RPA and agentic AI are not competing technologies. They are built for different types of work. RPA reliably automates structured, rule-based healthcare processes: eligibility checks, claims scrubbing, payment posting. Agentic AI handles what RPA cannot: prior authorisation workflows, denial management, and any process that requires reasoning across fragmented systems. This article explains how each technology works, where each applies in a healthcare setting, and how to sequence your implementation to avoid the failure patterns that affect 95% of enterprise AI pilots.

Agentic AI vs RPA in healthcare: what is the difference and which should you implement

Most healthcare operations leaders treating RPA healthcare automation and agentic AI in healthcare as competing bets are asking the wrong question. The question is not which technology wins. It is which processes each one is built for, and what happens when you deploy one where the other belongs. Getting this distinction right is the difference between a working system and a costly pilot that goes nowhere.

Why healthcare runs on automation and where most organisations are today

The financial pressure driving automation in healthcare is not marginal. US health systems collectively spend more than $140 billion annually on revenue cycle management, with manual processes, fragmented vendor landscapes, and outdated technologies contributing to high costs, delays, and errors. according to McKinsey’s January 2026 analysis, citing Harris Williams market data. The RCM process alone typically costs three to four percent of a health system’s revenue at scale.

RPA is already deeply embedded in that infrastructure. As far back as 2022, 43% of US health systems were using RPA specifically for claims management, according to Becker’s Healthcare research cited by R1 RCM. But adoption is now at an inflection point. By 2025, more than 30% of providers had prioritised AI and automation for seven specific RCM use cases, compared with four to five the year before. At the same time, Experian Health’s State of Claims 2025 survey of 250 healthcare professionals found that while 67% believe AI can improve the claims process, only 14% are currently using it to reduce denials. Awareness has run ahead of deployment and the gap between them is where most organisations are stuck.

What RPA actually does in a healthcare setting

RPA healthcare automation works by scripting a fixed sequence of actions that a bot executes against existing interfaces; logging into payer portals, extracting data fields, populating forms, and moving records between systems. It does not reason. It does not adapt. It follows rules exactly as written, every time.

This makes RPA highly effective for processes that share three characteristics: structured data formats, stable system interfaces, and no requirement for contextual judgement. In healthcare, those processes include eligibility verification, standard claims scrubbing before submission, payment posting, appointment reminder dispatch, and basic prior authorisation form submission where payer requirements are fixed and well-defined.

The documented results in these areas are significant. One large national healthcare provider reduced payment posting time from eight hours to 45 minutes daily after RPA implementation, according to Valere Health. CareSource, a non-profit serving over two million members, achieved 90% invoice automation and a 50% reduction in manual work after deploying UiPath for claims, utilisation management, and prior authorisation. These are not hypothetical results. When the process fits, RPA delivers.

Where RPA breaks down

The brittleness is predictable. When a payer changes a portal layout, a form field label, or a file format, the bot stops. It does not recognise the change, does not adapt, and requires a developer to rebuild the affected workflow. In a healthcare environment where payer requirements shift frequently, this maintenance burden accumulates quickly.

More fundamentally, RPA cannot reason. It cannot read a clinical note to assess whether a prior authorisation request is medically appropriate. It cannot interpret a denial to identify its root cause. It cannot navigate a non-standard claim or make a judgement call when data is missing. The moment a process requires any of those capabilities, RPA is the wrong tool. Not a limited one, but the wrong one.

“RPA isn’t dying — it’s evolving. When you need something to work the same way every single time — without exceptions, without interpretations — RPA remains unmatched.”
— Chris Radich, Public Sector CTO at UiPath

The corollary is equally direct: when a process does require exceptions and interpretation, RPA will fail at exactly the moments that matter most (CIO.com, June 2025).

There is also a structural ceiling emerging from regulation. TEFCA and CMS mandates now require actionable, traceable information flows across fragmented healthcare systems, capabilities that rule-based automation cannot deliver. Interoperability is no longer optional, and it requires more than bots that copy data between screens.

What agentic AI does differently

Agentic AI in healthcare is not a faster version of RPA. It is a different architecture. At its core is a reasoning engine, typically a large language model, connected to tools, memory, and an orchestration layer. Instead of executing a fixed script, an agentic system pursues a defined outcome by planning a sequence of actions, adapting when it encounters unexpected inputs, and maintaining context across multiple steps and data sources.

Prior authorisation illustrates the difference most clearly. Today, this process is handled manually: staff navigate multiple payer portals, interpret payer-specific documentation requirements, gather clinical records from the EHR, submit the request, and then monitor its status, often across weeks, with follow-up calls and resubmissions when documentation is rejected. According to the AMA’s 2024 Prior Authorisation Physician Survey (conducted December 2024, released February 2025), physicians complete an average of 39 prior authorisations per week, spending approximately 13 hours on the process. 93% report it delays patient care. 31% say requests are often or always denied, many due to documentation gaps that a well-designed agent would have caught before submission.

An agentic system handles this end to end: it reads the clinical note, identifies the payer’s criteria, surfaces any missing documentation, submits through the portal, monitors status, and, on denial, pulls the relevant records, builds the appeal, and routes it for human review. No staff member clicking through six screens. No queue waiting for someone to notice a rejection.

We built a system of this kind for a US healthcare provider serving more than 100,000 members across multiple states. By deploying a multi-channel, pre-appointment AI agent, each doctor now saves more than five hours per week, while patient engagement increased over 20% through personalised, accessible communication. The full case study is available at vstorm.co.

