AI in healthcare has reached an odd moment. Leaders believe in it, budgets point toward it, and the pressure to improve financial performance keeps rising. Yet adoption of AI in healthcare continues to lag, especially for revenue cycle management. It’s a contradiction two healthcare leaders unpacked in a recent conversation.
Why AI Adoption Lags in Healthcare Revenue Cycle
During a recent conversation with Jason Considine, President of Experian Health, and Sharlene Seidman, Senior Vice President of Revenue Cycle at Yale New Haven Health, Healthcare IT Today explored why enthusiasm for AI remains high while deployment remains low. Their perspective highlights the trust gaps, operational realities, and workforce questions that both slow as well as motivate AI adoption.
Key Takeaways
- AI adoption in healthcare is about trust. Most healthcare leaders already see AI’s potential, but adoption still lags because risk-averse organizations need proof, not hype. Building trust means showing measurable outcomes first.
- Automating work is not the same as replacing people. Both leaders stressed that the goal of AI is to remove friction, not headcount. Success depends on helping staff feel confident using new tools and seeing AI as an ally.
- Getting payer data right up front pays off downstream. The biggest RCM efficiency gains come from clean data and fewer re-touches. AI can now spot and correct errors at registration before they derail reimbursement or patient experience.
Healthcare’s AI Problem Is a Trust Problem
AI awareness is no longer the barrier for health systems. The challenge is confidence.
Considine described an industry that sees the upside clearly yet hesitates without concrete outcomes.
“Most organizations are aware of AI and its potential application,” said Considine. “But less than 15% have actually adopted AI in some way. That speaks to the opportunity for provider organizations, because the promise of this technology to drive efficiency and better patient outcomes is substantial.”
Experian’s latest 2025 State of Claims Report found that 67% of healthcare Revenue Cycle Management (RCM) leaders believe AI can improve the claims process, yet only 14% use it to help reduce denials. The reasons given for the low adoption include:
- Unproven accuracy
- Unproven HIPAA compliance
- Daunting training for teams
- Skepticism about whether AI can interpret payer-specific rules
Taken together, these concerns signal the lack of trust in AI’s readiness for RCM, especially handling claims. Building that trust starts with clarity – clarity on what the technology can and cannot do, and clarity of how it affects staff.
AI Adoption Depends on Culture More Than Code
Seidman spoke candidly about staff fear of AI and why acknowledging it matters: “We don’t want them to be afraid of what it’s going to do, if it’s going to eliminate jobs. We want them to embrace it and then create a culture where we’re constantly willing to innovate.”
Considine added: “It’s not about eliminating jobs, it’s eliminating some work… That’s the cultural change we as leaders have to drive to make people less fearful about adopting AI and leaning in.”
Both leaders emphasized that showing staff how AI removes non-valued tasks is key. With many RCM roles going unfilled, organizations are asking smaller teams to carry heavier workloads. AI helps relieve that pressure by handling the tasks that take time but not expertise.
Clean Intake Data Still Determines the Entire Revenue Cycle
RCM leaders know the real trouble rarely starts with the claim. It starts upstream at registration.
According to Experian’s State of Claims Report, 32% of denied claims are due to incomplete or incorrect patient registration data and another 50% come from missing or inaccurate claim data. These numbers explain why Experian Health continues to invest in tools that ensure data quality.
“We’ve seen organization deploy AI to reduce denials and automate the appeals process,” said Considine. “However, if the data is not collected correctly upfront it can cause all kinds of downstream problems. So, technology is being deployed to help understand where information is incorrect, repair that, and streamline that whole process.”
“I agree,” echoed Seidman. “RCM leaders have to worry about all of the cost associated with getting it right, the rework, the multiple touches and that’s what we’re trying to reduce or eliminate.”
AI for RCM is Needed in Healthcare
AI is becoming essential for a workforce already stretched thin. Every denied claim, every missing field, every piece of data that needs fixing adds to a growing pile of work that fewer people are available to manage. By absorbing the repetitive tasks and cleaning up the upstream errors that drain time, AI creates space for staff to focus on conversations and decisions that shape a patient’s financial experience.
Learn more about Experian Health at https://www.experian.com/healthcare/
Learn more about Yale New Haven Health at https://www.ynhhs.org/
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