In an era marked by complex global interdependencies, organizational risk management has emerged as a key strategic function. Risk is no longer confined to financial loss or operational disruption but encompasses a wide spectrum of uncertainties with implications for long-term sustainability and competitiveness. Traditional approaches, including ISO 31000 and COSO ERM, emphasize risk identification, evaluation, and mitigation within a structured governance framework. However, recent technological advancements, particularly in the field of artificial intelligence (AI), have introduced new paradigms in how risks are perceived, predicted, and managed. The primary aim of article is to systematize core definitions, clarify key theoretical frameworks, and identify unresolved questions through a comprehensive review of contemporary academic literature. This article provides a comprehensive theoretical overview of organizational risk management while integrating AI-driven tools and approaches. The main conclusions of the article indicate that risk management has undergone a transformation from a reactive to a proactive approach. Today’s organizations not only respond to risk, but learn to anticipate and exploit it. In addition, the use of AI significantly increases organizational capabilities in the field of risk identification, analysis and control. On the other hand, integrating AI brings ethical and systemic challenges, and risk management alone in the future requires hybrid intelligence. The conclusions drawn are theoretical – further empirical research is needed.