Emerging patterns and fresh directions in AI & climate adaptation

AI-powered systems, such as climate-risk intelligence dashboards and government decision-support tools, demonstrate how AI is being integrated into policy, infrastructure design, and investment planning. Advances in explainable AI, climate-data agents, and knowledge-graph systems are also making complex climate information more accessible and actionable for practitioners.

However, the key challenge are geographic and sectoral gaps persist, with underrepresentation of vulnerable regions and limited focus on community-level adaptation. Concerns around data governance, transparency, and equitable deployment remain central. Taken together, the current wave of research and innovation points toward a rapidly maturing AI landscape — one with significant potential to strengthen climate resilience, provided it is developed and applied responsibly.

• Growing use of AI agents & knowledge graphs to democratise climate data analysis lowering the barrier for non-specialists to work with climate data.

• Development of explainable AI (XAI) in climate risk forecasting (e.g. extreme events, wildfires), to improve transparency and trust in model-driven decisions (not just a “black-box”).

• Use of AI to support climate finance, tracking investments, enabling better governance and oversight — bridging the gap between data, funding, and on-the-ground adaptation.

• AI as a decision-support tool for national adaptation planning, helping governments and institutions prioritise where to invest for resilience (e.g. via the IWMI tool).

• Broader recognition of foundation models and large AI systems as relevant to climate change — beyond narrow tools (e.g. remote sensing, risk mapping), reflecting the cross-sectoral potential of AI.