Artificial intelligence has a critical role to play in flagging weather hazards as they occur, writes Kelly Brough.
In the aftermath of major weather events in Australia, most recently Cyclone Alfred, there’s often a heightened sense of urgency to better prepare ourselves for the next one. And we know all too well that the frequency and intensity of natural disasters in Australia is increasing – storms, floods, bushfires, cyclones and heatwaves, are all on the horizon. It’s a matter of when, not if.
With each disaster, we tend to focus on the minutes and hours after the fact. Could we have had faster emergency responses, better evacuations, more helicopters in the air? While these improvements are important, focusing solely on response overlooks a fundamental truth. By the time a disaster is unfolding, much of the damage is already inevitable.
There is a growing recognition that we must shift more investment and attention toward prevention. The challenge is that traditional prevention methods rely on data and assessments that can quickly become outdated. This is where AI has a critical, but currently underutilised, role to play.
Unlike real-time detection tools designed to flag hazards as they occur, AI can support strategic prevention and long-term planning. By drawing on historical and real-time data, and environmental patterns, it can help governments and first responders fundamentally change how disaster resilience is approached. It will take us from simply reacting to weather events, to anticipating and reducing the risk before they hit.
Using AI to guide hazard reduction and mitigation
Every year, Australia invests millions in hazard reduction and mitigation activities. Yet deciding where, when, and how to deploy these resources remains a major challenge. Agencies typically rely on a patchwork of historical data, which can leave high-risk areas under-prioritised or actions poorly timed.
AI unlocks the ability to model large, complex datasets – consisting of vegetation patterns, topography, real-time weather conditions, and even informal community reports – to predict where intervention efforts would have the greatest impact. For example, it can help identify when a particular region is entering a period of high ignition risk due to a combination of fuel loads, dryness, and wind patterns. We can then prescribe controlled burns to occur at the optimal window, to reduce likelihood of a sudden outbreak of bushfires that spread out of control.
Similarly, AI can support governments in prioritising infrastructure investments by modelling future risk scenarios. This ensures that resilience efforts protect the most vulnerable communities based on forward-looking insights, not just past events. This type of modelling is already used for insurance underwriting so extending to proactive risk management is a logical opportunity.
There is also growing interest in how this kind of predictive capability could enable public-private collaboration. For example, insurers could co-invest with governments in community-level risk reduction programs, while also using AI-generated insights to support insurance affordability and accessibility in high-risk areas.
Improving coordination across multi-jurisdiction agencies
Disaster resilience is rarely the responsibility of a single entity. Local councils, emergency services, state and federal agencies all play a role. Yet fragmented data systems and differing approaches to risk assessment creates inefficiencies and friction.
A shared, AI-enabled platform can provide consistent, real-time risk insights across disparate agencies. Better coordination will lead to more unified responses, directly translating into better outcomes for communities.
Additionally, AI can provide a credible, data-driven basis for funding decisions. By modelling risk reduction outcomes, governments can allocate resources transparently to initiatives that will deliver the greatest impact. By ensuring funding is fair and evidence-based, agencies can also better build trust with the local community.
Why responsible AI matters in disaster resilience
While AI technologies offer enormous potential, they must be designed and deployed responsibly. After all, disaster resilience is fundamentally about protecting lives and livelihoods, so any system must be designed for and with the community. That begins with clear governance frameworks: robust standards for data privacy and security; transparency around how insights are generated; and guardrails to ensure AI is only used to support, not replace, human decision-making.
It also requires meaningful community engagement. AI tools must reflect the lived experience of the people they’re designed to protect. That means working closely with local communities who bring deep, place-based knowledge and long-standing land management practices to the table. When diverse voices shape the development of these tools, we build more effective ones.
A proactive future for disaster resilience
While we know extreme weather events are becoming more frequent and intense, it doesn’t mean the consequences have to get worse too. AI is unlocking a new era of disaster management centred on prevention, early action, and smarter resource allocation.
At Accenture, we’re exploring the development of our own AI-powered Multi-Hazard Resilience Platform, to support governments and first responders to plan ahead, allocate resources more effectively, and protect the communities most at risk.
Our approach to supporting Australia’s weather disaster resilience is centred on long-term, strategic prevention and coordination. The work we’re doing today is designed to help decision-makers take action before disaster strikes, because true resilience starts long before the emergency call is made.
Kelly Brough is Data & AI Lead at Accenture ANZ
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