Leveraging AI to enhance multi-hazard early warning systems

This report assesses current applications of AI across all four MHEWS pillars, identifies operational gaps where AI can add value, and outlines the conditions required for responsible and effective implementation of AI. It builds on the work of the AI for EW4All Group, by drawing on global case studies, partner experience, and emerging research. Across the report, AI is best understood as an enabling technology - one that enhances speed, scale, and analytical capacity, while remaining dependent on strong institutions, governance frameworks, and human expertise.

To support responsible, transparent, and deeper integration of AI into EWS​, the report identifies priority actions:

• ​Robust observational infrastructure — ground networks, satellites, and in-situ sensors — must underpin AI performance, particularly in SIDS, LDCs, and LLDCs.​
• AI integration needs strong governance: national focal points, human oversight for life-safety decisions, and clear accountability frameworks.
• AI design must be human-centred and equity-driven, embedding humanitaria​n principles, ensuring multilingual and low-connectivity compatibility, and co-designing tools with affected communities
• EWS should be built for interpillar integration from the outset, using modular, interoperable architectures and feedback loops that let data from one pillar strengthen the others — risk models, forecasts, communication, and preparedness all reinforcing each other.
• Investments in AI have enormous potential and sustained funding is essential to move AI from pilot projects to operational scale, supported by institutional capacity-building and partnerships across governments, the private sector, and research institutions.​
• Efforts to integrate AI into EWS must focus on addressing existing gaps.