?
- Can they identify variation before it affects reimbursement?
- Can they recognize changes in payer behavior as they emerge?
- Can they understand which operational conditions are driving financial outcomes?
- And can they use those insights to continuously improve how the organization operates?
These capabilities become more valuable as AI expands across revenue cycle operations.
The next phase of revenue cycle AI will require organizations to connect execution with operational insight, using what they learn from daily operations to make better decisions and respond faster to changing conditions.
Scale and execution still matter. But as AI reduces the manual effort required for routine work, the organizations that pull ahead will be the ones that can act quickly on what their own operations are telling them.
Explore the full whitepaper, Rethinking Revenue Cycle Strategy in the Age of AI, for a deeper look at how these forces are reshaping revenue cycle strategy and the capabilities health systems will need for what comes next.