Why AI in revenue cycle has become a necessity — 3 takeaways

Revenue cycle leaders are facing a national coder shortage, intensifying payer complexity and tighter margins. In this environment, AI has become an operational necessity, not a strategic option. But the differentiator isn’t tool selection; it’s whether leaders have reframed workforce and governance for a technology that now arrives continuously, not in discrete projects.

During an April 14 featured session hosted by Soventum at Becker’s Hospital Review 16th Annual Meeting, three leaders working across academic medical centers, health systems and vendor partnerships discussed how they are pairing AI with upskilling and governance to protect both ROI and teams. The panelists were:

• Michelle McCormack, director of clinical documentation integrity at a large health system in California

• Tami McMasters Gomez, executive director of revenue cycle at UC Davis Health in Sacramento, Calif.

• Thea Campbell, business director of revenue cycle at Solventum and acting president of the American Health Information Management Association

Below are three takeaways from their conversation.

Note: Quotes have been edited for length and clarity.

1. The business case is coder shortage meets payer complexity

Ms. McMasters Gomez traced AI’s shift from innovation to necessity in mid-rev cycle to a national coder shortage producing backlogs in charge lag, AR days and related KPIs — especially at growing systems.

Ms. Campbell said payer complexity is the equal partner: realizing revenue has grown too fragmented and payer-specific for a single standardized workflow, and with organizations fighting for every dollar they collect, AI has become the tool finally equipped to address that complexity at scale.

2. Upskilling, not replacement, is the workforce play

At UC Davis Health, Ms. McMasters Gomez said the team is moving coders from repetitive areas like radiology, where AI is mature, onto more complex work such as dermatology procedure coding — while standing up a formal policy for auditing AI output and routing coders into different roles.

“There’s got to be a human in the loop,” she said.

Ms. McCormack said CDI teams will need a different talent mix once simple audits are automated: strategic thinkers, project managers and staff who can read tool performance and feed learnings back to vendors.

“These tools are only as good as the documentation it sits on,” she said.

Ms. Campbell framed the cultural shift plainly: “AI is not going to take your job. AI is going to change your job.”

3. Governance and pace separate winners from losers

Ms. McMasters Gomez described UC Davis’s IT AI governance and IT security structures, which vet every vendor through what she called a deliberately agonizing process before sign-off.

Ms. Campbell said IT partners at large systems are sometimes managing 150-plus products, meaning revenue cycle has to negotiate for a handful of priority projects rather than assume capacity.

Both Ms. McCormack and Ms. Campbell said AI can no longer be treated as a discrete project — new capabilities arrive continuously, and leaders who chase every vendor will face significant rework as regulation catches up.

Beyond the project mindset

The panel’s common thread: pace, governance and workforce strategy matter more than tool selection. Ms. McCormack urged caution and stacked deployments around a small set of trusted vendors, with clinical, IT and revenue cycle leaders in the room together before adding more. For executives whose AI programs are still being managed pilot by pilot, the implication is a structural rethink of how AI capabilities are absorbed into daily operations.

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