Across industries, electrical systems are already more connected, instrumented, and increasingly supported by AI-powered services. Sensors, digital platforms, and connected equipment continuously generate data about asset health, electrical events, and system performance.
This increased visibility has been a major step forward for operations. In the last few years, we have successfully moved from knowing what is happening in the system, thanks to alarms, and closer to being able to predict what might happen in the future with the advent of predictive analytics and condition based maintenance.
Yet for many organizations, a new challenge has emerged. It is no longer about accessing data – a challenge that AI-powered services are designed to address; it is about knowing how to make sense of it and turn it into practical actionable advice or a task.
Every day, thousands of alarms, alerts and predictions are generated across electrical infrastructure. Many are low priority, some are redundant, and a few are critical. Distinguishing between them in real time is not always straightforward. As a result, teams often spend more time sorting and interpreting data than acting on it.
Growing complexity is putting service models under pressure
