Part 3 of the “AI-Driven Future of Revenue Cycle Management” series by Jason McNeal, Experity EVP of Revenue Cycle Management 

Healthcare revenue cycle management (RCM) has long depended on offshore labor to handle high-volume, repetitive administrative work such as medical coding, eligibility verification, claims processing, and denial management. This model emerged because these tasks are labor-intensive, rules-based, and costly to perform domestically.

A structural shift is underway. Advances in artificial intelligence (AI), healthcare RCM automation, and agentic systems are redefining how revenue cycle work gets done. The trajectory of these technologies suggests that offshore RCM labor, while unlikely to disappear overnight, could be significantly reduced and, in some cases, rendered obsolete.

1. The Nature of RCM Work Makes It Highly Automatable

Most traditional RCM functions share three characteristics:

• High volume
• Rule-based decision logic
• Heavy reliance on structured and semi-structured data

These are precisely the types of tasks AI excels at automating.

Modern AI systems can already:

• Scrub claims before submission
• Verify insurance eligibility in real time
• Automate coding using clinical documentation
• Route and resolve denials using predictive analytics

As these capabilities mature, the economic rationale for maintaining large offshore teams whose primary advantage is lower labor cost begins to erode.

2. The Rise of the “Touchless” Revenue Cycle

The concept of a “touchless” revenue cycle, where minimal human intervention is required, is moving closer to reality.

AI can accelerate claims processing, automate portions of prior authorization, and connect processes that previously required manual intervention.

This evolution has significant implications for offshore RCM models. Offshore labor has historically provided human intervention at scale. As healthcare RCM technology reduces the need for those manual touches, the amount of labor required to support them also declines.

3. Agentic AI: From Assistance to Autonomy

The next phase of innovation is agentic AI: systems capable of taking action independently rather than simply assisting human users.

These systems can:

• Identify missing documentation
• Retrieve and validate clinical data
• Apply payer-specific rules
• Correct and resubmit claims autonomously

This represents a potential turning point for RCM healthcare automation. Offshore teams have historically handled much of the exception management, rework, and follow-up within the revenue cycle. Agentic AI is increasingly targeting those same functions.

4. Evidence of a Market Shift Away From Offshore Models

There are already signals that healthcare organizations are reconsidering traditional offshore RCM models:

• Pivoting toward AI-driven, software-led models
• Reconsidering outsourcing contracts
• Increasing focus on compliance, data control, and transparency

These shifts point toward a period of transition as healthcare RCM companies determine where automation can replace or reduce labor-intensive processes.

5. Cost Dynamics: Software vs. Labor

Offshore RCM has historically been justified by labor arbitrage. AI introduces a different cost structure.

Offshore Labor

• Linear scaling, with more work requiring more people
• Moderate error rates

AI Automation

• Near-zero marginal cost at scale
• High scalability
• Lower error rates

AI systems can operate continuously, scale rapidly, and reduce rework, making healthcare RCM automation increasingly cost-effective as the technology matures.

6. What Will Remain Human, at Least for Now

Despite this shift, some roles will persist:

• Complex payer negotiations
• Exception handling in ambiguous cases
• Compliance oversight and auditing
• Strategic revenue optimization

These functions require a smaller number of highly skilled employees rather than the large workforces traditionally used to support repetitive RCM processes.

7. The Likely End State: A Hybrid but AI-Dominant Model

The transition is unlikely to happen all at once. Instead, the industry could progress through several stages as AI capabilities mature and healthcare RCM companies become more comfortable shifting work from people to technology.

Phase 1: Now to 2 Years

Hybrid model with AI augmenting offshore and onshore teams

Phase 2: 2 to 5 Years

Reduction in offshore headcount and expansion of automation

Phase 3: 5 to 10 Years

AI-dominant operations with minimal human intervention

From Offshore Dependency to AI-Enabled Operations

AI is redefining revenue cycle management as systems become capable of handling more of the structured, repetitive work traditionally performed by offshore teams.

The economics of that work are changing as well. Organizations can increasingly use software to perform tasks that once required large teams, reducing the advantages of labor-based scaling and creating new opportunities for efficiency.

Offshore RCM resources will not disappear overnight, but their role will likely shrink significantly as AI adoption accelerates.

If current trends continue, the traditional offshore RCM model could ultimately be replaced rather than simply optimized.

As AI reduces manual intervention across the revenue cycle, the industry moves closer to a near-touchless practice management environment.

This article is part of the “AI-Driven Future of Revenue Cycle Management” series exploring how artificial intelligence is transforming healthcare billing, reimbursement, and operational strategy.

Explore Experity RCM