Healthcare payers and providers are currently operating in a pressure cooker of financial and operational strain. And at the same time, administrative costs continue to climb, having now reached the $12 to $19 per claim range.

Compounding this, the industry is grappling with ongoing staffing shortages and a burnout rate that affects nearly 50% of healthcare workers. While many organizations have turned to RPA and traditional AI to bridge these gaps, they are finding that these tools, despite being undoubtedly helpful, have reached their functional ceiling.

The limitation of classic RPA is its rigid, rule-based nature. It works perfectly until it encounters a “messy” real-world scenario that doesn’t fit a pre-defined “If-Then” statement. Similarly, generic AI often falls short because it lacks the deep operational context required for complex, highly regulated healthcare workflows.

The Agentic AI + Automation Power Pair in Healthcare

At Naviant, we believe the next leap in performance won’t come from a single tool, but from a digital workforce where bots, agents, and employees work in tandem. In this model, bots facilitate repetitive, high-volume tasks, agents decide how to navigate complex goals, handle exceptions, and coordinate multi-step workflows, and employees supervise and intervene as needed through the human-in-the-loop approach. Today, we’ll be zoning in on the roles and partnership of the tech pieces, being agentic AI and automation, but you can learn more about the importance of human-in-the-loop here.

So, let’s take a closer look at these two technologies’ differences and how their combined use can help healthcare leaders finally achieve real, transformational impact. Plus, we’ll go through five high-impact workflows, from claims processing to value-based care.

Why “Automation + AI” ≠ “Automation + Agentic AI”

To understand why this combination is a game changer, we need to define the three distinct roles in our digital workforce:

RPA (The Rule-Follower): RPA excels at predefined tasks and specific commands with impressive precision. It is the “muscle” that moves data between disconnected systems.

Traditional/Generative AI (The Content Creator): These models can write, summarize, and draft. They are incredibly useful for administrative tasks but are typically “task-specific”—they create content but cannot unilaterally coordinate a complex process from start to finish.

Agentic AI (The Goal-Driven Orchestrator): Unlike its predecessors, Agentic AI demonstrates autonomy and learning. It has the unique ability to navigate complex environments independently yet within predefined governance and escalation boundaries. Plus, it can make real-time, goal-driven decisions within the parameters of a process.

When you combine them, you create a resilient digital workforce. For example, in a traditional setup, a bot might pull a medical record, an AI might summarize it, but a human must still decide whether that record justifies a claim.

In an Agentic setup, the agent receives the goal (“Validate this claim”), breaks it into steps, calls the bots to fetch data, interprets the summary, checks it against policy rules, and then involves a human when a high-risk exception occurs.

How the Handoff Works: Bots Facilitate, Agents Decide

The architecture of a modern digital workforce consists of three interconnected layers:

The Orchestration Layer: This layer takes on the role of supervisor, telling task agents what to do and involving human reviewers as needed.

The Agent Layer: These are the “thinkers.” They receive high-level goals, decompose them into specific tasks, and reason through data to determine the next best action.

The Bot Layer: These are the “doers.” They are called by the agents to interact with legacy systems, scrape portals, or move files.

A typical flow looks like this:

• The agent is given a goal, such as “Process this prior authorization request.”
• The agent decomposes this goal into tasks: check eligibility, gather clinical evidence, and populate the payer form.
• The agent calls a bot to login to the EHR and fetch the records.
• The agent then interprets the clinical data, compares it to medical necessity rules, and prepares the submission.
• If the data is incomplete or the case is borderline, the agent escalates to a human clinician for a final review.

This system relies on clear decision boundaries. In healthcare, human oversight is an absolute requirement for safety-critical or high-risk decisions. The agent handles the administrative “heavy lifting,” while the human remains the ultimate authority for clinical judgment.

Five Workflows Where Agentic AI + Automation Delivers

1. Claims Processing (Payer)

• Current Pain: Rising denial rates, which currently average 20% across all claims. Manual processing is slow, prone to errors, and costs between $12 and $19 per claim.
• RPA Alone: Extracts data from forms like CMS-1500 and moves it into adjudication systems.
• The Agentic AI Teammate: By identifying the underlying reasons for denials and automatically compiling the necessary clinical documentation, agents can even generate initial appeal drafts to save staff time.

KPIs:

• Reduced Denial Rates: Lowering medical denials through proactive detection.
• Lower Administrative Cost per Claim: Moving closer to “straight-through processing.”
• Faster Adjudication Cycle Times: Reducing the time from submission to payment.

2. Patient Scheduling and Resource Allocation (Provider)

• Current Pain: Access centers are overwhelmed, leading to long wait times, scheduling errors, and poor utilization of expensive resources like operating rooms.
• RPA Alone: Moves appointment data from a patient portal into the scheduling module of an EHR.
• The Agentic AI Teammate: The agent acts as a care navigator. It gathers symptoms from the patient, checks service availability across multiple sites, applies triage rules, and books the right appointment type. It then triggers pre-visit tasks like lab requests automatically.

