Energy, natural resources, and chemicals industry leaders are increasingly channeling investments into artificial intelligence to enhance operational efficiencies, curb emissions, and foster growth amid market volatility. A recent KPMG report highlights a significant shift, with 65% of CEOs in these sectors now identifying generative AI as a top investment priority, a notable increase from the previous year. This growing confidence is underscored by the expectation of substantial returns, with 66% of executives anticipating a return on their AI investments within a one to three-year timeframe, a dramatic rise from just 15% in 2024.
The strategic integration of AI has moved beyond experimental phases to become a core component of corporate strategy for many energy companies. This digital transformation is seen as pivotal for navigating the complexities of the global market, including geopolitical tensions and regulatory hurdles. The report indicates that 72% of CEOs plan to allocate between 10% and 20% of their budgets to AI initiatives in the coming year. The industry’s heightened confidence in its medium-term prospects, now at 84%, reflects a strong belief in the transformative potential of AI to create sustainable value and build more agile, future-ready operations.
Optimizing Operations and Driving Efficiency
Artificial intelligence is fundamentally reshaping the oil and gas sector by delivering transformative gains in productivity, accuracy, and efficiency across both core operations and enterprise functions. AI-powered tools are being deployed to optimize drilling and reservoir performance, maximize profitability in refining and renewable assets, and streamline supply chains and financial processes. By enabling faster, data-driven decisions, AI helps to mitigate risks and enhance the overall value chain. The application of sophisticated machine learning and predictive analytics can significantly improve energy efficiency and generate substantial economic benefits, with some estimates suggesting that AI-driven energy efficiency measures could create as much as $1.3 trillion in economic value by 2030.
The impact of AI extends to the very core of energy production and management, where it is proving to be an indispensable asset. For instance, the Abu Dhabi National Oil Company (ADNOC) leveraged AI to achieve $500 million in savings in 2023 alone, while also reducing carbon emissions by one million tonnes. These achievements highlight the dual benefits of AI in both economic and environmental terms. As the industry moves from generative AI to more advanced agentic AI, more than half of ENRC CEOs expect these technologies to have a significant or transformational impact, particularly in operational and workforce efficiency.
Accelerating the Transition to Renewable Energy
The successful integration of renewable energy sources is critical for achieving global net-zero emissions targets by 2050, and AI is playing a vital role in this transition. AI technologies are being applied to improve the forecasting of wind and solar output with up to 90% accuracy, reducing the reliance on fossil-fueled backup plants and cutting balancing costs by as much as 20%. This enhanced predictability allows for more efficient scheduling of energy storage and dispatch, boosting the utilization of renewables by 15%. By optimizing the integration of intermittent energy sources, AI helps to create a more stable and reliable grid, which is essential for a low-carbon energy future.
AI is also instrumental in the development and management of renewable energy projects. Digital and analytical solutions enable companies to select the best sites for new projects, improving asset value and ensuring more cost-effective engineering. Predictive maintenance, powered by AI, helps to minimize downtime by identifying potential equipment failures before they occur, thereby extending the life of assets such as wind turbines and solar arrays. In the context of energy storage, AI algorithms can determine the optimal times to store and release energy based on grid demand, weather forecasts, and electricity pricing, further enhancing the efficiency of renewable energy systems.
Advancing Sustainability and ESG Goals
Sustainability is no longer a peripheral concern for energy companies but a central element of their business strategy, with 72% of CEOs confirming that ESG principles are embedded in their corporate plans. AI is emerging as a critical tool for advancing these sustainability objectives. A significant 82% of CEOs believe that AI can support emission reductions and help optimize energy use, while 74% see its potential in boosting climate risk analytics to better model future scenarios. This growing consensus highlights the integral role of AI in the broader energy transition and in achieving long-term climate goals.
The decarbonization potential of AI is substantial, with studies suggesting that it could help reduce global greenhouse gas emissions by 5-10%, an amount comparable to the total annual emissions of the European Union. This is achieved through a variety of applications, from optimizing the integration of renewable energy to enabling the development of microgrids and decentralized energy systems that reduce dependence on fossil fuels. AI is increasingly viewed as the operating system for the energy transition, facilitating everything from real-time grid balancing to predictive maintenance. To fully realize this potential, however, energy leaders must focus on integrating secure, high-quality data and strengthening ESG governance to ensure that AI-driven initiatives deliver measurable and sustainable outcomes.
Navigating Implementation Challenges and Ethical Concerns
Despite the clear benefits of AI, its widespread adoption is not without challenges. A majority of executives recognize the need to keep pace with AI but also point to several significant barriers to implementation. Ethical concerns are cited by 55% of CEOs as a primary hurdle, followed by fragmented data systems (49%) and regulatory complexity (47%). These challenges necessitate careful navigation to ensure that AI is deployed responsibly and effectively. The need for a suitable governance framework that aligns with ethical principles and international law is paramount, particularly as the technology continues to evolve.
The complex global landscape further complicates the adoption of AI in the energy sector. Companies must navigate varying regulatory environments around the world, each with different appetites for the energy transition. This requires a nuanced approach to AI strategy, one that is adaptable to local contexts and regulations. Moreover, the increasing demand for electricity from AI-relevant data centers presents its own set of challenges, as the need for continuous power can make it difficult to phase out fossil fuel-based generation completely. Addressing these issues will require collaboration between corporations and governments to upgrade grid infrastructure and ease permitting for renewable energy projects, ensuring that the growth of AI does not undermine progress toward net-zero goals.