At Becker’s 16th Annual Meeting, healthcare leaders gathered at an Optum-sponsored executive roundtable to discuss AI in action—and the clinical and financial value it’s delivering today.
The roundtable was moderated by Devin Airey, managing director at The Advisory Board. Panelists included Virginia Pfeifer, senior director of strategic product management at Optum, and Nancy Landman, senior director of provider technology consulting at Optum.
Here are three key takeaways from the session:
1. Data-driven AI starts with trusted data
Organizations recognize that effective AI depends on large volumes of high-quality data from multiple sources. Integrating data across silos is essential—but complex. Mistrust often emerges when teams compare reports and conclude, “That’s not what my data says,” one participant noted. Building confidence starts with strong data governance, a foundational capability that’s frequently underestimated.
2. Beyond trusted data, AI success requires clinician buy-in
Even the best tools won’t create value if clinicians don’t support them. Many organizations have “tech-forward” clinicians who are eager to help design and test new solutions. Engaging these early adopters—and acting on their feedback—can turn them into champions who drive broader adoption.
A potential risk, however, is that clinician-built tools meet the needs of a small group but don’t scale, creating new silos. That’s why IT and clinical informatics teams need to stay closely involved—evaluating interoperability, governance, workflow alignment and enterprise rollout from the start.
One participant cautioned that adopting multiple AI point solutions can add work and dilute—or eliminate—ROI. “Leverage what you have, especially your EHR,” the participant advised. Start by maximizing existing platform capabilities, then use AI selectively to fill well-defined gaps.
3. Health systems are seeing benefits from AI but haven’t quantified them yet
Most health systems have implemented AI—particularly ambient listening—and early feedback is largely positive. Participants shared that these tools can save time, reduce documentation burden and improve efficiency, as intended.
In theory, those gains should translate into lower costs, stronger margins, greater capacity and increased revenue eventually leading to improved patient experience and outcomes. Yet few organizations have been able to demonstrate hard cost savings or a clear ROI so far—though most expect measurement to mature over time. “This is so new,” one participant said.
A participant whose organization is seeing positive returns in the revenue cycle noted that the team is also using generative AI, algorithms and predictive modeling in clinical settings. While leaders believe these approaches will improve care coordination and clinician efficiency, it remains difficult to attribute savings or ROI to any single technology when multiple initiatives are deployed in parallel.
Another participant shared a use case where AI supported clinical excellence and revenue growth. The organization used AI to identify incidental findings in imaging—valuable insights hidden in existing data. When the tool flagged a potential lung nodule, an APRN saw the patient within one day in a dedicated clinic. If follow-up testing confirmed concern, the patient was immediately connected to the oncology service line. This approach improved outcomes, leveraged existing capacity and generated a positive margin without adding new overhead.
Looking ahead, one participant envisioned AI-enabled emergency departments where scans are interpreted more quickly—improving throughput and time to treat, reducing avoidable admissions and lowering costs.
Conclusion: The discussion underscored a consistent message: realizing AI’s promise in healthcare requires more than deploying new tools. Health systems that pair trusted, well-governed data with clinician partnership—and a deliberate strategy to scale what works—are well positioned to translate efficiency gains into measurable clinical and financial outcomes. As organizations mature their measurement approaches, leaders expect the focus to shift from experimenting with isolated solutions to operationalizing AI across workflows that improve care, capacity and cost.
At the Becker's 11th Annual IT + Revenue Cycle Conference: The Future of AI & Digital Health, taking place September 14–17 in Chicago, healthcare executives and digital leaders from across the country will come together to explore how AI, interoperability, cybersecurity, and revenue cycle innovation are transforming care delivery, strengthening financial performance, and driving the next era of digital health. Apply for complimentary registration now.
