AI is absorbing much of the entry-level, repetitive work that once taught generations of revenue cycle professionals the business. That raises a workforce question the industry is only beginning to answer: If the traditional training ground changes, what takes its place?
Hari Balasubramanian, chief technology officer of health information systems at Solventum, will take up that question at Becker’s Healthcare’s 11th Annual IT + Revenue Cycle Conference, Sept. 14-17 at the Hilton Chicago, as a panelist on “How AI Shifts Impact RCM Succession Planning.”
Ahead of the conference, Mr. Balasubramanian spoke with Becker’s about how emerging leaders will learn the revenue cycle, which skills are gaining and losing value, and what a well-structured revenue cycle team looks like three to five years from now.
Editor’s note: Responses have been lightly edited for length and clarity.
Question: As AI absorbs more of the entry-level work where future RCM leaders once learned the business, what replaces that traditional training ground?
Hari Balasubramanian: The traditional training ground isn’t disappearing, but it is evolving. Instead of spending years manually processing transactions, future leaders will learn the revenue cycle through exception management, analytics, process optimization and AI oversight — with AI transparency allowing them to clearly examine and understand how AI was applied. The opportunity is to expose emerging talent to a broader view of revenue integrity much earlier in their careers and accelerate their learning.
While AI is tackling higher volumes of routine work, it is still the responsibility of revenue cycle managers to submit clean and appropriate claims. That requires humans in the loop to manage exceptions, apply critical thinking and balance AI’s benefits against compliance risks.
Like any new tool, you have to learn how to use it — you swing a hammer and hit a nail before you can frame a wall. YouTube has helped millions of people use tools and accelerate their learning, and appropriately applied AI will empower revenue cycle teams in the same way.
Finally, as AI automation takes on routine work, organizations should intentionally create rotational experiences across CDI, coding, denial prevention and revenue integrity, so future leaders understand how to manage the entire process and can make informed decisions that ensure changes made upstream positively affect financial outcomes downstream.
Q: When you talk with health system revenue cycle leaders, which skills are becoming more important than they were three years ago, and which traditional skills are becoming less central as automation expands?
HB: The most valuable skills today are critical thinking, communication, workflow design, compliance, clinical literacy and the ability to evaluate and manage AI-generated recommendations. Leaders increasingly need people who can identify process bottlenecks, interpret insights and use critical thinking to make judgment calls and policy decisions when technology surfaces exceptions.
What’s becoming less central is the ability to perform high-volume, rules-based transactional work. Those activities remain important, but automation is increasingly handling them. The differentiator is no longer who can process the most work, but who can manage, monitor and improve the system that governs the work. That includes working with clinical teams and across disciplines more broadly than most teams do today — a welcome shift from reactive to proactive process management.
Q: A lot of organizations automate faster than they are reskilling. Where does that gap show up most clearly in practice, and what should leaders prioritize first to close it?
HB: The gap typically appears when organizations deploy technology but fail to redesign workflows and roles around it. One of the key failures in adopting AI is not defining and fully understanding the problems you are trying to solve. Without that full picture — if you automate before you identify the problem and the best solution — teams suddenly have new tools but are still operating under old processes, which can create confusion, duplicate work and skepticism about whether AI is delivering value.
In addition to ensuring you have subject matter expertise involved, you also need to focus on helping employees understand how their role is changing and what new skills will be expected. Only then do you achieve full understanding and alignment between people and technology. Successful organizations treat AI adoption as a workforce transformation initiative, not a software implementation project. Training, communication and change management should begin before the technology is fully deployed.
Q: Retention is its own challenge. How should leaders communicate about AI and redesign roles in a way that keeps both experienced and newer talent engaged with technology?
HB: Transparency is critical. Employees want to understand what AI will do, what it won’t do and where their expertise continues to matter. When leaders frame AI as a tool that removes repetitive tasks and allows people to focus on more meaningful work, adoption tends to improve. The ultimate compliment from a knowledge worker or subject matter expert is when AI partners with them — and to get there, the solution has to earn trust and be accepted as effective.
Experienced professionals possess institutional knowledge and clinical or coding expertise that AI cannot replicate. At the same time, newer employees often bring comfort with technology and data-driven workflows. The strongest organizations create opportunities for both groups to learn from one another while participating in the transformation together.
Q: Looking three to five years out, what does a well-structured revenue cycle team look like? Where should people add the most value, where should technology take the lead — and what does that mean for how leaders build their bench today?
HB: Three to five years is an eternity in AI time. AI is currently demonstrating a doubling of capability roughly every five months. We are already seeing that Solventum’s agentic AI, combined with our proprietary expertise and content, can match or exceed the coding accuracy of a professional coder without getting tired. But coding rules must be followed, and many medical coding and clinical scenarios today — and in the future — require critical thinking to evaluate and complete.
Three years from now, I expect leading revenue cycle organizations will operate through highly automated, exception-based workflows. Technology will handle much of the routine work involved in documentation review, coding support, quality checks and transaction processing. Humans will manage the exceptions and focus on decisions, policies and improvements in clinical practice and documentation, fine-tuning revenue capture while remaining compliant.
In other words, people will focus on the areas where judgment, governance and cross-functional decision-making are essential: managing exceptions, overseeing AI performance, improving workflows, reducing risk and driving strategic financial outcomes. Leaders building their bench today should prioritize adaptable talent that can bridge operational expertise, business strategy and technology fluency. Tomorrow’s world will also bring regulatory and other changes where revenue cycle leaders will need to continue adapting their organizations and policies using these new tools and capabilities.
Hari Balasubramanian will join the panel “How AI Shifts Impact RCM Succession Planning” at 3:20 p.m. Monday, Sept. 14, at Becker’s 11th Annual IT + Revenue Cycle Conference, Sept. 14-17 at the Hilton Chicago. Click here to learn more.
At the Becker's 11th Annual IT + Revenue Cycle Conference: The Future of AI & Digital Health, taking place September 14–17 in Chicago, healthcare executives and digital leaders from across the country will come together to explore how AI, interoperability, cybersecurity, and revenue cycle innovation are transforming care delivery, strengthening financial performance, and driving the next era of digital health. Apply for complimentary registration now.
