AI-Driven Transformation in the Energy Sector Optimising Systems, Cutting Emissions, and Accelerating Innovation to Meet Global Sustainability Goals

According to data published by Precedence Research, AI is revolutionizing the energy sector by optimizing operations, reducing emissions, and supporting faster innovation. AI applications in oil and gas, electricity systems, and industrial sectors are projected to save billions annually and significantly improve energy efficiency. With expected energy savings of 8% by 2035, AI is pivotal in driving the transition to a more sustainable and resilient energy future.

Ottawa, Feb. 10, 2026 (GLOBE NEWSWIRE) -- AI in the energy sector is enhancing system efficiency, reducing operational costs, and accelerating innovation, according to data published by Precedence Research. The technology is expected to yield up to USD 110 billion in annual savings in electricity systems by 2035 and contribute to 8% energy savings across sectors. Despite challenges like regulatory hurdles and a skills gap, AI’s growing role in energy systems is critical for achieving sustainability and meeting global energy goals.

The energy sector, facing rising demand and an increasingly complex operational environment, is increasingly turning to Artificial Intelligence (AI) to optimise systems, reduce costs, improve production, and enhance resilience. With the global push for more sustainable energy solutions, AI's role in improving efficiency, cutting emissions, and supporting faster innovation is becoming crucial.

AI applications are transforming energy systems across multiple fronts from energy generation and transmission to end-use consumption. As AI-driven technologies rapidly advance, energy companies are deploying innovative AI solutions for tasks ranging from predictive maintenance to renewable energy integration, helping to meet both sectoral goals and regulatory targets.

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AI Applications Optimising Energy Systems

The energy sector is vast, complex, and rapidly evolving, driven by the electrification, decarbonisation, and digitalisation of global economies. AI applications in energy systems range from oil and gas operations to electricity grid management, where AI is instrumental in managing the complexity of supply, transmission, and demand profiles.

Key AI Applications in Energy Systems

• Oil and Gas Industry: AI has been deployed for subsurface data processing, reservoir simulation, predictive maintenance, and leak detection. By 2024, AI computing capacity in the sector had grown by almost 70% annually, showcasing the increased reliance on AI for exploration and production.
• Electricity Systems: In power plant operations, AI applications are projected to yield cost savings of up to USD 110 billion annually by 2035 from avoided fuel costs and lower maintenance costs. Additionally, AI can help unlock 175 GW of additional transmission capacity in existing grid lines, further improving energy efficiency.

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AI Applications Across Different Sectors

AI is driving significant energy savings in industrial applications, transportation, and buildings, with potential energy savings of 8% by 2035 across sectors like light industry and transport.

Sectoral Energy Savings (Widespread Adoption Case, 2035)

AI in industry, particularly in sectors like automotive, heavy-duty trucks, shipping, and aviation, is poised to yield significant energy efficiencies, especially in the long-term transition to electric vehicles and optimisation of transport systems.

Regional Variances in AI Adoption in Energy

While advanced economies lead in the adoption of AI technologies, especially in industrial automation and robotics, Asia is at the forefront of robotics adoption, hosting two-thirds of the top 40 global robotics companies.

The application of AI in energy innovation is increasing across all regions, but digital infrastructure gaps remain a challenge, particularly in developing economies, which may delay broader sectoral integration.

Leveraging AI to Accelerate Energy Innovation

AI is not just an enabler of efficiency improvements but also a major driver of energy innovation. Technologies like AI-powered simulations and predictive analytics are already transforming how energy technologies are developed and deployed.

In energy innovation, AI is playing a pivotal role in accelerating breakthroughs such as the development of more efficient solar technologies, like perovskite solar cells, where AI is being used to simulate and test a much wider range of materials than could be achieved through traditional methods. AI could drastically reduce the time it takes to discover new energy materials, a process that would otherwise take years.

AI-Driven Energy Innovation Progress:

Barriers to Widespread Adoption of AI in Energy

While the potential for AI in the energy sector is immense, several barriers exist to its widespread adoption:

• Regulatory challenges: Often, energy regulations do not align with the rapid deployment of AI technologies.
• Data access and interoperability issues: The energy sector often operates in silos, making it difficult to share data across systems.
• Skills gap: A shortage of AI-trained professionals and data scientists hinders the application of AI at scale.
• Digital infrastructure limitations: Many regions, particularly in developing countries, lack the digital infrastructure needed to support AI applications.

The energy sector is undergoing a profound transformation, and AI will play a crucial role in shaping its future. From optimising energy systems to accelerating innovation, AI is set to enhance efficiency, reduce costs, and improve resilience across the sector. While renewables are already leading the charge in providing cleaner energy, the role of AI in driving future advancements, such as the integration of small modular reactors (SMRs) and new energy storage technologies, will be vital in meeting global energy goals.

As AI adoption in the energy sector increases, it will help to solve complex energy challenges, unlocking new opportunities for a more sustainable and innovative energy system. However, addressing the barriers to adoption will be essential for realising the full potential of AI in energy.

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