From resistance to ROI: Leading AI-driven workforce change in healthcare

This article is adapted from a featured speaking session at the 2026 Becker's Annual Conference.

National workforce surveys show persistent understaffing across clinical documentation and coding teams, with many health information departments operating below required staffing levels year over year. As AI in the healthcare revenue cycle becomes more accessible, the real challenge is no longer whether to adopt it — but how to lead change responsibly.

For revenue cycle leaders, success now depends on managing transformation in a way that delivers measurable ROI, maintains compliance and empowers the people behind the processes.
 

Redefining the revenue cycle workforce through AI enablement

When we look at the revenue cycle models that are truly productive today, the focus is not on replacing human workers. It is on workforce transformation and enablement.

Manual workarounds, spreadsheets and disconnected tools may help teams survive in the short term, but they inevitably create new bottlenecks and burnout. In contrast, successful AI integration embeds technology directly into existing workflows, supporting (not interrupting) the people making complex decisions every day.

By augmenting CDI and coding teams with AI, organizations can offset severe labor shortages while allowing skilled professionals to focus on higher‑value analytical work. This approach not only improves throughput but also helps retain experienced staff by reducing repetitive, low‑complexity tasks that contribute to dissatisfaction and turnover. 
 

Eliminating coding backlogs and revenue leakage with AI

For many health systems, the fastest path to ROI comes from addressing specific operational pain points.

Using autonomous coding to address high‑volume, low‑complexity work

Tami McMasters Gomez, executive director of revenue cycle at UC Davis, notes that immediate operational value often comes from tackling coding backlogs. By deploying autonomous coding solutions for repetitive, entry‑level tasks, particularly in high‑volume areas such as radiology and pathology, organizations can begin to dig out of massive queues. 

This targeted use of AI directly impacts key performance indicators across the revenue cycle. Improvements in discharged‑not‑final‑billed (DNFB) metrics help accelerate billing timelines, reduce rework and improve days in accounts receivable. Over time, these gains translate into stronger cash on hand and more predictable financial performance. 

Preventing denials earlier in the revenue cycle

Tami also highlights the importance of using AI at the front end of the revenue cycle. Leveraging technology for prior authorizations and documentation alignment helps prevent downstream denials and mismatched diagnosis-related groups (DRGs).

When issues are addressed earlier, teams spend less time chasing corrections after the fact. Patients receive cleaner, more timely bills, and finance leaders avoid the costly ripple effects of delayed collections and appeals.
 

Building workforce trust through transparent, compliant AI

AI can only deliver a strong return on investment if the workforce trusts it.

Michelle McCormack, director of CDI, emphasizes that embedding AI into daily workflows requires a deliberate shift in mindset, from fear to empowerment. This shift depends on transparency, auditability and a clear understanding of how AI supports, rather than replaces, human expertise.

Why human oversight is essential for ai in healthcare

Recent state-level legislation, including in California, explicitly mandates that AI cannot be used to make healthcare decisions. This reinforces a critical principle for revenue cycle leaders: humans must remain in the loop.

Trust is built when staff understand what the technology is doing, what it is returning and how those outputs are validated. Strong compliance and governance frameworks ensure AI recommendations are reviewable, explainable and aligned with evolving regulatory requirements.

Change management succeeds when leaders are open about how AI impacts daily responsibilities and when teams feel confident that accuracy, ethics and accountability remain top priorities.
 

Leadership commitments for scaling AI responsibly

Successfully managing the transition to an AI-enabled revenue cycle requires a few core leadership commitments:

• Transparency in AI-enabled workflows: Be clear about what is changing, why it matters and how it benefits both the organization and the people doing the work.
• Governance and compliance by design: build trust through ongoing review of AI outputs, focused attention on high-risk denials and alignment with emerging state and federal regulations.
• Data-driven ROI measurement: Use your data to track progress continuously. Identify upstream gaps, evaluate vendor performance and confirm that AI is truly reducing administrative burden — not shifting it elsewhere.
   

Securing the future of your revenue cycle with AI

The promise of AI in the healthcare revenue cycle extends far beyond cost reduction. It is about creating a resilient financial foundation for the entire organization.

When rework is reduced, 30-day delays in collections are prevented and patients receive accurate bills in a timely manner, both cash flow and the patient experience improve. By leading AI adoption with empathy, strong governance and a commitment to workforce enablement, revenue cycle leaders can turn today’s labor challenges into an opportunity for sustainable growth.

When you focus on people first and technology second, resistance gives way to trust, and AI delivers the ROI it was meant to achieve.

Thea Campbell, MBA, RHIA, FAHIMA, is global business director, revenue cycle — revenue integrity at Solventum.

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