en.Wedoany.com Reported - A Deloitte study shows that by 2030, artificial intelligence (AI) could save the global energy system over $200 billion annually. The study points out the technology's strategic role in reducing costs, improving operational efficiency, and accelerating the global energy transition.
The study suggests that in Brazil, integrating AI into energy infrastructure could be a significant opportunity to accelerate decarbonization, enhance operational efficiency, and strengthen power system resilience. The country needs to expand access to innovative capital instruments, structure projects appropriately, promote regulatory security, and consolidate market awareness of the technical opportunities involved. Guilherme Lockmann, Deloitte's lead partner for Power, Utilities & Renewables, stated that coordinated action among investors, businesses, governments, and civil society is essential to building a robust ecosystem, enabling Brazil to become a protagonist in efficient energy systems and a low-carbon economy based on innovation, technology, and sustainable finance.
The study indicates that AI's annual savings potential could reach nearly $500 billion by 2050. The total economic benefit between 2030 and 2050 is estimated to be between $8.3 trillion and $11 trillion. Lockmann analyzed that AI has the potential to be a core economic driver of the energy transition, with the structural efficiency gains it brings—estimated at up to $11 trillion in benefits—potentially reducing the estimated cost of the global energy transition by up to 5%. This cost is currently close to $200 trillion, meaning AI could accelerate investment, enhance competitiveness, and make decarbonization more financially viable.
In the power sector, AI applications are mainly concentrated in three areas: power system optimization and control, asset lifecycle management, and end-use energy efficiency and management. In grid operations, advanced algorithms can balance supply and demand in real-time, reduce losses, and integrate intermittent renewable sources like solar and wind more safely and stably. In asset management, data-driven predictive maintenance helps anticipate failures, extend equipment life, and lower operational costs. On the consumption side, AI can analyze usage patterns and adjust industrial processes and building systems to maximize energy efficiency. Tim Wiesel, Deloitte's AI & Data partner, noted that AI's application in the power sector goes beyond optimizing specific tasks; it brings a revolution in operational and management models, making them more dynamic and adaptable to change, ultimately creating smarter energy systems capable of making autonomous, efficient decisions, resilient to failures, and aligned with the challenges of transitioning to a low-carbon economy.
The report projects that by 2030, AI could reduce energy consumption by approximately 2,700 to 3,700 terawatt-hours, roughly three times the amount the technology itself is expected to consume during the same period. By 2050, cumulative savings could reach nearly 12,000 terawatt-hours, representing about 10% to 12% of projected global energy consumption under a net-zero emissions scenario. By 2030, about 60% of the savings will come from the industrial and power sectors, totaling 1,550 to 2,210 terawatt-hours; by 2050, the power sector could dominate the gains, with a potential of 3,540 to 4,530 terawatt-hours. Reduced consumption will also impact greenhouse gas emissions. The study shows that AI-driven savings could avoid approximately 660 million tonnes of CO2 equivalent annually by 2030. As the energy system becomes more efficient and low-carbon, the annual marginal impact tends to decline, falling below 400 million tonnes of CO2 equivalent by 2040 and stabilizing at around 100 million tonnes of CO2 equivalent by 2050.
The report emphasizes that realizing this potential requires coordinated action from the public and private sectors to overcome challenges related to data quality, professional training, technical infrastructure, and governance models. Energy and manufacturing companies are seen as the protagonists in adopting AI, needing to prioritize high-quality data, cybersecurity, and governance, and invest in scalable applications with quick returns and a direct impact on operational resilience. Technology companies play a central role in innovating and adapting solutions to the energy industry's needs, particularly through integration with technologies like the Internet of Things and digital twins. Financial institutions can act as scaling enablers by supporting projects through instruments such as green bonds, sustainability-linked loans, and hybrid capital models. Luiz Paulo Assis, Deloitte's Infrastructure Advisory partner, explained that AI applied to energy systems is becoming a strategic element that can increase efficiency, reduce operational risks, and improve outcome predictability—key factors in attracting investors and lowering the cost of capital. Governments need to create a favorable regulatory environment, including clear standards, secure data sharing, investment in capacity building, and flexible frameworks that enable innovation without compromising system security and resilience. The study also points out that the development of AI in the energy sector should follow "Sovereign AI" principles, focusing on transparency, accountability, strengthening local capabilities, and protecting sensitive data to ensure solutions are reliable and serve the public interest.
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