Agentic AI: A Smarter Path Forward for Healthcare Revenue Cycle Leaders

By Emily Bonham, senior vice president of product management, AGS Health.

In healthcare revenue cycle management (RCM), we’ve long relied on automation systems that process rules-based workflows with limited or no need for complex logic and nuanced judgement. Robotic Process Automation (RPA) has been highly effective at automating repetitive, high-volume tasks such as claim status checks and data entry.

However, its limitations are increasingly apparent. Today’s revenue cycle challenges demand more than just speed and efficiency; they require adaptability, context, and intelligent decision-making.

That’s where agentic AI comes in.

Agentic AI represents a next-generation approach to automation—one that mimics how humans think, make decisions, and interact with systems and people. Unlike RPA, which follows strict, predefined scripts, agentic AI models operate as autonomous agents. They’re context-aware, goal-oriented, and capable of reasoning across complex workflows. For revenue cycle teams under pressure from rising denials, staffing shortages, and shrinking margins, this kind of intelligence isn’t just nice to have—it’s becoming essential.

What Makes Agentic AI Different?

The simplest way to explain agentic AI is to compare it to a seasoned team member—one who not only knows how to complete a task but also when to escalate, adapt, or reprioritize based on changing circumstances. Agentic systems can:

• Interpret and act on real-time data from multiple  sources
• Make decisions without human intervention
• Learn from patterns and improve over time
• Collaborate with human team members when needed

In practical terms, this means AI can now triage claims, initiate and complete payer calls, route work dynamically, or even autonomously document and code encounters—all with logic and consistency.

Why This Matters for RCM

Healthcare RCM is a perfect candidate for agentic automation because it sits at the intersection of structure and unpredictability. Processes are highly regulated, but real-world conditions vary constantly. Consider these examples:

• Accounts receivable: Agentic AI can identify which claims require expert attention and which can be resolved through automation, ensuring staff spend their time where it’s most needed.
• Insurance follow-ups: AI agents can navigate payer phone trees, wait on hold, retrieve claim information, and even update the EHR, without tying up human resources.
• Denial management: Instead of flagging a denied claim for review, an agentic system can analyze the denial reason, check documentation, and suggest or initiate corrective actions.

These aren’t distant possibilities—they’re already being piloted and implemented in real-world environments.

The Human + Agentic AI Model

It’s important to note that agentic AI is not about replacing people—it’s about augmenting them. The most effective models combine human oversight with AI execution:

• Human experts oversee automated workflows, handle edge cases, make nuanced judgment calls, or perform relationship-driven tasks.
• AI agents handle high-volume, rule-governed, or low-dollar work with consistency and speed, while equipping staff members with insights and suggested actions.

This hybrid approach doesn’t just improve throughput; it also enhances job satisfaction for teams that no longer spend their days on tedious follow-ups or simple reconciliations.

Getting Started with Agentic AI

For organizations beginning to explore this space, here are a few guiding steps:

1. Consolidate and clean your data: Fragmented data across EHRs, billing systems, and vendor platforms limits AI effectiveness. Start by creating interoperable, governed data environments.
2. Identify high-ROI use cases: Look for repeatable processes with moderate complexity and clear financial upside, like denial prediction, prior authorization automation, or A/R follow-ups.
3. Experiment with short feedback loops: Choose pilots where you can quickly assess ROI and adjust based on results. Don’t aim for perfection—aim for momentum.
4. Build trust through transparency: Ensure your AI systems are auditable and explainable, especially when financial decisions are being made autonomously.

A Path to Sustainable Margins

Every healthcare leader is being asked to do more with less: deliver care, navigate compliance, and protect financial performance. Those who lead with tech-forward cultures by embracing intelligent automation and prioritizing data cleanliness in their revenue cycle operations are well-positioned to rise to the occasion. In contrast, those who resist innovation due to skepticism or overly protective and risk-averse policies risk falling behind—exposing their financial performance to volatility and long-term disruption.

Agentic AI offers a path forward, not as a magic bullet, but as a powerful tool for reclaiming time, improving accuracy, and aligning resources where they have the most impact.

It’s still early days for agentic AI in healthcare RCM, but the direction is clear. With the right balance of vision and pragmatism, revenue cycle leaders can unlock a new level of operational intelligence and move closer to sustainable, value-driven performance.