As denial rates rise, margins thin, and operating costs continue to put pressure on providers, more than 60% of healthcare tech executives are implementing agentic AI or have secured the budget to do so — and 85% plan to increase investment. 

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But the gains for many are incremental and uneven, especially for AI in revenue cycle management (RCM). Many RCM teams struggle to point to measurable financial impacts from their AI investments.

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One of the key reasons for that is the category of AI being prioritized and implemented. Assistive AI responds to prompts, supports discrete tasks, and requires humans to initiate and advance each step. By contrast, agentic AI can plan and sequence tasks, adapt to changing conditions, and enable true transformation of healthcare operations. 

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Here’s what agentic AI actually means for RCM, how it enhances staff capabilities, and where multi-agent RCM systems are headed.

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What is Agentic AI?

Agentic AI can plan and sequence multi-step tasks, adapt to changing conditions, gather relevant information, and coordinate with other systems. Rather than waiting for a specific prompt, agentic AI systems can pursue goals across multiple steps, use different tools, integrate with different data sources, and adjust their approach based on what they encounter.

For comparison, traditional AI uses rules-based automation and robotic process automation (RPA) to execute predefined tasks. Assistive tools perform instructions exactly as programmed, handling structured, repeatable processes with speed and consistency, but require significant human intervention to complete a multi-step project. 

‍Generative AI introduced the ability to create content, respond to natural-language prompts, and support discrete decision-making. It can draft appeal letters, summarize clinical notes, or answer basic questions, but it still requires a human to initiate and direct each interaction.

Agentic AI is a major shift. With multiple AI agents working together — and humans in the loop at key decision points — systems can plan, execute, and adapt across multi-step workflows. Humans govern the process and review the results rather than performing every task (or validating every result). 

Why Agentic AI for Healthcare RCM?

There is massive potential for tech to streamline and improve healthcare administration across the board; healthcare IT represents less than 10% of the $740B spent on healthcare administration per year. 

RCM, specifically, is well-suited for agentic AI because: 

• RCM workflows match the strengths of agentic AI. RCM involves multi-step, high-volume workflows that require coordination with multiple systems. An effective RCM system connects to relevant entities and data sources: clinical, financial, and payer – in real time. Agentic AI systems can execute multi-step, multi-system tasks better than any technology on the market.
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• RCM work doesn’t run on a clean, predictable path. Every payer has its own portal. Every plan has its own quirks. Rules-based automation is fast on the cases you can script and useless on the cases you can't. In RCM, the exceptions are the majority. With the ability to reason and execute without human intervention, agentic AI is the first technology built for this long tail. It doesn't follow a fixed sequence of steps. It pursues an outcome — and adapts when the situation in front of it doesn't match the playbook.
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• RCM drives hard-dollar value. Roughly half of healthcare providers say RCM is a top-three priority because of its tangible ROI: higher collection rates, more recovered revenue, lower operational costs, and healthier cash flow. The immediate impact on operating margins makes RCM an important opportunity.

The natural fit between RCM and AI plays out in the larger market: nearly half of hospitals already use AI for RCM, and almost two-thirds use revenue cycle automation in some form. Some of the long-standing barriers to AI adoption have eased, leaders say, and major health systems are making significant long-term investments in AI technologies for healthcare operations. 

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How Agentic AI Changes, Not Removes, the Human Role

One of the most common concerns about agentic AI in healthcare is that autonomous systems will make decisions without adequate human oversight – a legitimate concern. 

A human in the loop is a critical safeguard for ethical, compliant workflows. There are key steps in the revenue cycle management process that require human input and decision-making, and an AI system cannot replicate the knowledge and expertise of staff who have interacted with the patient base for years. 

‍Agentic AI should restructure and enhance, not eliminate, the human role in the process. 

The goal is to elevate staff from task execution, where they perform every step in a workflow, to oversight and exception handling, where they govern the system and intervene when the situation requires human judgment. 

