As the effects of climate change intensify, financial institutions, policymakers, and businesses are under growing pressure to anticipate and respond to environmental risks. But transforming climate projections into actionable financial strategies is no small task. The data is complex, the models are coarse, and the stakes—economic, environmental, and social—are high. This talk explores how AI and data analytics can be used to bridge the gap between climate science and financial decision-making. We’ll examine three major challenges: the limitations of available climate data, the mismatch between global climate models and the local or sector-specific needs of decision-makers, and the difficulty of translating environmental scenarios into financially consistent, forward-looking outcomes. Through examples such as machine learning-based statistical downscaling and the integration of environmental stressors into investment models, we’ll explore how data can help quantify emerging risks and opportunities. We’ll also touch on the less discussed but increasingly urgent question of how climate change may reshape the future of work, from sectoral disruptions to shifts in labour productivity.