As the urgency of climate action grows, cities are becoming central to both managing climate risks and advancing solutions at scale. By 2050, nearly 70% of the global population is projected to live in cities. But amid intensifying climate risks, urban areas need resilient infrastructure that not only meets current needs but can withstand and adapt to future stresses. Resilience today requires more than physical robustness, it demands flexibility, adaptability and sustainability as well.

Against this backdrop, artificial intelligence (AI) is becoming a cornerstone of resilience, providing cities with new capabilities to predict risks, optimise resources, and plan for the future. The launch of the AI Climate Institute (AICI) at COP30, aimed to equip developing countries with crucial skills to harness AI for climate action, reflects the growing recognition that AI systems should be built suited to respective local needs and climate realities.

Malaysia is already showing how AI can work in practice, with the technology being applied across key areas from disaster preparedness, traffic management, water systems, to decentralised energy. Together, these efforts point toward a future where AI is not only making cities smarter but more resilient and centered on communities.

Moving from reactive to predictive response

The traditional approach to urban planning has often been reactive. Cities wait for floods, heatwaves, or power failures to happen before investing in protective measures. However, cities can no longer afford to take a wait-and-see approach with climate extremes becoming faster and less predictable than ever before.

cities can no longer afford to take a wait-and-see approach with climate extremes becoming faster and less predictable than ever before

AI is changing this model by helping governments and utilities move from reactive to predictive response. AI systems trained on large volumes of climate, geospatial, and operational data can identify patterns that humans would struggle to detect, generating forecasts that anticipate shocks hours, days, or even years before they occur.

This predictive capacity is critical as cities confront more complex, unpredictable challenges. AI enables infrastructure to “sense and respond” in real time, whether by shifting energy loads during a heatwave, supporting distribution automation systems that strengthen grid resilience through faster fault detection and service restoration, or redirecting traffic flows after an accident. Cities that can react quickly to shocks reduce downtime, protect citizens, and maintain trust.

Malaysia’s National Flood Forecasting and Warning Programme (PRAB) offers a vivid example. Managed by the Department of Irrigation and Drainage (Jabatan Pengairan Dan Saliran), PRAB uses real-time hydrological data and modelling to improve flood forecasting in 41 major river basins and give earlier warnings to vulnerable communities.

Ultimately, AI-enabled responsiveness moves infrastructure away from static design and towards adaptive systems that evolve with their environment. Infrastructure that adapts dynamically is less likely to fail when stressed, providing the flexibility needed in an era of uncertainty where shocks may be sudden and severe.

Powering urban resilience through smarter energy management

Resilience is not only about managing shocks – it is also about ensuring that limited resources are deployed as efficiently as possible under normal conditions. Inefficiencies create hidden vulnerabilities and costs: overstretched energy grids and frequent uncertainties in times of crises.

One of the largest opportunities for resilient city infrastructure is reducing wasted energy and more effectively integrating renewable sources. AI models can improve energy efficiency by aligning charging schedules and power use with renewable generation peaks, reducing strain on the grid and limiting dependence on non-renewable backup sources.

In Malaysia, Tenaga Nasional Berhad (TNB)’s Community Energy Storage System (CESS) offers an early example of how AI could support a more decentralised energy model. The project combines rooftop solar panels, smart meters, and battery storage to empower urban residents to monitor their energy usage, store excess electricity, and even sell surplus power back to the grid.

Urban resilience also depends on how effectively cities manage everyday energy demand, particularly under climate stress. AI-enabled solutions such as advanced metering infrastructure (AMI) smart meters and time-of-use (ToU) pricing regulated by the Energy Commission (Suruhanjaya Tenaga) align household electricity use with grid capacity and forecasting, reducing peak demand and easing system strain. As climate and demand pressures intensify, technologies such as distribution automation systems could further strengthen grid resilience by enabling faster fault detection, isolation, and service restoration across urban networks.

Anchored on the National Energy Transition Roadmap (NETR), Malaysia’s broader energy transition agenda is supported by a range of policy, infrastructure and technology initiatives aimed at building a more flexible and resilient energy system. Within this wider ecosystem, AI-enabled solutions can help strengthen demand forecasting, optimise grid operations and support the integration of variable renewable energy at scale, translating transition ambitions into more adaptive and efficient energy management practices.

Smarter urban planning for more liveable cities

Beyond the immediate horizon, cities need to anticipate how demographic shifts, technological change, and climate impacts will alter their needs over decades. AI offers tools to support this kind of long-term planning, giving policymakers access to more detailed, dynamic information about urban environments.

Traditional digital twins – or virtual replicas of urban infrastructure and systems – enable city planners to test how buildings, transport networks, or energy grids will respond under different scenarios. AI-enhanced models go further, combining data from multiple domains including water, energy, transport, and other systems into a single, interconnected view.

This helps cities to design, operate, and govern urban systems more holistically – where important decisions are made with full awareness of potential trade-offs and synergies. By experimenting virtually, cities can avoid costly mistakes and design effective systems that remain functional under uncertain conditions.

One of Malaysia’s most notable examples is the Virtual Island of Penang (VIP), an AI-powered digital twin platform launched to improve city management and planning. The RM30 million initiative combines satellite imagery, real-time data, and AI to produce interactive modules such as weather and geotechnical slope monitoring.

Tools like digital twins not only inform better engineering choices but also provide a transparent way to engage communities in city planning, showing the risks and benefits of different options. Engaging citizens helps ensure that AI systems reflect local needs, reduce bias, and achieve legitimacy – ultimately making cities more liveable for residents.

Moving from ambition to practical action with AI

As countries advance climate ambitions, particularly around net zero, the energy sector, enabled by AI, will be central to turning these goals into real, system-wide change. AI is already helping cities manage critical systems more efficiently, from improving risk forecasting to optimising resource use. In Malaysia, applications ranging from traffic management to energy storage show how AI can strengthen resilience without compromising sustainability.

This growing convergence between energy and AI will also take centre stage at the Energy Transition Conference 2026 (ETCon26), taking place in June 2026, where policymakers, industry leaders and innovators will explore how both sectors can work together to support a smarter, more resilient and sustainable energy future. The conference reflects a broader regional shift towards viewing AI not simply as a digital tool, but as a strategic enabler of the energy transition itself.

Yet achieving these outcomes depends on more than technology alone. Cities must equally invest in governance, skills, and public trust to ensure AI serves all residents equitably. Responsible deployment, clear accountability, and open communication will determine whether AI truly strengthens the social contract between cities and citizens.

If these foundations are built, the relationship between energy and AI could become mutually reinforcing. As AI helps optimise urban energy systems, improve grid resilience and manage growing demand more efficiently, a stronger and more reliable energy sector will equally be needed to support the expanding digital and AI ecosystem. Together, both sectors can help cities navigate today’s pressures while building greater resilience for the future.