When implemented thoughtfully, agentic AI systems take on high-volume, repetitive work, to relieve the burden on overworked staff. Staff decision-making and expertise is essential for critical decisions and providing care to patients, and staff can make more time for these critical workflows with an agentic AI system handling more routine processing work. 

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Where Humans Belong in an Agentic System

For a successful agentic AI implementation, humans should set the rules, define the boundaries, and handle the cases that fall outside them.

Consider a few elements of the multi-agent system:

• Orchestration agents that supervise and direct the overall workflow
• Task agents that execute specific functions
• Review agents that check outputs for accuracy and compliance
• Planning agents that anticipate future scenarios and flag risks

In this model, the human role is to govern the orchestration layer, setting the rules and boundaries that other agents will operate within. When review agents flag discrepancies, humans review the exceptions and make decisions (or get a second opinion). When planning agents flag risks or recommend adjustments for the future, those are raised for a human decision-maker to consider, decide, discuss with other stakeholders, and respond. 

For instance, an agentic AI system can review claim submissions at scale and flag outliers for staff review. Another system can review patients with high balances, calculate payment risk, and identify ideal candidates for flexible payment plans. Meanwhile, a multi-agent system can review complex payer contracts and provider networks to highlight items of note for human review. Each of these workflows will result in human review and input, but the initial information-gathering and first-pass review happens much faster than it would otherwise.

What This Looks Like in Patient Billing

Consider a typical patient billing workflow. Staff (1) manually review each balance, (2) generate a statement, (3) respond to a patient inquiry, (4) process a payment, and (5) post the transaction to the EHR—five discrete steps that require human initiation and execution. 
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With agentic AI, the system sequences those steps autonomously. It verifies the balance, personalizes the communication, delivers it through the patient’s preferred channel, resolves the inquiry, processes the payment, and posts the result back to the EHR. A human intervenes only when the patient’s inquiry is complex, the balance is disputed, or the payment routing requires judgment. The staff member moves from executing five routine steps to overseeing one or two exception cases.

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Multi-Agent Systems and End-to-End Orchestration

Most organizations deploying AI in RCM today are working with individual agents: a tool that generates appeal letters, a bot that scrubs claims, or a system that automates eligibility checks. These single-purpose agents can deliver some value, but they represent just an early phase of what is possible. 

The problem with partial automation

AI that requires constant manual review is not a real solution. It creates new, but still frustrating, types of manual work. 

Example: AI systems that lack contextual understanding flag countless records for human review because they can’t distinguish between minor variations in the data and significant operational issues. 

This creates “hidden rework factories” where staff spend time validating AI suggestions rather than higher-value work.

Read more → The real test for AI in RCM is workflow completion

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The next phase involves multi-agent systems where specialized agents coordinate to handle entire workflows, passing information between each other, validating outputs, and escalating to humans only when necessary.

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How Multi-Agent Coordination Works in Practice

After a patient visit:

• Agent 1 verifies insurance details
• Agent 2 assigns ICD and CPT codes
• Agent 3 reviews the claim against payer-specific compliance rules
• Agent 4 compiles the complete claim package for clinician review

Once the provider receives the 835 form from a payer:

• Agent 1 reviews the 835 form for discrepancies, errors, underpayments
• Agent 2 reviews the results to rule out common mistakes
• Agent 3 drafts an appeal to address the error or underpayment
• Agent 4 compiles the response package for human review

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Like any new technology, multi-agent systems have enthusiastic early adopters and those who are waiting for stronger evidence before investing. So far, the early adopters are seeking to transform end-to-end processes, while the more cautious movers are leaning toward point solutions that address specific bottlenecks or limited use cases. 

“Early adopters appear to be forming a reinforcing cycle: greater readiness enables multi-agent bets, which increase confidence in larger savings, which justifies deeper process redesign and faster learning. The competitive implication is that agentic AI could widen performance gaps, with early adopters positioned to capture productivity gains and redeploy them into resilience, experience, and growth, while watchers risk arriving later with a lower return ceiling.” 
- Deloitte research, published February 2026‍

Many providers are trying the EHR + 1 technology platform strategy, in which providers supplement their EHR capabilities with specialized AI tools. In these cases, other AI tools offer a strategic advantage that the EHR’s built-in functionality simply can’t match. With this approach, the EHR remains the central technology foundation, offering consistency for clinicians, while augmenting the system with new AI functionality. 

