Many healthcare leaders are now looking beyond traditional forms of automation to integrate artificial intelligence (AI) into revenue cycle management (RCM). AI agents can learn, reason, and collaborate with human teams. Agentic AI represents a transformative leap forward in how healthcare organizations manage revenue cycle workflows, improve business office efficiency, and deliver better financial outcomes.
What Are AI Agents?
AI agents are a more sophisticated form of digital agent than simple process automations. They are designed and built with advanced AI technologies, such as large language models (LLMs), generative AI (GenAI), knowledge graphs, and machine learning (ML), that enable them to manage complex workflows autonomously while collaborating seamlessly with human associates.
Unlike traditional process automations, which use simple logic and static scripts to execute predefined tasks, these digital agents leverage continuous learning, contextual understanding, and multi-step decision-making capabilities. This evolution enables AI agents to adapt to dynamic scenarios, interact with systems and humans, and autonomously take next-best actions based on real-time data to enhance how work is done. Revenue cycle professionals can offload more logic-driven workflows to AI agents, enabling them to focus on oversight, exceptions, and areas where their deep expertise and nuanced judgment add the most value.
AI agents can be designed to operate across a broad range of functions or to focus deeply on highly specialized processes. Broad-ranging agents draw from expansive datasets and diverse knowledge sources, enabling them to support complex, cross-functional initiatives with a wide variety of possible outcomes. In contrast, specialized agents are tuned to narrower datasets and specific process goals, allowing them to deliver highly targeted, precise results that align closely with the requirements of a particular workflow. This flexibility means organizations can deploy AI agents strategically by either addressing enterprise-scale objectives or optimizing discrete, high-impact processes.
Advantages of AI Agents
The shift from traditional process automation to AI agents is marked by several key advancements:
- Continuous Learning: AI agents evolve over time, ingesting new data, documentation, and feedback to improve their accuracy and efficiency. They evolve with each new data point, feedback loop, or exception handled.
- Collaboration: Unlike basic process automations, which execute tasks in isolation, AI agents actively collaborate with human associates and systems, ensuring a more integrated approach to problem-solving. They work with information systems, other digital agents, and people to allocate tasks, track results, and iterate intelligently with autonomy.
- Contextual Awareness: Unlike process automation, AI agents adapt to complex scenarios and dynamically decide on the next best action. By understanding the context of their tasks, they can make informed decisions and effectively handle exceptions.
- Scalability: AI agents are more able to scale across workflows with minimal human intervention, adapting to specific rules and payer-specific requirements.
Applications for Healthcare Revenue Cycle Management
Workflows that are document-heavy, rule-bound, and highly variable present ideal conditions for AI agents to learn and evolve based on customer and payer-specific processes. Initial areas of RCM using digital agents include:
- Eligibility Verification: Reducing eligibility-related denials by automating verification processes.
- Denial Management: Accelerating denial resolution and increasing recovery rates through intelligent workflows that investigate root causes, gather supporting documentation, and trigger appeals.
- Authorization Management: Preventing authorization denials by managing pre-certifications with payers and proactively identifying and addressing gaps to reduce downstream payment issues.
- Appeals Processing: Improving overturn rates and reducing write-offs by generating accurate and comprehensive documentation packages.
Building AI Agents: A Collaborative Effort
The development and deployment of AI agents requires a multidisciplinary approach and an expert-level understanding of RCM workflows, payer behavior, and exceptions. Creating and training these intelligent agents requires effectively ingesting data, operating procedures (SOPs), payer documentation, and historical interactions to build workflows that identify when human involvement is needed. A strong governance structure is important for ensuring seamless integration and establishing proper security and compliance protocols to ensure the agents are deployed effectively.
The Future of Digital Agents
The journey of agentic automation is one of continuous growth and improvement. From process automation to intelligent automation to agentic AI, digital agents are transforming how revenue cycles operate. However, healthcare leaders would be wise to proceed with purposeful intent and at a pace conducive to change management best practices.
AGS Health recommends identifying a strategic roadmap that begins with mapping workflows for high-volume, high-value areas and establishing a system for continuous improvement. As AI agents ingest more data and receive feedback, their capabilities will expand, enabling them to handle increasingly complex workflows. This iterative process for improvement ensures that digital agents remain aligned with the evolving needs of the healthcare organization’s staff and strategic priorities.
Digital agents built on agentic AI have the potential to scale capacity while also enhancing decision-making, reducing waste, increasing accuracy, and elevating the work humans do best. Learn more about how to collaborate and adapt healthcare operations to drive better RCM outcomes in our white paper, A Healthcare Leader’s Guide to Building a Digital RCM Workforce.
