Solutions
Digitalization from POC to Deployment: Five Challenges to Prepare For
October 2025
Within the energy industry, maintenance has long been based on regular inspections, oil samples, and manual checks. It works – but also leaves much to chance. Unexpected downtime remains a costly reality.
With AI-based monitoring, the conditions are changing. By analyzing large amounts of data from sensors in real-time, it becomes possible to detect early signs of problems – long before something breaks down.
Instead of relying on fixed intervals, AI can identify subtle changes in, for example, gas levels, temperature patterns, or vibrations. The systems learn what is normal – and react when something deviates.
Real-time gas analysisreveals if components inside are beginning to deteriorate.
Temperature monitoringshows if cooling is functioning as it should – and warns of overload.
Vibration monitoringcaptures abnormal patterns that may indicate mechanical faults.
This isn't about replacing humans – but about giving operations and maintenance teams better decision-making support.
Several major grid companies worldwide have already implemented AI-based systems. The experiences are clear: fewer operational disruptions, better resource planning, and closer collaboration between operations, maintenance, and analysis.
But perhaps the greatest benefit isn't statistical. It's the shift in working methods – from firefighting to being able to act proactively. This increases reliability, safety, and reduces stress for personnel.
Preventing failures in critical infrastructure not only reduces the risk of costly interruptions – it provides better control over resources, higher availability, and greater security in delivery. For many operators, it's also a key to meeting regulatory requirements and achieving sustainability goals.
Many who venture into AI monitoring soon discover that it requires more than just hardware and algorithms. Data quality is crucial. So is the human perspective: building trust in the systems and creating understanding of their benefits.
For companies working with predictive maintenance and sensor-based analysis, this becomes especially clear. If sensors are placed incorrectly, if data is skewed, or if certain environments aren't represented, there's a risk of missing precisely those early signals that the technology is intended to capture. Successful projects therefore invest as much in skills development as in technical solutions.
With technologies like digital twins, edge computing, and federated learning, development is moving rapidly – and the possibilities are many.
For grid companies, energy producers, and large electricity consumers, AI represents a key to increased reliability, efficiency, and sustainability. Predictive maintenance is moving from being a future promise to becoming a practical tool – for an electrical grid that meets the demands of both electrification and climate goals.
Want to know more? Read about how we build our AI solutions.