Artificial Intelligence as a Coordination Mechanism in Crisis Management

Abstract

In an era of increasing complexity, effective emergency and crisis management demands sophisticated mechanisms to orchestrate multi-stakeholder responses across organizational and geographic boundaries. Generative AI has been explored for predictive analytics and content generation, but its potential as a coordination infrastructure and enabler of response readiness remains partially unexplored. Drawing on coordination science and crisis theory, we identify critical coordination challenges in contemporary crisis management, which are represented by temporal synchronization across distributed teams, information flows among heterogeneous stakeholders, task interdependency in dynamic environments, and resource allocation under uncertainty. We then define coordination mechanisms related to three dependency types, that is, “flow”, “fit” and “share”, and we discuss, also using illustrative examples, how AI can function as intelligent coordination agent that facilitates mutual adjustment, standardizes processes, and enables real-time protocol adaptation. Furthermore, we define three pillars of AI-enhanced coordination capacity which have an impact on organizational readiness: (a) dependency visibility; (b) mechanism agility; and (c) learning capability. We then present a framework of organizational readiness driven by coordination capacity. The paper contributes to crisis management literature by reframing readiness as organizational coordination capacity, and offers practical implications for designing AI-integrated crisis management systems in diverse contexts.