<![CDATA[The adoption of Artificial Intelligence (AI) in health insurance organizations has significantly transformed core operational processes such as underwriting, claims management, fraud detection, and customer analytics. While AI enhances efficiency and decision-making accuracy, it simultaneously introduces emerging risks that are complex, dynamic, and difficult to manage using traditional risk management approaches. These risks include algorithmic bias, lack of transparency, data privacy breaches, model drift, and increasing organizational dependency on automated decision systems. This study develops a conceptual framework for managing emerging AI risks in health insurance organizations through the integration of Enterprise Risk Management (ERM), risk governance, and risk leadership. The research employs a qualitative conceptual approach supported by a structured literature synthesis of Scopus-indexed journals and authoritative institutional reports. The theoretical foundation is grounded in COSO ERM (2017), ISO 31000:2018, and the NIST Artificial Intelligence Risk Management Framework (2023), complemented by recent literature on AI governance and risk leadership in high-stakes industries. The findings suggest that effective management of AI-related risks requires a multi-layered approach. Enterprise Risk Management provides a structured mechanism for risk identification, assessment, and mitigation. Risk governance ensures accountability, regulatory compliance, and ethical oversight in AI deployment. Meanwhile, risk leadership plays a critical role in shaping organizational culture, promoting ethical awareness, and ensuring cross-functional alignment in decision-making processes. The integration of these three dimensions forms a comprehensive framework that enhances organizational resilience in managing AI-driven uncertainties in the health insurance sector. ]]>
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