Artificial intelligence (AI) and machine learning (ML) are becoming increasingly important tools for predictive maintenance across the power industry, helping utilities identify potential equipment failures before they disrupt operations.
According to GlobalData’s latest Strategic Intelligence: Predictive Maintenance in Power (2026) report, utilities are combining sensor data, inspection imagery and operational histories with AI and ML to understand the normal behaviour of critical assets and identify early signs of deterioration.
For facilities management teams responsible for complex utility estates, the shift could enable maintenance to move further away from fixed schedules and reactive interventions towards more targeted, condition-based strategies.
Companies including Ørsted, Florida Power & Light and National Grid are already enhancing predictive maintenance capabilities using AI and ML, according to GlobalData. The technology is being used to predict failure probabilities, optimise maintenance schedules and improve outage planning.
Energy tracking is also emerging as an important reliability metric. By comparing expected and actual output, operators can identify deteriorating equipment before a failure occurs and assess whether maintenance interventions have delivered the expected improvement.
Rehaan Shiledar, Power Analyst at GlobalData, said translating asset condition information into expected energy losses could help organisations prioritise maintenance according to operational and financial impact.
Digital twins and augmented reality (AR) are another area attracting attention. Digital twins can provide continuously updated virtual representations of physical assets, while AR can give engineers access to relevant diagnostics, asset information and maintenance procedures while working in the field.
GlobalData highlights GE Vernova’s use of digital twins for power generation equipment alongside wearable AR and immersive headset technology for technicians. Siemens is similarly combining digital twin and AR technologies to connect physical assets with virtual information.
The report also identifies carbon pricing as a potential driver of predictive maintenance investment. Deteriorating equipment can increase fuel consumption and energy losses, meaning inefficient assets could result in additional emissions-related costs as well as higher maintenance expenditure.
The expansion of renewable generation is strengthening the case further. Distributed wind and solar assets make remote condition monitoring particularly valuable, while increasingly complex power flows place additional importance on maintaining grid reliability.
Shiledar said utilities and generators are accelerating predictive maintenance investment as they seek to improve the reliability of ageing assets while controlling operations and maintenance costs.
With IIoT sensors, edge computing and analytics becoming easier to deploy, GlobalData expects predictive maintenance to progress from individual pilots towards fleet-wide programmes, supported by growing safety, regulatory, ESG and operational resilience requirements.