APPLICATIONS
Manish Kumar, Executive Vice President of Digital Energy at Schneider Electric, explains:“ AI enablement adds significant value in complex, data-rich environments such as hospitals, airports, university campuses, large corporate offices and urban centres, where predictive maintenance, dynamic load balancing and autonomous optimisation can drive measurable efficiency and resilience.”
Implementation challenges Despite compelling benefits, predictive maintenance deployment faces real obstacles. Many legacy systems lack necessary sensors or digital interfaces, requiring retrofitting or data translation layers. Cultural resistance can emerge as maintenance teams unfamiliar with AI-driven workflows require clear training and return-on-investment demonstrations.
Predictive models must be customised to adapt to highly-variable equipment conditions, while upfront investment in infrastructure, sensors and AI platforms can be substantial.
Successful organisations adopt a phased approach, starting with pilot programmes on high-impact assets before scaling up gradually with modular architectures. Continuous model retraining ensures accuracy over time, while crossfunctional collaboration between IT, maintenance and operations embeds predictive analytics into everyday workflows.
68 March 2026