In June, the U.S. National Oceanic and Atmospheric Administration warned of a 63% probability that a very strong El Niño will develop before the end of 2026, potentially ranking among the most severe episodes recorded since 1950. The 1997–98 El Niño, one of the most powerful on record, unleashed devastating floods and droughts across Africa, Latin America, North America, and Southeast Asia, causing an estimated 22,000 fatalities and over $36 billion in economic damage.

Previous major El Niño events have likewise produced crop failures, catastrophic peatland fires, and extended droughts. More recently, the 2015 fire season in Indonesia—amplified by El Niño conditions—burned roughly 2.6 million hectares of land and generated economic losses exceeding $16 billion, according to World Bank estimates, as toxic haze blanketed Singapore, Malaysia, and Thailand, disrupting aviation, closing schools, and triggering public health emergencies across the region.

The consequences of such weather disruptions cascade through regional supply chains, affecting sectors ranging from aviation and manufacturing to insurance and public health. Southeast Asian nations including Indonesia, the Philippines, Vietnam, and Thailand consistently rank among the world's most climate-vulnerable countries, making early-warning capabilities not merely an environmental priority but an economic imperative.

Southeast Asia's challenge is not a shortage of climate data. The problem is a lack of decisive action.

The region already has access to satellites, weather observation networks, advanced climate models, and regional monitoring systems. International agencies can forecast El Niño onset months ahead of time, while institutions like Singapore's ASEAN Specialized Meteorological Centre (ASMC) provide continuous monitoring of haze and environmental conditions. The ASEAN Agreement on Transboundary Haze Pollution, which entered into force in 2003 and was ratified by Indonesia in 2014, along with the ASEAN Coordinating Centre for Humanitarian Assistance on disaster management, provides a legal and institutional framework for cross-border cooperation that SpaceAI tools could strengthen.

Yet governments and businesses still struggle to answer critical questions: Which communities will bear the brunt of extreme weather first? Which peatlands are growing most vulnerable? Which supply chains face the gravest disruption risk? What preventive measures should be taken before environmental stress escalates into economic crisis?

What the region needs is to convert data into timely, trusted decisions rather than simply amassing more unstructured information. This is where SpaceAI—the intersection of artificial intelligence and space technologies—bridges the divide between raw data and decision-making, between streams of numbers and tangible outcomes such as lives saved and households protected.

What Is SpaceAI?

SpaceAI integrates satellite-based Earth observation, large language models, cloud computing, and advanced analytics to convert massive volumes of environmental data into predictive, decision-ready intelligence. The global space economy, valued at approximately $596 billion in 2024 by the Space Foundation, is seeing Earth observation and analytics as one of its fastest-growing segments, with both established space powers and emerging Asian nations increasing investment in orbital infrastructure.

Conventional Earth observation is inherently retrospective. Satellites capture images, analysts interpret them, and governments respond only after damage has occurred.

AI changes that paradigm by enabling governments to act proactively. AI models can synthesize satellite imagery with weather forecasts, soil moisture readings, vegetation health indicators, and other environmental signals to pinpoint areas at elevated risk.

This is not a theoretical proposition. Researchers have already combined peat depth, elevation, slope, vegetation type, rainfall, and proximity to infrastructure with satellite data and machine learning to produce fire susceptibility maps for Indonesian peatlands. A more recent study conducted in Riau Province, on the east-central coast of Sumatra, Indonesia, used spaceborne data and machine learning to determine that groundwater level was the primary driver of fire risk. Armed with such risk maps, governments can prioritize patrols and fire bans in high-risk zones, block drainage canals to rewet peatlands, and raise groundwater levels before fires ignite.

Satellites are evolving beyond simple orbital cameras. Rather than transmitting vast quantities of raw data to Earth—which strains bandwidth and slows analysis—AI can process observations directly onboard the satellite, filtering for only the relevant information. This allows decision-makers to receive actionable intelligence faster than traditional analysis methods permit.

Even marginal gains in lead time can yield outsized economic returns. Governments can restore water levels in vulnerable peatlands before fires spread. Firefighting resources can be pre-positioned rather than deployed reactively. Farmers and logistics firms can adjust operations ahead of disruption. Insurers can more precisely model their exposure to weather-related risks.

Prediction Is Not Prevention

Possessing actionable intelligence is of limited value if governments lack either the willingness or the capacity to act on it. Responding to a risk assessment before that risk materializes demands political resolve—better data and superior analysis alone cannot fully resolve that challenge.

Nonetheless, SpaceAI's value lies in reducing the uncertainty that gives policymakers a pretext to defer action.

Achieving this vision requires Southeast Asia to build an integrated ecosystem connecting Earth observation, AI, scientific expertise, and trusted public institutions. Satellites supply the data, AI transforms it into predictive intelligence, and governments, emergency responders, and businesses translate those insights into coordinated action.

Singapore offers a concrete example of what one component of such an ecosystem can look like. Since April 2026, its newly established National Space Agency of Singapore (NSAS) has unified the country's space functions under a single body, with a mandate encompassing regulation, industry development, and the cultivation of domestic space and AI talent. The government has committed more than 200 million Singapore dollars ($155 million) to space research and development since 2022. Initiatives such as the forthcoming NeuSAR-2 synthetic aperture radar constellation—designed to enhance day-and-night, all-weather Earth observation across the region—demonstrate how that investment is translating into improved orbital capabilities.

The broader lesson for Southeast Asia is less about any individual satellite and more about the institutional infrastructure behind it: a dedicated agency, sustained financial commitment, and a workforce trained to convert data into decisions. Neighbors like Indonesia and Malaysia have also been developing their own space programs—Indonesia through its National Institute of Aeronautics and Space (LAPAN, now integrated into BRIN) and Malaysia through MYSA—but most regional players remain at earlier stages of integrating AI into their Earth observation workflows.

Once that foundation is established, Southeast Asia's governments must tackle the next challenge: cultivating the interdisciplinary workforce and cross-border trust required to translate sophisticated analytics into actionable policy.

Climate resilience is increasingly tied to economic competitiveness. Nations capable of anticipating disruptions before they snowball into supply chain breakdowns, public health emergencies, or financial losses will command a strategic advantage over those still dependent on reactive disaster management.

The warning signs for the next super El Niño are already sounding. SpaceAI cannot fully replace human judgment, nor can it substitute for the political will to act upon what it reveals. What it can do is narrow the distance between knowing and acting—making it easier for Southeast Asia's decision-makers to close the remaining gap themselves.

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This story was originally featured on Fortune.com.