Why Are Hospitals Hiring Chief AI Officers?

As AI moves from pilots into day-to-day healthcare operations, health systems are having to decide who should actually own the strategy, governance, and deployment of these tools. That is helping drive the emergence of the Chief AI Officer, but the role can look very different from one organization to another.

In this Second Opinion webinar, Christina Farr speaks with Ashley Beecy of Sutter Health, Aaron Miri of Baptist Health, and Matt Sakumoto of Nabla about why health systems are creating dedicated AI leadership roles, how those leaders work alongside existing digital, clinical, and technology teams, and what changes when AI moves from experimentation into enterprise deployment. The conversation will also explore how health systems evaluate vendors, manage risk, and decide where AI can create meaningful value.

Expect us to cover:

1. Why does a health system need a Chief AI Officer? What problem is the role solving that existing CIO, CDO, clinical, and innovation teams could not? Is this likely to become a permanent part of the healthcare C-suite?
2. Who actually owns AI inside a health system? How should responsibility be divided across the Chief AI Officer, CIO/CDIO, clinical leadership, legal, security, and operational teams? Who ultimately makes the call on whether an AI tool gets deployed?
3. What makes an AI product ready for enterprise deployment? What evidence do health systems want to see around clinical value, workflow fit, safety, security, reliability, and ROI before moving beyond a pilot?
4. How are AI leaders changing the vendor buying process? Does having centralized AI leadership make it easier for vendors to navigate a health system, or does it create a higher bar for approval? What should founders understand about selling into these organizations?
5. How much transparency do health systems need from AI vendors? What does a health system need to understand about how a model works, how it was validated, and how its performance is monitored without requiring a vendor to expose proprietary technology?
6. EHR-native AI vs. independent tools When should health systems rely on AI capabilities built into major platforms, and when does it make sense to bring in specialized third-party tools? How do leaders avoid adding another layer of software complexity?
7. What happens after the pilot? What separates AI tools that successfully scale across a health system from those that remain stuck in pilots? Is the biggest barrier technology, workflow integration, economics, governance, or organizational change?
8. What does success look like for a Chief AI Officer? How should these leaders be measured? Cost savings, clinician time, patient outcomes, adoption, safety, revenue, or something else?