AI-Enabled End-to-End Disaster Management Systems: Pathways to Interoperability and Shared ImpactThe increasing frequency and complexity of disasters demands end-to-end systems that can seamlessly link risk knowledge, monitoring, forecasting, early warning, and response. Artificial intelligence (AI) is rapidly advancing these capabilities - improving hazard detection, closing data gaps, enabling anticipatory analysis, and supporting faster, more targeted decision-making. Humanitarian organisations such as the IFRC, Red Cross and Red Crescent National Societies, the Norwegian Refugee Council (NRC) and INFORM together with technical partners such as MapAction, Development Seed, ToggleCorp and Data Friendly Space, are already piloting or deploying AI-enabled tools across the disaster management chain: from geospatial risk assessment and nowcasting to impact modelling, message generation, and operational coordination. Open source approaches remain vital to ensuring transparency, ethical use of AI, national ownership, and long-term sustainability. Yet despite the proliferation of promising tools, the ecosystem remains fragmented, with limited interoperability and uneven data sharing. These challenges constrain the potential for AI-enabled systems to scale and support the ambitions of Early Warnings for All (EW4All). This session will explore what an integrated, AI-enabled disaster management ecosystem could look like, and what barriers must be overcome to get there.
|