How AI-Enabled Visual Intelligence Drives Grid Resilience

Electric utilities routinely collect visual data from drones, helicopters, satellites, and ground-level inspections. It’s standard practice.

Yet many times, the said visual data remains underutilized due to sluggish manual processes and infrequent reviews. This is an enormous, missed opportunity, as when properly analyzed and applied, visual data can be used to improve operational decision making and grid performance.

This results in increased disruptions for thousands of customers. Think about trees falling on lines that should have been flagged as risks long ago, field crews dispatched without proper ground insight, and asset records that simply reflect how the grid looked at the time of commissioning (versus how it looks in a particular moment).

AI-enabled GridOS® Visual Intelligence from GE Vernova presents a tangible solution to address this situation. By fusing visual data at scale with AI, Visual Intelligence is enables utilities to know more, act faster, and build a more resilient network.

At its core, Visual Intelligence is a cloud-based software platform that helps utilities leverage visual data at scale. Think of it as giving every part of your grid a set of eyes and then using AI to make sense of everything those eyes see.

The visual data inputs are broad. They include imagery captured by drones and helicopters, LiDAR point clouds, satellite data, and even ground-level capture from vehicles or smartphones. Any source that produces a visual record of the grid and its surrounding environment is relevant.

What the platform produces is equally powerful, – namely an accurate, continuously updated 3D digital twin of the grid. This "dynamic" model reflects the real-world condition of infrastructure, including poles, conductors, insulators, vegetation, and terrain, with a level of detail and currency that traditional asset records simply cannot match.

AI enables automation of previously manual processes, – notably in this case, analyzing imagery for critical visual insights. With it, utilities can automatically detect a missing insulator, identify a tree growing dangerously close to a conductor, flag a rusting pole, or spot a component showing early signs of failure across thousands of miles of network, in a fraction of the time it would take a human team. Without AI-powered visual intelligence, processing the volume of visual data a modern utility generates would be impossible.

Visual Intelligence is not a new concept. Utilities have been collecting aerial and drone imagery for years. So why is adoption accelerating now? Three converging forces have created a tipping point.

Storms, wildfires, and extreme weather events are no longer rare disruptions; they are now a recurring operational reality. Recent storms across Scandinavia brought this into sharp focus. Utilities operating without a real-time, AI-analyzed view of their grid found themselves working blind during and after the event, unable to efficiently route crews, verify damages, or consolidate the true cost of recovery.

The takeaway is clear: proactive risk mitigation requires visibility. Knowing which trees are structurally compromised, which areas are prone to flooding, and which assets are most vulnerable before a storm arrives is the difference between properly managing an event and being completely overwhelmed.

A decade ago, collecting high-resolution LiDAR data across an entire network was prohibitively expensive. Today, drone technology, satellite imagery, and airborne sensors have made visual data collection faster, cheaper, and more comprehensive than ever before. Angles and resolutions that were previously impossible are now routine.

The challenge is no longer collecting the data — – rather, it’s organizing, processing, and extracting actionable intelligence from it. That’s a core capability of AI.

Processing petabytes of visual data to identify specific defects, measure vegetation clearances, and update asset records requires a level of computational power and algorithmic sophistication that simply did not exist at commercial scale until recently. Today it does.

One GE Vernova customer in the United States monitors more than 50,000 miles of grid through Visual Intelligence, processing close to a petabyte of data. That level of analysis was not feasible even a few years ago. Today, it is, – and it fundamentally changes how utilities can operate.

Visual Intelligence is not a single-purpose capability but a foundational layer. Once a utility builds an accurate, AI-derived, continuously updated model of its grid, that digital foundation enables a wide range of high-impact operational use cases. Each directly improves resilience, reliability, and efficiency. Here’s a summary of Visual Intelligence’s core use cases:

What makes these use cases particularly powerful is their wider benefits across different solutions. At the core of Visual Intelligence is the 3D grid twin that brings more value, accuracy, and precision to the network model. Subsequently, all core grid applications running off the network model are further empowered by Visual Intelligence’s addition of visual insights on the state of the grid.

Over the years, some of the world’s most respected utilities have seen remarkable benefits with Visual Intelligence, including:

Ready to see Visual Intelligence in action? Contact the GE Vernova Grid Software team to request a demo or learn more about how Visual Intelligence can work for your network.

Based on GE Vernova webinar "Driving Grid Resilience with AI-Powered Visual Intelligence"