Inside an AI Operating Model: Montefiore’s Approach to Governance, Portfolio Decisions, and Adoption
As health systems move from isolated AI pilots to enterprise deployment, leaders need a disciplined way to govern, evaluate and scale solutions across clinical and operational settings.
In this webinar, leaders from Montefiore Health System share their approach to establishing AI governance amid competing stakeholder priorities and growing demand for AI-enabled capabilities. Speakers discuss how Montefiore's AI journey has evolved and how AI solutions are evaluated — including capabilities from existing third-party partner platforms and when internal development makes sense. Panelists also discuss how they determine where AI agents can deliver value across the organization.
Through clinical and operational case studies, the panel outlines a repeatable governance framework by sharing the role of AI agents in redesigned workflows and learnings from deployment. Attendees will leave with practical guidance for assessing impact through hard financial measures as well as patient, clinician, and employee experience outcomes.
Key Learnings & Speakers
KEY LEARNINGS
Describe the foundational governance, compliance, policy and operating-model decisions needed before scaling AI.
Apply a practical framework to decide when to activate an existing platform capability, buy a point solution or develop internally.
Define success measures for AI that balance hard-dollar return, operational capacity, clinician and patient experience, safety and adoption.
Identify leadership and workforce implications of AI, including change management and baseline AI literacy expectations.
SPEAKERS
Deepesh Chandra, Chief Digital Officer and Information Officer, Montefiore Einstein
Sudipto Srivastava, Chief Data and Analytics Officer, Montefiore Einstein
Moderator: Heather Chait, MBA, DPM, Clinical AI Ecosystem Lead, Philips