Diving briefs:
- According to a recent release, AI applications such as predictive maintenance and digital twins can prevent 15% of predicted natural disaster losses in the power grid, water systems and transportation infrastructure, savings worldwide by 2050. Deloitte Center for Sustainable Progress Report.
- The report says governments and other stakeholders need to overcome security concerns before technical limitations, financial constraints, regulatory uncertainty, data availability, and AI-enabled resilience were widely adopted in infrastructure systems.
- “Investing in AI will help reduce the frequency of power losses, slow down system recovery after a storm, break down, and reduce unusable roads and bridges,” said Jennifer Steinmann, business leader of Deloitte Global Sustainability, in an email.
Dive Insights:
Natural disasters have caused nearly $200 billion in annual losses to infrastructure around the world over the past 15 years, according to Deloitte. The report could increase to around $460 billion by 2050, according to the report. Climate change is expected to increase the frequency and intensity of these events.
“Investing in AI has the greatest, short-term potential to help reduce the damage caused by storms such as tropical cyclones, tornadoes, thunderstorms, settlements, and blizzards,” Steinman said. “These natural disasters promote the maximum share of infrastructure losses due to high frequency, wide geographic reach and increased strength.”
AI in Infrastructure Resilience Reports uses empirical case studies, probabilistic risk modeling and economic forecasting to demonstrate how AI can help leaders strengthen their infrastructure, responding, responding, and recovering more rapidly from natural disasters.
“AI technology can provide preventive, detective and responsive solutions to deal with natural disasters, but some interventions have more impact than others,” says Steinmann.
Investing in AI while the infrastructure is in the planning stage accounts for about two-thirds of the possibility of AI preventing natural disaster costs, she said. Tools like AI-powered digital twins, predictive maintenance systems, and scenario analysis can help urban planners design more resilient infrastructure.
“At the same time, leaders need to invest in building the required digital and data infrastructure, encourage cross-sector collaboration, and ensure access to high-quality data so that they can maximize the effectiveness of their AI tools in three phases: planning, response and recovery,” says Steinmann.
Cities can overcome resource constraints by working with private sector stakeholders and research institutes to focus on more cost-effective solutions that offer demonstrable measurable benefits, such as AI-powered early warning systems.
“Starting with a pilot project, focusing on one hazard type like a storm and working directly with private companies and research centers will help us demonstrate the value for a wider adoption and build momentum,” Steinman said.
She added that development banks, insurance companies and financial institutions are increasingly encouraging AI-driven risk reduction strategies through flexible funding models and innovation funds.
