By 2027, 40% of control rooms in the power and utilities sector will deploy AI-driven operators, according to Gartner. This shift aims to reduce human error while improving efficiency, though it may increase vulnerabilities in cyber-physical system security.
The findings stem from Gartner’s predictions and its 2025 CIO and Technology Executive Survey, which revealed that 94% of chief information officers (CIOs) in the power and utilities industry plan to boost AI investments in 2025, with an average spending increase of 38.3%.
“AI technology is poised to transform the power and utilities sector,” said Jo-Ann Clynch, Senior Director Analyst at Gartner. “Human decision-making is critical, but it is also a significant factor in industrial accidents. AI-driven operations offer a compelling solution, performing tasks with repeatability, precision, and without bias when effectively governed.”
The power and utilities industry is undergoing a fundamental shift, moving from traditional utility-owned models to decentralised systems driven by distributed energy resources like solar panels and energy storage. This shift enables dynamic, circular behaviours, where customer-owned intelligent assets can optimise objectives such as cost, production, and comfort.
“Power and utility CIOs must focus on creating intelligent operations to integrate these assets into their digital ecosystem,” said Clynch. “Investing in data infrastructure and analytics, transitioning to cloud-based services, and preparing for AI integration in control room operations will help them succeed in this new competitive landscape.”
AI-driven operators in control rooms are expected to enhance efficiency through real-time data processing, predictive maintenance, and automated anomaly detection. However, this adoption introduces new security risks, requiring substantial investment in advanced measures and adherence to evolving regulations.
For power and utility CIOs to fully leverage AI-driven decision intelligence, Gartner suggests prioritising operational efficiency, bolstering cyber-physical system security, and fostering human-AI collaboration.
Comprehensive AI integration plans should identify areas where AI can reduce human error and implement robust governance for monitoring and risk management. Additionally, investing in advanced security practices, such as quantum-enhanced measures, is essential to address vulnerabilities introduced by AI systems.
Finally, training control room staff to work effectively with AI technologies is critical for maintaining oversight and interpreting AI outputs, ensuring that critical thinking remains a central component of decision-making.
