Payer Behavior Is Now the Top Barrier to RCM Growth. Can Your Platform Keep Up?

Adonis recently published their Inside RCM 2026 research report, surveying RCM leaders on the biggest threats to revenue performance. The findings mark a clear shift.

What the data says

In 2025, RCM leaders identified three factors most impacting their ability to grow and collect revenue:

1. Frequent changes to payer adjudication rules
2. Denial volume and complexity
3. Payer reimbursement levels and contract terms

For 2026, leaders predict the biggest obstacles to revenue growth will be:

1. Denial and underpayment management
2. Payer reimbursement pressure
3. Staffing constraints

Adonis' conclusion is direct: "Payer behavior has overtaken internal constraints as the top barrier to growth." This is a departure from prior years, where internal optimization was viewed as the primary lever for improvement. In 2026, revenue performance is increasingly dictated by external forces rather than patient volume or internal capacity.

The visibility problem

The report also surfaces a deeper issue. Persistent gaps in real-time insight are driving reactive operating models. Revenue risk gets identified only after claims are denied or cash is delayed. Denials, once viewed as a back-office concern, have escalated into a strategic financial issue discussed at the executive level because of their direct impact on margin and cash flow.

Adonis frames this as "reactive RCM," and they're clear about the cost: organizations that can't see payer behavior in real time are absorbing both operational strain and financial exposure.

What this means for RCM platform companies

If you build the software that RCM teams depend on, this report is a demand signal. Your buyers are describing, in specific terms, the gap between what they need and what they're getting:

• Real-time visibility into payer behavior and denial patterns
• Faster adaptation to shifting adjudication rules
• Predictability instead of reaction

The market isn't asking for incremental improvement. Adonis puts it plainly: "The difference between average and high-performing RCM teams will be defined by predictability and leverage. Organizations that can identify revenue risk earlier, reduce payer-driven surprises, and scale through technology rather than headcount will be better positioned to navigate ongoing reimbursement pressure."

The question for platform companies is whether your engineering organization can ship these capabilities fast enough to matter.

What "fast enough" actually means

When payer rules shift and the platform can't keep pace, the downstream effect on your customers is concrete: claims get denied, rework piles up, cash collection slows down, and some revenue gets written off entirely. The longer the gap between a payer change and a platform update, the more your customers absorb in operational cost and lost revenue.

A decade of research from Google's DORA program confirms that the speed and stability with which a company delivers software predict financial performance, customer satisfaction, and overall valuation. In RCM, that connection is direct. Your customers' financial outcomes depend in part on how quickly your platform adapts to a changing payer environment.

Two different AI conversations

Many RCM platform companies are investing in AI capabilities within their product: denial prediction, payer behavior analysis, real-time visibility. That's exactly what the Adonis report suggests the market needs. Building those features well is a significant engineering challenge, and the organizations that get there first will have a real advantage.

But there's a separate question about the engineering process itself. Many teams are also adopting AI coding tools to try to build software faster. On that front, the evidence is more cautious. DORA's 2025 research found AI adoption at 90% across engineering teams while platform stability declined. A controlled study from METR found experienced developers using AI tools were 19% slower on real tasks, despite believing they were faster.

These are two different conversations. AI in the product may be exactly the right bet. But AI in the development process doesn't automatically translate into shipping faster, especially if the underlying codebase and architecture carry years of accumulated complexity.

The real gap

This isn't a strategy problem. Most RCM software leaders know where their market is heading. The gap is years of accumulated complexity in the platform that makes every new capability harder and slower to ship than the last. And these are the kinds of problems that are nearly impossible to diagnose from the inside, when the team responsible for fixing the foundation is the same team expected to keep delivering on top of it.

Your buyers just told you what 2026 looks like for them. The question is whether your platform is ready for it.