AI Drives Energy Sector Toward 50% Autonomous Operations by 2030, Study Finds
A new global study finds the energy sector is accelerating toward autonomous operations, driven by AI, rising power demand and workforce challenges.
(P&GJ) — The global energy sector is rapidly accelerating toward autonomous operations, with companies targeting nearly 50% full automation by 2030 as artificial intelligence (AI) reshapes performance and operational strategy, according to a new study from Schneider Electric.
The report, based on a survey of 400 senior energy and chemicals executives across 12 countries, shows the industry nearing a tipping point, with roughly one-third of operations already fully autonomous and average autonomy levels at about 70%.
Executives cited rising cost pressures, workforce shortages and increasing demand for reliable energy supply as key drivers behind the shift. Nearly 60% warned that delaying adoption would increase operating costs, while more than half pointed to talent constraints as a growing risk.
The push toward automation is closely tied to surging power demand, particularly from AI-driven applications and expanding data center infrastructure. Global electricity demand is projected to approach 1,000 terawatt-hours by 2030, placing additional strain on energy systems and increasing the need for more efficient operations.
“Globally, organizations already report operating at 70% autonomy, with plans to hit 80% by 2030,” said Gwenaelle Avice Huet, Executive Vice President, Schneider Electric. “Autonomy is rapidly becoming the new operating model of industry. As AI advances and energy systems come under growing pressure, autonomous operations are proving essential for resilience and competitiveness. And this shift isn’t about replacing people, it’s about empowering them to focus on higher value work, strengthening safety, and elevating skills. Those who scale now will shape the next era of industrial performance.”
AI was identified as the leading driver of this transition, with nearly half of respondents citing it as the primary enabler of autonomous operations. Other contributing technologies include cloud computing, digital twins, advanced process control and cybersecurity systems.
“The report finds the adoption of autonomy in the sector to be more advanced than expected, with open, software-defined automation essentially leading the next phase of energy innovation”, added Gaurav Sharma, Independent Energy Market Analyst and contributor to the research. “In a sector where reliability, safety, and carbon reduction are now non-negotiable, these technologies are emerging as the most effective way for operators to deliver ‘more with less’ and run more resilient and competitive operations.”
Regional differences remain, with the Gulf Cooperation Council and Asia leading current adoption levels. North America, however, is expected to see the fastest growth in the coming years, driven by large-scale energy production and rapidly expanding data center demand.
“Autonomous operations are redefining how energy and chemicals companies run their entire facilities, and Schneider Electric and AVEVA are at the forefront of that shift, supporting customers such as Shell, European Energy, ADNOC and Baosteel on real-world deployments,” said Devan Pillay, President of Schneider’s Heavy Industries Segment. “By integrating Schneider Electric’s process control and power management with AVEVA’s digital technologies and industrial intelligence, we deliver integrated software-defined architectures that provide real-time visibility and enable AI driven digital twins that can predict, adapt and self-optimize with minimal intervention.”
Recent deployments highlight how automation is already being applied in the field, including upgrades to refinery operations and AI-supported clean fuel production systems.
Pipeline & Gas Journal will further explore the report’s findings and more in an upcoming podcast featuring Schneider Electric, providing additional insight into how automation and AI are reshaping energy infrastructure and operations.