AI Is Now Core to Revenue Cycle Management: What Health Leaders Reported
A recent American Hospital Association webinar, "Beyond the Buzz: Measuring AI's Impact on Health Care Revenue Cycle Management," reviewed a Forrester Consulting study commissioned by Waystar. The study surveyed 316 revenue cycle and IT leaders: 34% from hospitals and health systems, 66% from non-acute providers. Roles spanned directors (40%), managers (28%), VPs (23%) and C-suite (9%).
The signal is clear: leaders see AI as integral to RCM. Seventy percent view AI in RCM as a critical priority, and 90% plan to maintain or increase investment. Confidence is rising too-60% now trust AI more than when they first implemented it.
Where AI Is Delivering Measurable Results
- Workforce efficiency: 36% report gains
- Denial prevention: 27% improved
- Payment speed: 21% improved
- Payment accuracy: 18% improved
- Patient experience and collections: 37% improved
- Reporting and analytics: 23% improved
- Cash flow: 22% improved
- Accuracy vs. manual processes: 60% say AI is more accurate; 19% say significantly more accurate
As one panelist put it, as confidence grows, leaders expand AI use cases-focusing where impact is greatest.
Adoption Is Accelerating Inside Workflows
Within months, AI moved from testing to embedded practice. In September 2024, adoption was early and cautious. By June 2025, AI had become a strategic imperative-built into workflows and tied to operational efficiency and financial performance.
Leaders are prioritizing tighter payer engagement, deeper relationships with existing tech partners, and embedding AI within current solutions rather than running it on the side.
What This Means for RCM Leaders
- Prioritize use cases with direct financial impact first: denial prevention, patient financial experience, payment accuracy and speed.
- Set baselines and target metrics upfront (clean claim rate, denial rate, days in A/R, cost-to-collect, staff throughput).
- Integrate AI into existing platforms (EHR, clearinghouse, rules engines, RPA) to avoid fragmented workflows.
- Establish clear ownership, auditability, and human-in-the-loop review for higher-risk decisions.
- Demand transparency from vendors on models, data inputs, error rates and continuous improvement cadence.
- Pilot fast, measure rigorously, and scale what beats your benchmarks-not what demos well.
- Upskill your team to manage prompts, exception queues and model feedback loops.
- Align incentives with payer partners around clean claims and first-pass resolution to compound AI gains.
Resources
Explore the American Hospital Association's work on digital transformation and finance strategy at the AHA.
If you're building team capability for AI in operations and finance, review curated AI courses by job role to speed up adoption and results.
