From Data to Decisions: An AI Situation Room for Crisis and Disaster Management

Despite major advances in hazard modelling, climate science, and early warning systems, disaster management decision-making remains constrained by fragmented information, time pressure, and high levels of uncertainty. While large language models (LLMs) show promise in synthesising complex information, their operational use in disaster contexts is limited by concerns around reliability, transparency, and trust. This contribution presents an AI Situation Room architecture designed to address these challenges by embedding LLMs within a structured, agentic decision-support system for disaster risk and humanitarian operations.

At the core of this architecture is AISHA, an agentic superforecaster that combines retrieval-augmented generation, probabilistic reasoning, and explicit hypothesis testing to support situational awareness, short-term risk outlooks, and scenario development. Rather than producing single narrative outputs, AISHA operates across a supervised information value chain: scanning heterogeneous data sources, structuring and triangulating evidence, generating alternative interpretations, assigning confidence levels, and making assumptions and uncertainties explicit. Human analysts remain in the loop at critical stages, ensuring contextual judgement, accountability, and quality control.

The AI Situation Room has been piloted in disaster and crisis-related settings to support rapid analysis, anticipatory action discussions, and operational prioritisation. Results indicate that agentic AI can reduce cognitive overload, improve traceability of analytical judgements, and strengthen the translation of complex risk information into actionable insights. Crucially, the approach reframes LLMs from autonomous answer-generators to analytical collaborators that augment expert reasoning under uncertainty.

This presentation contributes a practical, operationally grounded framework for the responsible adoption of LLMs and agentic AI in disaster management. By addressing transparency, governance, and trust, it demonstrates how AI Situation Rooms can help bridge the persistent gap between geoscientific risk knowledge and real-world decision-making in increasingly volatile hazard environments.