KPIs:

• Improved Resource Utilization: Better bed and theatre orchestration based on predicted demand.
• Reduced Patient Wait Times: Faster transition from the patient’s initial request to scheduling their appointment.
• Lower No-Show Rates: Through proactive, intelligent reminders and follow-ups.

3. EHR Data Consolidation and Chart Management (Provider)

• Current Pain: Clinicians suffer from focusing on too many things at once, from bouncing between EHRs to imaging systems and then to pharmacy portals. This fragmentation leads to review times as high as 70 minutes per record.
• RPA Alone: Scrapes data from external portals and pastes it into the EHR.
• The Agentic AI Teammate: A Medical Records Summarization (MRS) agent converts fragmented records into concise, citation-backed summaries. It reconciles medications, identifies duplicates, and highlights renal dosing concerns for clinician review.

KPIs:

• Reduced Summary Review Time: Potential 90% improvement (e.g., from 70 minutes to 6).
• Increased Clinician Face-Time: More time spent on patient care vs. documentation.
• Improved Data Accuracy: Citations and automated reconciliation reduce human error.

4. Member Onboarding and Benefits Navigation (Payer)

• Current Pain: New members struggle to navigate complex benefit structures, leading to a high volume of basic calls to support centers and poor member satisfaction scores.
• RPA Alone: Automates the backend account creation in membership databases.
• The Agentic AI Teammate: A multi-agent “navigator” handles the entire onboarding lifecycle. It verifies identity, explains specific benefits in plain language, schedules initial wellness visits, and updates records directly.

KPIs:

• Reduced Call Center Volume: Handling over 25% of routine service calls through automation (FierceHealthpayer_Abby-Navient-010825-Whitepaper_v3 1.pdf).
• Improved Member Satisfaction (NPS): Faster, more accurate answers to benefit queries.
• Reduced Onboarding Cycle Time: Moving members from “enrolled” to “fully set up” faster.

5. Value-Based Care Reporting (Payer–Provider)

• Current Pain: Identifying gaps in care and reporting on quality metrics is often a retrospective, manual process that happens months after the patient encounter.
• RPA Alone: Pulls monthly reports from various systems and aggregates them into a spreadsheet.
• The Agentic AI Teammate: Agents monitor quality metrics and wearable sensor data in real-time. They locate high-risk patient groups, suggest targeted interventions, and report on performance against value-based contracts as care happens.

KPIs:

• Higher Quality Scores (HEDIS/Stars): Through real-time gap identification and closure.
• Reduced Readmission Rates: Proactive monitoring and intervention for high-risk groups.
• Improved Bonus Capture: Maximizing reimbursements tied to value-based care contracts.

Implementation Playbook: Timelines, Tech Stack, and KPIs

Moving toward a digital workforce calls for a disciplined, phased approach to manage risk and prove value, which starts with these steps:

• Identify: Select a target process that is repetitive, measurable, and contains multiple steps. Focus on a specific “decision point” where an agent can add value.
• Pilot: Launch a small-scale pilot, such as appointment coordination or a subset of claims processing, to measure impact in a controlled environment.
• Scale: Once the bot-agent architecture is proven, expand to more complex, end-to-end workflows.

Then, consider the technical ingredients:

• Orchestrator: The brain that manages the workflow.
• Bots: The hands that execute tasks in legacy systems.
• Agents: The thinkers that handle reasoning and goals.
• Data/Governance: High-quality, organized data and a “human-in-the-loop” framework for oversight.

To show incremental value over existing automation, track three categories of metrics as your KPI framework:

• Operational: Cycle times, straight-through processing rates, and exception rates.
• Financial: Cost per transaction and total ROI, which has demonstrated ROI of up to 10x in targeted, purpose‑built healthcare use cases.
• Experience: Clinician burnout scores and patient/member satisfaction levels.

Making the Case to Your CFO

When presenting this to your CFO, frame Agentic AI not as a one-off IT project, but as the construction of a reusable digital workforce platform. Unlike traditional software that requires constant maintenance for every change, Agentic AI is adaptive and learns over time, reducing long-term “technical debt”.

A concise business case should focus on:

• Scalability: The ability to handle rising volumes without a linear increase in head count.
• Risk Mitigation: Purpose-built, healthcare-specific AI avoids the “hallucinations” of generic models by sticking to your specific business rules and data.
• Speed to Value: Using solutions like Naviant’s Health Claims Accelerator has the potential to get you 80-85% of the way to full automation immediately.

By starting with high-priority pilots and maintaining strict governance, you can manage risk while achieving the efficiency, accuracy, and compliance you’ve been striving for.

Ready to see how purpose-built AI can transform your claims operations? Connect with us to schedule a demo.