How the AI conversation in 2026 signals a shift in revenue cycle strategy

Healthcare is at a turning point in how it approaches AI. Over the past two years we’ve moved from experimenting with generative capabilities to scaling AI across clinical and administrative domains. As we look ahead to 2026, the conversation isn’t just about “Can AI help,” it’s about how deeply AI must be embedded into core workflows to prevent revenue leakage and protect margin. A recent Becker’s Hospital Review piece on 2026 predictions highlights trends that should be top of mind for every revenue cycle leader.  
 

From point solutions to integrated AI platforms 

One of the most important shifts is away from isolated point solutions toward integrated AI platforms that support multiple use cases across the care continuum. For revenue cycle teams, this means going beyond standalone tools for coding, CDI or denials review. Instead, unified systems that link clinical documentation, coding logic, and payer rules reduce friction and help teams get claims right the first time, preventing downstream denials before they occur.
 

AI’s role in reducing denials and revenue leakage 

Prediction number six from Becker’s hits this point squarely: AI will increase revenue cycle efficiency by correcting “thrash” upstream. Denials have long been one of healthcare’s most persistent forms of waste. Too often organizations wait for claims to be denied and then invest scarce resources in appeals and rework. But AI can change that pattern by generating more defensible coding, aligning clinical documentation with payer expectations and resolving prior authorization hurdles before they derail reimbursement. In our own engagements, machine learning models trained on historical claims help revenue cycle teams identify high-risk cases early, flag them for intervention and ultimately reduce avoidable denials.
 

The rise of context-aware AI in revenue cycle decisions 

The seventh prediction introduces another layer of nuance: The rise of the “context layer” of AI that blends EHR data, payer requirements, system knowledge and real-time reasoning. Context matters in revenue cycle decisions. When AI can interpret documentation in light of clinical context and payer logic, it can surface potential gaps before claims are submitted, prevent revenue leakage and give teams actionable insights rather than raw alerts. This reduces administrative burden and supports better financial performance. 

Finally, prediction eight underscores the importance of transparency in AI: Clinicians and revenue cycle teams will no longer accept opaque “black box” systems. They want to understand why a recommendation was made, what data it’s based on and how confident the system is in its output. It’s important to build in that transparency so users can trust the intelligence they rely on every day. 
 

AI as infrastructure: Preparing for 2026 

2026 won’t be about AI as a novelty, it will be about AI as infrastructure. For revenue cycle leaders that means embracing integrated, context-aware, transparent AI to prevent denials, close revenue leakage and protect margin. The hospitals and health systems that make that shift will be better positioned to navigate payer complexity, improve financial performance and spend more time on the work that matters most. 

Josh Amrhein is a business manager for revenue integrity at Solventum.