Healthcare organizations are expanding their use of AI while governance structures continue to develop, according to 2026 industry research. That gap is influencing how organizations evaluate AI-powered revenue cycle management software, particularly tools used for coding, claims editing, and denial prevention. As AI medical coding, claims editing, and denial-prediction tools take on a growing share of the billing workflow, explainability has moved from a nice-to-have feature to a core requirement for compliance and audit readiness.
The scale of the gap is notable. Black Book’s 2026 comparative study of AI-powered claims automation analyzed feedback from 9,308 validated users across 7,744 physician practices, groups, and healthcare delivery networks. The report also cited separate 2026 industry research showing that more than 80% of physician respondents use AI professionally and that 68% of medical groups added or expanded AI use during 2025. At the same time, only 42% of medical group leaders reported having an AI governance policy or formal AI-use policy in place or under development.
For revenue cycle management software specifically, the stakes are higher than in many other AI use cases, because the outputs directly affect what gets billed to patients and payers. A recommendation for medical codes generated by an artificial intelligence algorithm that has no connection to the documentation behind such codes could pose further risks, especially in case an insurance company’s audit asks to prove the rationale behind choosing a certain code.
According to RapidClaims, this new approach has directly impacted their decision on creating their platform. RapidClaims claims that its AI-based medical coding algorithm employs evidence-based coding and produces auditable outcomes, which means that medical code recommendations are linked to supporting clinical documentation. The human-in-the-loop technology is supposed to draw attention to cases where more information needs to be considered by humans.
Black Book’s 2026 findings suggest that the evaluation of revenue cycle automation is extending beyond processing speed to areas such as claims accuracy, denial reduction, system integration, compliance readiness, AI accuracy, and measurable ROI. Vendors that can’t clearly explain how an AI-generated code or claim edit was reached are increasingly at a disadvantage with health system compliance and revenue cycle leadership, regardless of how accurate the underlying model may be.
The governance gap also has practical implications for how quickly organizations can scale their use of AI medical coding. Black Book’s research also highlights a gap between perceived potential and current adoption: 14% of provider organizations reported actively using AI to reduce denials, while 67% believed AI could improve the claims process. The findings suggest that organizations are still evaluating how AI can be deployed effectively within existing operational, compliance, and oversight frameworks.
As regulatory and governance expectations around AI continue to evolve, RapidClaims expects explainability to become an increasingly important factor in how health systems evaluate revenue cycle management platforms. The company says it will continue investing in capabilities that make AI-generated coding and claims logic more auditable by design. This approach aligns with a broader industry focus on traceability, human oversight, compliance readiness, and measurable performance.
The implications extend beyond any single vendor. As health systems formalize AI governance and procurement processes, AI RCM software that can demonstrate a clear, documented basis for their outputs may have an advantage during technology evaluation. For a category that has historically emphasized automation, speed, and accuracy, greater attention to explainability, auditability, human oversight, and governance could become an increasingly important part of how RCM software is evaluated through 2026 and beyond.
