毕马威发布《智能能源——人工智能驱动转型与价值重塑》：解码AI赋能能源转型的密码

Core Insights - The energy industry is currently facing three main challenges: ensuring safe and reliable supply, accelerating decarbonization, and maintaining cost control. Artificial intelligence (AI) is emerging as a key solution, deeply integrated into the strategic planning of the energy sector [1][3] - A report by KPMG highlights the latest advancements in AI applications within the energy sector, based on a survey of 163 executives from medium to large energy companies across eight countries [1][3] AI Application in the Energy Sector - There is a significant growth in AI applications within the energy industry, with 56% of companies expanding their AI projects and 44% integrating AI into core operations. AI is viewed as a critical factor for optimizing operational processes [3][5] - 79% of surveyed companies have achieved measurable efficiency improvements through AI, with 60% reporting returns exceeding 10% on their investments. Looking ahead, 92% of respondents plan to increase their investments in AI projects, although they do not expect short-term returns [3][5] Investment and Implementation Strategies - Companies are investing heavily in AI technology across various areas, including hardware upgrades, software procurement, data infrastructure transformation, and talent acquisition. This investment is crucial for large enterprises [5][6] - AI projects in the energy sector are categorized into two types: value-driven projects aimed at maximizing efficiency and returns, and purpose-driven projects focused on enhancing safety and sustainability [6][8] Operational Challenges and Transformation - Energy companies face multiple challenges, including aging infrastructure, rigid operational models, and stringent regulatory requirements. Successful AI implementation requires comprehensive capability building at foundational, functional, and enterprise levels [10][12] - Establishing transformation management offices or AI centers of excellence is essential for ensuring alignment and consistency in AI strategy and project delivery across all levels of the organization [10][12] Future Directions and Ecosystem Development - The transition to AI in the energy sector is expected to evolve through three stages: from automation to autonomy, changing interactions between companies and customers, and accelerating decarbonization and innovation [12][19] - Companies are actively building broader intelligent ecosystems that facilitate collaboration among customers, competitors, regulators, suppliers, and technology partners [12][19] Generative AI in China’s Energy Sector - China's energy sector is leading in digital infrastructure development, with significant data assets, although the maturity of intelligent applications remains relatively low. Generative AI is still in its early stages but holds substantial potential [12][19] - By the end of 2024, 13 state-owned energy enterprises are expected to release 25 vertical industry models, with over 28 applications in areas such as production optimization and fault prediction [12][19]