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A Multi-Agent Patient Billing Scenario

For patient billing specifically, a multi-agent system can help coordinate across the entire patient financial journey:

1. An eligibility agent verifies coverage and estimates patient responsibility
2. A communication agent generates and delivers personalized billing communications
3. An inquiry agent handles inbound patient questions across chat, SMS, email, and voice
4. A payment agent processes transactions and applies them to balances
5. An integration agent posts all activity back to the EHR in near real-time
6. An orchestration agent sequences the workflow, monitors for exceptions, and escalates edge cases to staff

Each agent is specialized, but the patient experiences a seamless financial journey from pre-visit through payment, and the RCM team focuses on the cases that genuinely require human judgment. 

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What to Look for in an Agentic AI RCM Solution

For healthcare leaders evaluating agentic AI solutions, consider these criteria to find the solutions that deliver lasting value: 

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Clinical and Contextual Grounding

• Does the AI understand the clinical context behind billing decisions? Or is it replicating statistical patterns from historical claims data? This is a core issue for many AI implementations: if it is built solely on historical claims data, it learns to replicate past coding and billing decisions, including erroneous ones.
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• Does the AI understand the patient’s specific financial situation, including balance composition, insurance status, and payment history? Or does it apply generic rules to every interaction? Contextual grounding is what separates a system that can resolve a billing inquiry from one that simply escalates it.

True Autonomy With Defined Oversight Checkpoints‍

• Can the system execute multi-step workflows end-to-end? Or does it require human initiation at every step? 
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• Where are the human checkpoints placed? If oversight is required at every step, the system is not truly agentic. If oversight is strategically placed at high-stakes decision points aligned with compliance needs and operational goals, that reflects mature agentic design.
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• Can you configure the level of autonomy, both now and in the future? Ideal systems allow configurable levels of autonomy, so organizations can start out with more human involvement and thoughtfully expand as confidence builds.

Bi-Directional System Integration

• ‍Can the AI read from and write to your core systems in real time? (EHR, practice management, billing platforms, etc.) True bi-directional integration, where the AI reads patient data, executes actions, and writes results back to the source system, is the baseline for workflow completion.

Multi-Agent Orchestration Capability

• ‍Is the solution a single-purpose agent or part of a coordinated system? Even for providers that start with a focused implementation, needs will evolve over time. Flexibility and interoperability are crucial; it’s important to explore how this platform can expand to orchestrate additional processes or integrate cleanly into a multi-agent ecosystem. 

Measurable Outcomes Tied to Financial Performance

• ‍How will expected impact/ROI be measured? The right AI solution should articulate expected impact in terms of the metrics that matter to revenue cycle performance (days in A/R, collection rates, cost to collect, etc.) and the patient financial journey (patient satisfaction, patient retention, etc.)
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• How specific are the ROI projections? Vague promises of “time saved” or “efficiency gains” are insufficient. The organizations seeing the greatest returns from AI are those that identify specific financial KPI goals and hold themselves (and their technology partners) accountable to delivering against those goals. 

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AI-Powered RCM for Healthcare

Modern healthcare organizations are facing significant financial pressures, and as margins tighten, agentic AI is a strategic investment for RCM. 

Collectly is the AI-powered all-in-one RCM platform, automating patient financial workflows from eligibility through final payment. 

Trusted by 3,000+ medical facilities nationwide, Collectly delivers real-dollar ROI. Providers using Collectly:

• saved 66% on costs to collect
• reduced DSO to 12.6 days
• boosted patient collections by 2-3x
• reduced administrative burden from patient support by 80-85%

With bidirectional EHR integration and AI agents built for billing and RCM, Collectly has enabled providers to collect over $1 billion in patient payments to date. 

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Ready to see how Collectly applies agentic AI to patient billing? Request a demo today.