Healthcare has reached a point where AI in revenue cycle management is everywhere. Every conference booth, every vendor pitch, every slide deck seems to feature AI as the hero. Yet for many revenue cycle leaders, the reality is less about transformation and more about noise. In a recent Office Hours conversation, Stuart Newsome and industry leader Nio Queiro, CEO of The Queiro Group, unpacked what it really takes to bring the human touch back into healthcare AI transformation.
Moving beyond point solution fatigue
According to Nio Queiro, many health systems are experiencing what she calls “point solution fatigue.” As she explained, the cost of building AI-driven tools has dropped so dramatically that nearly every vendor now claims to have a solution tied to revenue cycle automation. The problem is that not all of those solutions are rooted in the provider’s real pain.
The starting point is not the algorithm; it is the problem. Leaders must ask where their pain truly lives. Is it in front-end eligibility and authorization, in the mid-cycle with coding and clinical documentation, or in back-end denial management automation and collections? Only after that is clear does it make sense to talk about automation and AI.
Neo distinguishes between two major dimensions of AI in the revenue cycle. The first is automation, where task-based work is streamlined and accelerated, supported by data that makes the automation smarter over time. The second is insight, where AI surfaces patterns and trends that help leaders become better critical thinkers and decision makers. A meaningful AI partner will address both, aligned tightly to the organization’s specific workflow challenges and broader goals for revenue cycle optimization.
AI as a job creator, not a job killer
Fear of job loss remains one of the biggest barriers to AI adoption. Queiro draws a clear parallel to the rollout of EMRs, when many staff feared that automation would replace them. Instead, entire new professions emerged—in data analytics, system configuration, AI model validation, and workflow optimization.
AI in the revenue cycle is following the same pattern. It creates new needs for data specialists, automation monitors, and optimization leads who use AI-generated insights to redesign workflows and speed reimbursement. The work changes, but it does not disappear. The role of leadership is to frame AI as an avenue for upskilling and career growth.
Leaders have to “play” first
Queiro is explicit that leaders cannot convincingly advocate for AI if they have not personally used it. She encourages executives to start with familiar tools like ChatGPT or Microsoft Copilot and apply them to real work—researching a project, summarizing regulations, structuring a proposal, or formatting a report.
In many cases, the tool can complete about 85 percent of the work, leaving the leader to apply judgment and nuance to the final 15 percent. That shift frees up time for strategy and relationship building. Just as important, it reshapes how leaders think about asking questions. Search becomes conversation, and that experience makes it much easier to talk with staff about how AI can be a partner in their daily work.
Ultimately, when leaders “play first,” they normalize experimentation across the organization—and that cultural permission is what drives sustainable change.
Education is central to this transformation. Different people learn in different ways, so leaders should offer training in multiple formats, whether written guides, short videos, or live sessions. AI can even help create those materials at scale and personalize support, such as surfacing just-in-time micro-lessons when users make a recurring error.
Spotting AI “fluff” before it derails your strategy
With so many AI claims in the market, Queiro highlights several red flags that suggest a solution is more buzz than substance. If a vendor cannot clearly articulate which specific revenue cycle problem they solve and how people will remain central to the workflow, caution is warranted. If they resist basic technical transparency about architecture, integration, or data flows, IT leaders may struggle to validate security and long-term viability.
Return on investment is another test. ROI should not be built solely on headcount reduction. Strong AI-driven RCM solutions track measurable lifts in reimbursement, improved speed to payment, cleaner claims, fewer denials, and better use of staff time. Finally, genuine AI is inherently iterative. If a product does not support continuous learning and the expansion of use cases over time, it risks becoming a gimmick rather than a transformative platform.
As Queiro notes, discernment is the new leadership skill in an AI-saturated market.
Making AI adoption human centered
Sustainable AI adoption in the revenue cycle comes down to culture and communication. Leaders must model the behavior they want to see by using AI themselves, sharing results, and being transparent about both successes and stumbles. They should define clear metrics at the outset, then review and refine them frequently as the technology and workflows evolve.
Queiro emphasizes that AI initiatives should not go quiet after go-live. Staff need ongoing feedback about how the solution is performing and a voice in shaping its evolution. When teams see AI consistently reducing clicks, surfacing better information at the point of decision, and simplifying their day, resistance gives way to advocacy.
In the end, AI in revenue cycle management is not about replacing humans—it’s about equipping them with better tools, sharper insight, and more time to focus on the complex, relationship-driven work that only people can do.
That’s the true human touch in AI transformation—and the only path to lasting change.
To explore how Infinx helps healthcare organizations bring the human touch to AI revenue cycle transformation, request a demo and see how technology and people can move forward together.