Organizational Governance of Emerging Technologies: AI Adoption in Healthcare

Healthcare organizations need clear decision-making frameworks for AI adoption: The research identified 8 key decision points that align with how organizational leaders actually approach technology adoption, suggesting that standardized governance structures—rather than ad-hoc approaches—are essential for responsible AI implementation in healthcare settings.

Organizational governance is often overlooked despite widespread AI adoption: Despite proliferation of AI use in healthcare, the governance structures surrounding its implementation remain poorly understood, indicating a significant gap between the pace of AI deployment and the institutional frameworks needed to manage it responsibly.

Usability and accessibility of governance standards matter: Usability testing with healthcare and AI ethics professionals revealed that governance frameworks must be designed to be "easily and efficiently" used by organizational leaders, suggesting that overly complex standards may fail to guide actual decision-making in practice.

Multi-stakeholder collaboration is necessary for effective governance: The research involved leaders from major U.S. health systems and professionals from related fields, indicating that developing adequate AI governance requires input from diverse perspectives across healthcare and ethics domains rather than top-down mandates alone.

Practical implementation requires mapping real institutional workflows: By identifying specific decision points within actual health systems, the research demonstrates that effective governance must be grounded in how organizations actually work, not in theoretical ideals disconnected from operational realities.