Helping disaster response teams turn AI into action across Asia

Today in Bangkok, we’re bringing together 50 disaster management leaders from across Southeast and South Asia for our inaugural AI Jam for Disaster Management professionals, in partnership with the Gates Foundation, Asian Disaster Preparedness Center (APDC), and DataKind. 

The question guiding this initiative is simple, but urgent: How can AI help governments and nonprofits respond faster and more effectively when it matters most? 

Participants come from 13 countries—Bangladesh, India, Indonesia, Lao PDR, Malaysia, Myanmar, Nepal, Pakistan, Philippines, Sri Lanka, Thailand, Timor Leste, Vietnam—representing government agencies, multilateral organizations and non-profits. Many are directly involved in disaster response on the ground, coordinating information, supporting affected communities, and making time-critical decisions.

This effort also builds on the expansion of our OpenAI for Countries Program⁠(opens in a new window) announced at Davos. At its core, this work is about helping organizations move beyond interest in AI and into real-world applications, embedding it into the operational challenges they face every day. 

Responding to growing disaster risks in Asia 

Disaster response teams often operate in resource-constrained environments, working with fragmented data, manual processes and limited infrastructure. These constraints can slow coordination and delay critical decisions, especially in fast-moving situations where timely information is essential. Many teams are now exploring how AI can better support these workflows.

That urgency is only growing. In the second half of last year, a series of typhoons and severe storms across South and Southeast Asia disrupted communities and stretched disaster response systems to their limits. Asia remains the world’s most disaster-prone region, accounting for an estimated 75% of people affected by disasters globally. The World Bank estimates disasters have cost ASEAN countries more than $11 billion in previous years.