Comparing AI-native, AI add-on, and traditional RCM

A comparative look at medical billing efficiency and claim denial management with AI-native workflows

Revenue cycle management (RCM) has always been complex, but the stakes today are higher than ever. Average claim denial rates remain stubbornly above 10%¹ in most practices, payer rules shift constantly, and staff time is stretched thin by manual work. Many organizations are evaluating whether to stay with traditional billing systems, adopt a partial AI tool, or move to a fully AI-native platform that learns and adapts in real time.

The real differences between RCM models show up in how they handle the fundamentals: architecture, automation, denial prevention, and financial outcomes. Traditional and partial AI systems still leave staff chasing claims and reworking denials, while native AI in athenaOne® changes the RCM equation — streamlining processes end to end and giving practices the consistency and sustainability they need.

Let’s break down six fundamental factors in revenue cycle operations and look at how they work on AI-native intelligent systems, traditional RCM processes, and partial-AI legacy systems with added features. The differences may surprise you.

1. System architecture and intelligence

Traditional RCM platforms are often hosted or on-premise, customized site by site. Updates are sporadic, and every practice runs its own version. Partial AI vendors may offer a multi-tenant cloud, but customer environments are siloed, meaning their AI learns only from local data.

athenaOne is built differently. As a single-instance SaaS platform, all customers are on one system, continuously improving together. Every rule update, clean claim, and denial resolution strengthens the platform for everyone in real time. That scale gives native AI in athenaOne broader visibility, faster learning, and more reliable performance.

2. Depth of automation

Legacy systems remain heavily manual, with staff coding, submitting, and following up on claims – that can result in cascading rejections, denials, and appeals. Partial AI vendors may automate discrete steps like eligibility verification or EOB processing, but workflows still break down when exceptions or denials occur.

athenaOne automates steps throughout the entire revenue cycle: from intake and coding to claim submission and follow-up. AI can take on routine revenue cycle tasks like checking payer requirements, fixing and resubmitting claims, spotting payment variances, or confirming when prior authorization is needed, so staff can focus on patient care instead of paperwork. The result is fewer errors, less rework, and faster cash flow.

3. Denial prevention vs denial management

Traditional systems accept denials as part of the process. Staff rework claims manually or outsource denial management to third parties if they don’t have the staff. Partial AI solutions may predict some denials pre-submission, but they lack visibility across a wide payer network. With less data to work from, these systems only know what they know, and can’t see the bigger picture.

At athenahealth, our goal is to prevent claim denials entirely, and the 29K+ rules in our intelligent system are helping practices drive denials down. AI monitors public and private payer policies and historical patterns, adjusting claim rules continuously. Customers experience a 5.7% median denial rate² compared to a 10-18% industry average — nearly half as many denials to resolve – and our plan is to bring that number as close to zero as possible.

4. Documentation accuracy and coding

In traditional systems, coding is manual and error-prone, leading to underpayments or delayed reimbursement. Claims are paid months after the patient visit, making cashflow lean or unsteady. Partial AI RCM tools may offer voice-to-text or billing suggestions after the visit, but these often require significant staff editing and manual work.

AI-native athenaOne levels up our already industry leading comprehensive documentation services and claims processing services, systematically moving from the clinical record to the reimbursement. The platform will help transform clinical conversations into billable claims in real time, with AI nudges to verify completeness and accuracy. This approach will not only help reduce errors that cause downstream denials, it will also help providers leave the office on time and practices collect greater payer yields.

5. Network effect and scale

Traditional and partial AI systems often operate in isolation, taking information from a single organization or healthcare system. Any improvements — new payer rules, denial workarounds — must be created and maintained locally, and often by the healthcare organization itself.

With athenaOne, every payer interaction strengthens the platform for all. The network processes over 315 million claims per year and supports more than 160,000 providers. That network effect allows athenaOne to adapt faster to payer changes and deliver consistent results across specialties and practice sizes.

The results from these efficiencies are real: using AI-powered workflows has led to a 35% reduction in insurance-related claim holds³ and a 12.8% reduction in insurance-related denials⁴ – with greater efficiencies expected as more AI workflows are added to the Advanced Intelligence Layer and the AI continues to learn from the claims processed across our network.

6. Contract intelligence and missed revenue

Traditional RCM systems rely on manual contract reviews or delayed analytics. Partial AI vendors may surface trends, but they are rarely integrated into real-time workflows.

athenaOne applies AI directly to remits and contracts, flagging underpayments and outdated fee schedules automatically. This proactive contract intelligence helps practices capture revenue they are owed — without adding manual work. Implemented first for Medicare payments – the largest nationwide insurer – more fee schedules are being added to avoid lesser-of penalties.

These metrics represent more than technical efficiency. They translate into fewer administrative hours, stronger margins, and greater sustainability for practices.

athenaOne automates steps throughout the entire revenue cycle: from intake and coding to claim submission, adjudication, and follow-up.

Why AI-native RCM matters in today’s market

At first glance, the difference between traditional RCM, partial AI add-ons, and a fully AI-native platform may look like degrees of automation. In reality, the gap is much larger: it’s the difference between solving yesterday’s problems one claim at a time and building a system that continuously adapts to tomorrow’s challenges.

• Traditional RCM keeps staff stuck in reactive cycles of denial management, rework, and manual payer follow-up.
• Partial AI RCM can automate some steps, but because learning is siloed, results vary widely and problems resurface.
• AI-native RCM changes the equation. Because athenaOne is built on a single, shared-intelligence platform, every payer rule update and denial resolution improves the system for all customers immediately. That creates consistency, scale, and measurable outcomes that stand up year after year.

This is why athenaOne customers see nearly half as many denials as the industry average, a 78% median patient pay yield,⁵ and billions collected annually, and why we've been recognized for 19 years in Best in KLAS.

From incremental gains to strategic advantage

Traditional RCM keeps organizations in firefighting mode. Partial AI tools may reduce a few clicks, but they don’t address the hardest problems or create durable results. Only athenaOne, as a true AI-native platform, automates the revenue cycle end to end, prevents denials upstream, and continuously learns from the largest connected network in healthcare.

The outcome is a revenue cycle that no longer drains staff and margins but powers long-term growth. In today’s environment, the real question isn’t whether to use AI in billing — it’s whether your system is truly AI-native and built for what comes next.