The debate is over. The question is no longer whether AI belongs in healthcare, but how quickly organizations can turn its potential into performance. Now, the primary challenge for CFOs is prioritizing AI investments that deliver measurable ROI and scale under governance.
IAOP’s AI and Healthcare: What’s Real and What’s Hype webinar brought together leading voices across healthcare, technology, law, and strategy to follow this shift from AI as a concept to AI as a driver of revenue. From early failures to financial gains, here’s what health system finance leaders need to know.
The Acceleration Isn’t Slowing Down. It’s Compounding
A year ago, AI in revenue cycle was a promising bet. Today, it’s a corporate asset. Michelle Castillon, Chief Product Transformation Officer, has watched this shift firsthand: while early AI coding solutions stalled at 40-50% accuracy, today’s solutions are reaching 95%+ within 90 days, accelerating cash flow, reducing leakage, and improving net revenue realization. From coin flip to near certainty in one product cycle.
The implication is clear: what was once emerging technology is now delivering enterprise-grade financial impact. AI has become an engine for margin improvement and cash acceleration, but only when paired with disciplined execution.
Investing in High-Yield AI
The difference between AI that has been deployed and AI that is delivering is stark. For finance leaders, this distinction separates sunk cost from realized return. The wins worth CFO attention fall into a few categories:
- Work Queue Intelligence: Predictive modeling outputs embedded directly into revenue cycle workflow tools enable automatic inventory prioritization based on collectability, denial risk, and account complexity. The process becomes scalable without reliance on institutional memory or ad hoc decision-making.
- Appeals and Denial Management: Historically, generating clinical and technical appeal responses has been one of the most time-intensive functions in revenue cycle. That is changing fast. Access to richer clinical and coding data, combined with large language models trained on payer policy and contract terms, has made auto-generated appeal responses increasingly viable.
- The Payer Value Chain: Payer organizations are among the most aggressive AI adopters, applying it across product management, underwriting, care management, pharmacy benefits, claims adjudication, and customer service.
The Gap Between Insight and Execution
A generation of ambitious AI pilots taught the healthcare industry a valuable lesson: deploying technology and deploying it well are entirely different challenges. Early implementation fell short not because the tools were immature, but because outputs were divorced from the people and processes that mattered. Dashboards generated insights that went unused, alerts that were dismissed, and recommendations that never translated into action.
The leaders seeing real results are those that seamlessly weave AI into workflows, so the technology functions not a separate layer of analysis but as part of the organizational fabric itself. This way, intelligence is connected directly to execution. The lesson here is operational: Insight is easy. Integration is where the rubber meets the road.
Security Risks Can Sink ROI
As digital access expands from humans to autonomous agents, the number of entry points for malicious actors increases. In healthcare, that risk is magnified. Each system vulnerability puts patient safety, data privacy, and clinical outcomes at risk.
As organizations adopt AI agents, it increases the surface area for cyberattacks. - Jessica Pan, Global Outreach, SandboxAQ
For CFOs, these risks carry direct financial consequences, including regulatory penalties and reputational damage. The solution is not to retreat from AI, but to deploy it with the same level of sophistication leveraged by attackers.
Health systems that build proactive security posture into AI governance will be in a fundamentally different position than those that treat it as an afterthought. Risk-adjusted ROI will increasingly separate successful AI adopters from the rest.
The bots are ready. The question is whether the org chart is, too.
Managing Risk, Maximizing Return
AI progress is only sustainable with robust governance infrastructure. More tools without more discipline means more chaos. Successful AI adoption requires honest communication about what changes and what stays the same. Organizations that address and clarify uncertainty see new technologies embraced.
Creating dedicated AI governance teams, rather than shoehorning oversight into existing IT committees, is critical. Each deployment decision should carry clear answers from the AI governance team. Who owns this? Who audits this? What are the KPIs? If the answer to any of those is “great question,” start over.
The multi-supplier problem reinforces this point. When AI is launched by multiple vendors, organizations must own the outcomes across the whole ecosystem. Centralized ownership ensures leaders maintain visibility into performance. Without that, the ROI crumbles.
It really does take a true investment from an organization to change the culture and manage change and training. - Michelle Castillon, Chief Product Transformation Officer, Vee Healthtek
The Bottom Line for Finance Leaders
The health systems leading the way with AI are not those that have deployed the most tools or chased AI in the abstract. True value comes to those who combine bold experimentation with intentional strategy.
Those realizing measurable impact were deliberate from day one: integrating AI into workflows, establishing well-defined governance protocols, and aligning technology with business outcomes. They learned from their mistakes, turning setbacks into a competitive advantage. They now understand that AI’s potential is not measured by the number of automated systems or workflows but by how AI is embedded, measured, and scaled.
The message is clear. The playbook exists, and the technology is ready. Success now hinges on the execution.