Once disaster hits, time is at its most valuable. Whether a hurricane empties normally quiet streets or wildfires have the potential to burn entire towns to the ground, emergency managers find themselves in high-stress situations in which every second—and every move—matters. They need to mobilize teams, distribute scarce resources, and make rapid-fire decisions, often based on glimpses of the larger picture. It’s a position that requires severe expertise, good judgment, and the capacity to make tough decisions under incredible pressure.
And the stakes keep rising. Climate change is driving more frequent and intense disasters, straining emergency response systems to their breaking point. Most teams are still using old-school methods—whiteboards, spreadsheets, and scribbled notes—despite the magnitude and velocity of crises growing. Amidst chaos, even experienced practitioners can find themselves overwhelmed by the stream of information heading their way.
This is where AI is starting to make a tangible difference—not by automating away the human element, but by augmenting it. As Texas A&M University’s Dr. Ali Mostafavi explains, the objective of AI in emergency management isn’t to robotize individuals out of the loop, but to enable them to do their jobs faster, smarter, and better informed. AI is best at sifting through tremendous quantities of information, determining patterns, and pointing out perils that may not be apparent to the naked eye in the moment.
Consider flood prediction. At Texas A&M, Dr. Mostafavi’s Urban Resilience Lab has developed the Flood Genome, an AI-powered model that draws from decades of data to forecast how floodwaters will move through specific streets and neighborhoods. With a six-to-twelve-hour heads-up, emergency managers can reroute traffic, prepare shelters, and issue more precise evacuation orders. It’s not about eliminating risk—it’s about improving foresight and shrinking the window for error.
In firefighting itself, AI is becoming a trusted partner. California’s Department of Forestry and Fire Protection employs an extensive system of more than a thousand cameras to scan at-risk zones. Those feeds are scanned in real time by ALERTCalifornia, an AI program that identifies suspicious patterns for human examination. Battalion Chief David Acuña points out that though the technology is astounding, human judgment remains essential, particularly when a cloud of dust or a setting sun might be confused with a fire. The system aids early detection, but ultimate judgment still belongs to seasoned professionals.
That integration of tech and human judgment is where AI has the most to offer. But bringing new tools into emergency response workflows isn’t always easy. Response teams depend on processes that have been developed over the years of experience. If an AI system introduces complexity, necessitates steep learning, or spits out data that’s difficult to act on, it could be ignored at the very times it’s most needed. As Dr. Mostafavi says, AI must function within the systems teams already employ—providing insights, not just additional data.
Still, there are already clear examples of AI making a difference on the ground. When Hurricane Beryl struck Houston, power outages quickly turned into a food access crisis. Dr. Mostafavi’s team worked closely with the Houston Food Bank, using AI tools to map disrupted areas and identify the communities most in need. Their daily updated analysis enabled food distribution efforts to become more targeted and efficient, providing a key edge during an era when speed and precision were paramount.
Fundamentally, AI for disaster response works like a second pair of eyes and ears—always active, never tired, and able to sift through amounts of data that no individual could. But the genuine heart of any emergency response is still the individuals at the front: the responders exercising judgment calls, responding to changing conditions, and doing whatever it takes to keep others alive.
Technology by itself is not the solution. But when used appropriately, AI has the potential to enable emergency managers to make quicker, more informed decisions—and ultimately save more lives when it counts.”.
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