See More, Spend Less: Extending Grid Asset Lifespan Through Visual Intelligence

It seems like at least half the blogs I read about the utility industry contain the sentence: “It’s never been more challenging to be a utility.”

Personally, I’d offer up my own variation on that sentence. How about: “It’s never been more expensive to be a utility.”

There’s no denying it: utilities face more expenses than ever before, particularly when ensuring grid resilience and reliability. And a big part of those expenses comes from asset maintenance and replacements.

It’s common knowledge that power assets are being strained to their limits. For example, the 2025 Report Card for America’s Infrastructure reports that "data centers alone are expected to demand 35 GW of electricity by 2030, up from 17 GW in 2022, growing roughly 10% per year."

A combination of increased wear-and-tear from soaring loads, more physical damages from severe weather, and the simple fact that assets are aging fast puts all assets (and by extension, grid resilience and reliability) at risk of causing a power grid disruption. Thus, utilities are increasingly setting aside millions of dollars in precious capital in preparation for asset replacements.

But a closer look at what’s driving this upward cost trend reveals something interesting. Many utilities are budgeting for asset replacements simply because the original equipment manufacturer (OEM) provided them with an asset lifespan at the time of purchase, and the end date of that lifespan is fast approaching. Naturally, utilities often take that date into account when calculating their asset lifecycles and replacement budgets – essentially assuming that an asset will no longer be safe or practical to use after that OEM-dictated expiration date.

But that isn’t necessarily true. While the OEMs would love to have utilities spend millions of dollars buying newer (read: more expensive) assets, asset expiration dates are theoretical at best. Think about cars. Even though auto warranties only last a few years, we all know that if we drive our cars responsibly, maintain them well, and clean them regularly, they can easily last us 10 years or more.

The same logic applies to power grid assets, although instead of regular oil changes, utilities can maximize asset lifecycles by investing in smart grid software that provides visual insights about asset health. These visual insights can help utilities unlock data-driven, condition-based maintenance, or the ability to identify and act on maintenance needs long before an asset fails. Such a workflow helps utilities realize capital deferment and OEM expense reduction.

Take GridOS® Visual Intelligence, for example. Within Visual Intelligence, grid analytics overlays multiple sources of data like scans, photos, and other imagery against network maps. It uses basic color-coding to identify any threats to grid resilience, then automatically generates and assigns detailed work orders for field crews. This workflow is perhaps best known for its utility vegetation management use case, which identifies hazard trees and other encroaching vegetation posing a risk of an outage.

Visual Intelligence’ capabilities can also identify assets in need of maintenance or replacement, a significant improvement in efficiency and effectiveness over traditional field inspections. And similarly, it can confirm when assets nearing their OEM-specified expiration date are, in fact, in good working order and can be kept longer. This automated monitoring of asset health contributes significantly towards grid hardening objectives by ensuring all assets are in good enough condition to withstand threats to resilience.

Consider the following outcomes of Visual Intelligence:

Lower O&M spend through AI-powered early detection

Anyone walking down the street can look up, spot a rusted-over transformer, and see that it needs to be replaced. But condition-based maintenance enabled by Visual Intelligence doesn’t allow issues to get to that point. By analyzing aerial or ground-based visual data – such as LiDAR, RGB, or thermal imagery – Visual Intelligence can identify the most subtle issues in their infancy (e.g. the very beginning stages of corrosion or the slightest fraying of a conductor strand).

Within Visual Intelligence, high-resolution imagery and AI identify micro-defects and degradation hints, flagging, prioritizing, and tasking them for intervention long before they escalate into catastrophic, high-cost failures. This contributes greatly toward longer asset lifecycles and reduced operational expenditures (think about the cost of replacing a single damaged insulator disc, versus replacing the entire insulator string).

Take a look at the below image, in which Visual Intelligence identified a rotting pole top that threatened the connection of the pole top pin (circled in red). Because of this early alert, the utility quickly dispatched a field crew to replace the top few feet of the pole and reattach the pin.

Without the early alert from Visual Intelligence, the pole would have continued to rot until the pin's screws had nothing left to grab, at which time the attached insulator would either (1) fall and smash on the ground or (2) dangle in midair, held only by its connection to the power line, potentially snapping the line completely. In either of those two scenarios, a power grid disruption would surely have resulted. Visual Intelligence ensured a swift fix long before a disruption happened.

Improved field execution

A frustrating disconnect often occurs when field crews respond to a service request. Sometimes the directions are incorrect. Sometimes the crew expected one type of issue and encountered something else entirely or find an issue with multiple potential remedies. Every minute crews spend out in the field costs utilities significant O&M expenses. It’s important to keep them focused and ready to execute the work properly and efficiently, every time.

As part of its work order dispatch workflow, Visual Intelligence provides the crew lead with a “damage profile” before the crew leaves the yard. The damage profile outlines the precise nature of the issue and the best way to fix it. This enables the lead to round up the right people, tools, and replacement parts, ensuring the crew arrives on scene prepared and ready to execute. Repairs and replacements can be done faster and more accurately, thanks to the detailed work order from Visual Intelligence. When the work is completed, managers can use Visual Intelligence’s post-work visual data acquisition to automatically perform quality checks on activities. These checks can help identify rework needs or, if the work appears to have been done correctly, enable an efficient transition to the billing workflow.

In addition, Visual Intelligence’s AI engine can reconcile a utility’s geospatial information system (GIS) with physical reality (“ground truth”) based on its imagery analysis. This contributes further toward field crew execution by ensuring the data provided to the crew is accurate and precise, every time.

Precise 3D grid twin

Every inspection within Visual Intelligence generates a wealth of data on assets and the larger network they comprise. AI automatically extracts and inventories hardware (transformers, insulators, etc.) directly from imagery or LiDAR point clouds, ensuring a 100% accurate asset registry in the form of a live 3D grid twin. Beyond improving accuracy and precision for field crews, there are numerous other uses for such a detailed grid twin, including simulations and scenario planning, regulatory audits, downstream software application enrichment, and insurance valuations, among others. The grid twin even hones its own accuracy through continuous “as-maintained” updates, for which it records the outcome of maintenance activities. This prevents technicians from being sent to repair a defect that has already been fixed or to a site where access conditions have changed, saving thousands of wasted labor hours per year.

The true value

Beyond individual asset interventions, the aggregate value of Visual Intelligence lies in what it enables at the portfolio level. When every asset across the network carries a granular, continuously updated health score, capital planning shifts from probabilistic estimation to more-precise evidence-based allocation. Budget cycles will no longer rely on OEM schedules or statistical degradation curves; instead they will reflect the actual condition distribution of the network. This gives asset managers the ability to identify where capital is urgently needed, where it can be safely deferred, and how to sequence multi-year investment plans to maximize infrastructure lifespan at the lowest possible expenditure.

With Visual Intelligence in-house, utilities can challenge theoretical-at-best OEM schedules with granular asset health data informing them of maintenance needs that will maximize asset lifespans. Utilities who rely on its robust analysis can keep viable assets in the field longer and avoid premature equipment procurement, saving potentially millions of dollars.

For more information on improving grid resilience with GridOS Visual Intelligence, take a look at our solution paper on the topic.