Supercharging utility inspection operations with AI image analysis; pricing and benefits.

In 2025, AI image analysis is no longer an emerging concept — it’s a practical tool transforming how utilities handle inspections at scale.

At Scopito, we’ve been at the forefront of this shift — not with one-size-fits-all algorithms, but with customer-specific AI tuned to each client’s assets, workflows, and risk priorities.

That specificity matters. It’s why we can’t simply publish a generic “AI benefits” list — because the real gains depend entirely on your data, your assets, and your operational goals.

This case study on AI image analysis offers a grounded look at what’s possible. It’s based on a large-scale pilot project with a major South American utility aiming to inspect 800,000 poles annually. By pairing targeted AI image analysis with Scopito’s platform, the utility has turned an overwhelming manual task into a fast, accurate, and repeatable process.

Goal

In 2025, the utility’s goal remains clear: reduce downtime and extend asset life by inspecting 800,000 of their 5–6 million poles and towers annually. This coverage would allow them to detect issues early, prioritize repairs, and make data-driven maintenance decisions that protect both reliability and asset value.

Solution

Like most large-scale AI inspection initiatives, this project began with a six-month pilot to prepare for full-scale rollout.

• Data capture (2 months): Planning flights, coordinating resources, and collecting imagery — with built-in buffer for weather delays or other disruptions.
• Manual analysis (2–3 weeks): Human reviewers annotate faults in the captured images to create high-quality training data.
• AI training (2–3 months): Algorithms are trained on the annotated data until accuracy exceeds 90%.

For this pilot, Scopito developed algorithms using images representing just 0.04% of the utility’s total assets. Fault types targeted include:

• Insulator damage
• Encroaching vegetation
• Broken cross-arms
• Bird and bee nests
• Sagging guy wires
• Woodpecker holes
• Top cap issues
• Rotten wood
• Thermal anomalies
• Failing concrete

The exact areas of interest depend on the faults uncovered during the manual annotation phase.

By integrating AI image analysis, the utility can scale inspections year after year while extending asset lifespans. At the same time, they’re building a detailed digital twin of their entire network—an asset for both today’s maintenance planning and tomorrow’s predictive strategies.

Increased analysis speed

Speed is what makes large-scale AI inspections possible. Manually analyzing images of 800,000 structures simply isn’t realistic.

In 2025, Scopito’s AI image analysis processes 1,000+ images per hour, compared to about 175 images per hour for a skilled human analyst. With an average of two images per structure, that’s 1.6 million images per year.

For a human team working around the clock, analysis would take 380 days.

In comparison, it would take Scopito AI 66 days—and its processing capacity can scale further without significant added cost.

This speed advantage not only shortens inspection cycles but also accelerates the time from fault detection to repair, directly reducing outage risk and maintenance backlogs.

*AI processing capacity can be scaled up quickly to handle larger workloads without driving up costs.

Added accuracy with AI

Speed is only half the advantage—AI image analysis delivers consistency that human teams simply can’t match. It doesn’t get tired, distracted, or influenced by external factors.

With high-quality, well-labeled training data, accuracy quickly surpasses 90%, and as more data flows through the system, it continues to improve. In this project, accuracy is expected to exceed 95% once operations reach full scale—delivering reliable fault detection across millions of assets.

Extend asset lifetime

Faster, more reliable inspections mean issues are caught earlier—often before they lead to costly failures. That gives maintenance teams the head start they need to fix problems while they’re still minor.

Over time, as the AI processes more inspection data, it can identify patterns that signal emerging risks. This predictive maintenance capability allows utilities to prioritize repairs, plan budgets more effectively, and extend the operational life of their assets.

Visual fault presentation

Not all AI image analysis delivers the same value — the real impact comes when it’s paired with a visualization platform like Scopito.

For utilities managing millions of assets, visualization isn’t a “nice to have” — it’s the only way to maintain control at scale. Without it, the data from AI inspections quickly becomes overwhelming. The more faults you detect, the harder it is to prioritize and act.

Scopito solves this by delivering clear, intuitive overviews at every level:

• A color-coded map of all assets for instant health status.
• A line-map view highlighting encroaching vegetation, damaged components, or other faults.
• A detailed asset-view for pinpoint analysis and action planning.

By turning massive datasets into logical, actionable views, we make AI insights usable. Without that, AI is just numbers — with it, AI becomes a decision-making tool.

Your Quick-Reference Version

Want the key takeaways in a format you can save or share?
We’ve turned this case into a concise, 3-page PDF that’s perfect for:

• Quick internal reference during project planning
• Sharing with decision-makers who need the highlights fast
• Including in proposals or presentations to show real-world results

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Original article by Ditte R. Lønstrup. Updated in 2025 by: Gayle Godkin, Scopito’s Marketing Representative, who specializes in the commercial drone inspection space and helps communicate the value of visual inspection data to infrastructure industries.

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