Analysis: Why AI Has Yet to Remake China’s Energy Sector

China’s energy industry is witnessing a gradual uptake of artificial intelligence (AI) across various sectors such as oil, gas, power grids, and mining. Despite the push to harness AI for cost savings, increased efficiency, and safety, a large-scale digital transformation remains distant due to several persistent challenges including high investment costs, questionable returns, a shortage of specialized talent, and ongoing data security concerns [para. 1][para. 2].

Industry experts note that while AI is slowly being adopted, its current applications are somewhat limited and have not yet disrupted the sector’s established business models. The main obstacles to widespread AI implementation include a lack of professionals who possess both AI expertise and in-depth knowledge of the energy sector, limiting the technology’s use in high-value and high-risk operations. Furthermore, concerns over the accuracy of AI in critical processes and data security are inhibiting deeper integration into core systems [para. 3][para. 4][para. 5].

Financial considerations further slow AI uptake. The technology’s significant upfront costs, encompassing research, development, and the training of large language models (LLMs), lead many companies to proceed cautiously. Energy firms are especially uncertain about the return on investment of AI projects. Industry insiders, like Lü Yan of Deloitte China, argue that for AI to reach industrialization, it must achieve cost parity with existing solutions and offer sustainable business models, a target that might take another five years to realize [para. 6][para. 7][para. 8][para. 9][para. 10][para. 11][para. 12].

Talent development is another key hurdle. China currently lacks a sufficient workforce skilled in both AI and sector-specific domains such as chemicals and clean energy, with training systems for such hybrid expertise still in their infancy. This talent shortage restricts the application of AI in specialized fields, constraining broader industry growth. Nevertheless, experts recognize AI’s potential to revolutionize sub-sectors, especially in geological exploration and the acceleration of new material development. Shortening innovation lead times, especially in technologies like battery design and carbon capture, is seen as crucial for achieving sustainability and competitiveness in energy [para. 13][para. 14][para. 15][para. 16].

Reliability and data security present additional barriers. Energy companies require high levels of data accuracy and reliability, which current AI models are not always able to guarantee. As a result, AI is often used for supporting tasks, such as identifying mineral deposits or predicting geological and environmental risks, but not yet for core decision-making. Industries like chemicals, where processes are highly specialized and error tolerance is low, remain particularly challenging for AI implementation due to customization costs, concerns over precision, and the risk of security breaches [para. 17][para. 18][para. 19][para. 20].

Companies are responding by creating restricted “data silos” and internal AI training centers disconnected from external networks to reduce security risks. There is also a growing need for industry-wide data standards and robust mechanisms for collecting, updating, and ensuring the quality of data. Experts advocate for these standards, alongside unified LLM frameworks and supportive policies, to address data reliability issues and unlock the wider potential of AI in China’s energy industry [para. 21][para. 22][para. 23][para. 24][para. 25][para. 26][para. 27].

In summary, while AI adoption in China’s energy sector is slowly increasing, it is hampered by significant cost, talent, and data challenges. Looking forward, industry-wide standards, increased investment in hybrid talent, and improvements in data security and management will be key to achieving broader transformation [para. 1-27].