The broader data is consistent with this pattern. One California healthcare network deployed AI-powered claims review and achieved a 22% decrease in prior authorisation denials by commercial payers and an 18% decrease in denials for services not covered, without adding RCM staff, and saving an estimated 30 to 35 hours per week in back-end appeals work, according to the AHA and Ailevate, 2025.

The compliance question: What HIPAA means for agentic AI in healthcare

Both RPA and agentic AI touch protected health information, and HIPAA applies to both. The compliance architecture, however, is more complex for agentic systems and the regulatory bar is rising.

On January 6, 2025, HHS Office for Civil Rights proposed the first major update to the HIPAA Security Rule in 20 years, removing the distinction between “required” and “addressable” safeguards and mandating full encryption of all electronic PHI in storage and transit. Breach notification timelines would shorten from 60 days to 30 days, and continuous monitoring would become a formal requirement. As of publication, this update remains a proposed rule (HIPAA Journal).

For agentic AI specifically, the compliance requirements go further. A peer-reviewed framework published by researchers at Mississippi State University in April 2025 identifies three non-negotiable controls for HIPAA-compliant agentic systems: attribute-based access control (ABAC) for granular PHI governance, a hybrid PHI sanitisation pipeline to prevent data leakage, and immutable audit trails for compliance verification. Additionally, any third-party API infrastructure that processes PHI, including the LLM provider, requires a valid Business Associate Agreement (Neupane et al., arXiv, April 2025).

The vendor complexity problem is real. A 2025 report by Ponemon Institute and Imprivata found that nearly half of surveyed health IT leaders experienced a data breach or cyberattack involving third-party network access in the prior year. As one healthcare security expert put it in TechTarget: with agentic AI, one of the most complex compliance challenges is the sheer number of different business associates that may be involved, PHI continues to be PHI as it flows through each system unless properly de-identified at every stage.

Practically, this means on-premise deployment or rigorously governed cloud architecture with full data lineage visibility is not optional in a healthcare context. It is a foundational design requirement, not an afterthought. For organisations we work with through our healthcare agentic AI practice, governance and compliance are scoped before engineering begins, not bolted on after.

RPA vs agentic AI: which process fits which technology

The table below maps the most common healthcare administrative processes against RPA fit and agentic AI fit. The logic is process-driven, not technology-driven: start from the nature of the work, not from a vendor’s capabilities.

Which should you implement: a decision framework

This is not a binary choice. The practical question for most healthcare operations teams is one of sequencing: where does each technology apply, and in what order should you build?

Start with RPA where processes are fully standardised, interfaces are stable, data formats are predictable, and no contextual judgement is required. Eligibility verification and payment posting are the clearest examples. These processes deliver strong ROI quickly, and they build the automation maturity; clean data, integrated systems, staff buy-in; that agentic AI depends on later.

Move to agentic AI where processes involve unstructured data, multi-system coordination, frequent exceptions, or decisions that require context. Prior authorisation and denial management are the highest-value entry points for most healthcare organisations. McKinsey’s January 2026 analysis recommends starting with back-end RCM; accounts receivable follow-up, underpayment management, denial resolution; as the lowest-risk zone for initial agentic AI deployment, because the work is rules-governed in structure but variable in content.

The hybrid model, RPA executing the deterministic core, agentic AI handling exceptions and cross-system orchestration, is where most mature healthcare organisations are heading. As Blue Prism’s 2026 analysis puts it: the sweet spot is hybrid automation, let AI handle the unpredictable parts and keep RPA for the reliable core processes.

What determines whether this works is readiness, not ambition. Agentic AI requires clean data infrastructure, integration access, governance frameworks, and a clear definition of success before the first agent goes live. Organisations that skip this assessment face a well-documented failure pattern. According to the MIT Media Lab’s Project NANDA report, “The GenAI Divide: State of AI in Business 2025” (July 2025), 95% of enterprise AI pilots deliver no measurable P&L impact, based on analysis of 300 deployments across industries. The biggest ROI, the report finds, consistently comes from back-office automation where AI is given the right integration and context to do its job (MIT NANDA, July 2025).

What a production-grade implementation looks like

The gap between a working agentic system and a stalled pilot is not the quality of the underlying model. It is the quality of the implementation decision that precedes it.

Organisations that move furthest with healthcare workflow automation share a consistent pattern: they start narrow; one high-volume, high-pain process; and build human oversight into the workflow from day one. They instrument the system so every agent decision is traceable and auditable, which matters both for HIPAA compliance and for the ongoing tuning that makes agentic systems improve over time. And they structure the engagement so their internal team gains the capability to maintain and extend the system, rather than creating a dependency.

This is the logic behind our TriStorm methodology: Transformation Consulting identifies where agentic AI creates the highest operational leverage; Technology Consulting translates that into an architecture blueprint; Agentic AI Engineering builds and deploys the production system with observability built in from the start. No handoff gaps. No discovery that the use case was not viable after three months of development.

In healthcare, that process almost always starts with prior authorisation or denial management, the highest-volume, highest-pain points in most revenue cycle environments, and the processes where the case for agentic AI is clearest and fastest to prove.

Key takeaways

RPA and agentic AI solve different problems. RPA belongs on structured, stable, high-volume processes where the rules never change. Agentic AI in healthcare belongs on processes that require reasoning, multi-system coordination, and the ability to handle exceptions, such as prior authorisation, denial management, and clinical documentation are the clearest entry points. The organisations seeing the strongest results are not choosing between the two: they are using RPA to automate the deterministic core and agentic AI to handle everything the bots cannot. The sequencing matters, the governance matters, and the choice of where to start matters more than the technology itself.