AI and revenue cycle succession planning: Developing future leaders
September 10, 2026 | Hari Bala
Read time: 4 mins
As healthcare organizations automate more routine and transactional revenue cycle work, many leaders are confronting a new challenge: How do we prepare the next generation of revenue cycle leaders when traditional pathways for learning the business are disappearing?
I recently spoke with Becker's Healthcare about this topic in advance of the 11th Annual IT + Revenue Cycle Conference, where I'll join industry peers for a panel discussion on how AI is reshaping succession planning. We explored how healthcare organizations can strengthen revenue cycle leadership pipelines, develop emerging talent and build workforce strategies that help people thrive alongside AI.
The need for proactive workforce planning is becoming increasingly urgent. Nearly 40% of workers' core skills are expected to change within the next five years as organizations adopt technologies such as AI and automation. For revenue cycle leaders, this underscores the importance of investing in new competencies today and creating clear pathways to develop future leaders.
How AI is changing revenue cycle leadership development
For decades, many revenue cycle leaders began their careers performing high-volume transactional work. These experiences helped them understand payer rules, coding processes, claims management and reimbursement workflows.
Today, AI is increasingly managing many of those routine activities. While this changes how professionals gain experience, it does not eliminate the need for learning and development. Instead, the focus is shifting toward higher-value work.
Tomorrow's revenue cycle leaders will likely develop expertise through:
Rather than spending years focused primarily on transaction processing, emerging talent may gain a broader understanding of organizational performance and revenue integrity much earlier in their careers.
This evolution presents an opportunity for healthcare organizations to create intentional development pathways that provide exposure across Clinical documentation integrity (CDI), coding, denial prevention, revenue integrity and patient financial services.
Cross-functional experience helps future leaders understand how decisions made in one area can influence outcomes throughout the revenue cycle.
The most valuable revenue cycle skills are evolving
As automation expands, the skills that create the greatest value are becoming increasingly human. Future revenue cycle leaders will need strong capabilities in:
- Critical thinking — The ability to assess complex situations and determine the best action when exceptions arise.
- Communication and collaboration — Leaders must align stakeholders across clinical, operational and financial teams while helping employees navigate change.
- Compliance and regulatory expertise — As AI tools become more common, maintaining regulatory compliance and appropriate oversight remains essential.
- Clinical and operational literacy — Understanding the clinical and operational factors that influence reimbursement continues to be a critical competency.
- Workflow design and optimization — Organizations need professionals who can continually improve processes and maximize the value of AI-enabled technologies.
Success is no longer defined by the number of transactions an individual can process. Increasingly, it depends on the ability to monitor, evaluate and improve the systems that drive revenue cycle performance.
Why AI adoption requires workforce transformation
The pace of AI adoption in healthcare continues to accelerate. According to the 2024 HFMA Pulse Survey, more than 70% of healthcare finance leaders reported actively evaluating, piloting or implementing AI-enabled solutions within their revenue cycle operations. As organizations invest in these technologies, many are discovering that achieving value requires more than deploying software.
One of the most common challenges healthcare organizations face is implementing technology faster than they develop the people, processes and governance structures needed to support it. AI does not generate value simply because it has been deployed. Organizations must first identify the problem they are trying to solve and then redesign workflows, responsibilities and training programs around the technology.
The most successful healthcare organizations approach AI implementation as a workforce transformation initiative, not merely a software project. Key success factors include:
- Comprehensive workforce training
- Effective change management
- Clear communication strategies
- Defined AI governance processes
- Ongoing employee development
These efforts help employees understand how their roles are evolving and where human expertise continues to provide unique value.
Building the revenue cycle team of the future
Looking ahead, leading revenue cycle organizations will likely operate through highly automated, exception-based workflows. AI will increasingly support functions such as documentation review, coding assistance, quality assurance and claims processing, reducing the burden of repetitive administrative work and enabling teams to focus on higher-value activities.
As technology takes on more routine tasks, the role of the workforce will continue to evolve. Human expertise will remain essential for governance and oversight, strategic decision-making, risk management and continuous performance improvement. The most successful organizations will be those that combine intelligent automation with leaders who can guide change, evaluate outcomes and drive innovation across the revenue cycle.
Hari Bala is chief technology officer, health information systems, at Solventum.
