From Reactive to Proactive: How AI Is Changing the Way Utilities Maintain Their Grid

For most of the utility industry's history, grid maintenance has been reactive by necessity. Crews respond to outages, address issues once they surface, and rely on inspection cycles that can leave months or years between visits to any given pole. 

The problem is what happens in the gaps: infrastructure doesn't fail on a predictable schedule, and issues that aren't yet causing trouble tend to stay invisible until they do. AI is starting to close that gap, giving utilities the ability to catch problems before they become outages, all while putting more capability in the hands of the field teams already doing the work.

A recent deployment with Florida Keys Electric Cooperative (FKEC) showcased the impact adding AI to your tech stack can have.

From Reactive To Proactive, At Scale

Utility inspection has always been limited by time, labor, and access. A lineman can only cover so much distribution line in a day, and visual inspections tend to happen in snapshots, months or sometimes years apart. Problems that aren't yet causing an outage often stay hidden until they do.

Our technology here at Noteworthy AI changes that. By collecting data at scale and automating the detection of assets and conditions on utility poles, it's now possible to baseline an entire service territory instead of sampling pieces of it. Every pole, every transformer, every crossarm gets captured, inventoried, and analyzed as a matter of routine.

FKEC deployed Noteworthy AI in February 2026, outfitting two camera systems on meter-reader vehicles already driving their territory every day. In just over four months, that setup has analyzed roughly 12,000 assets, covering about half of FKEC's existing pole locations, all without adding a single dedicated inspection vehicle or route.

For FKEC, that approach surfaced something that conventional inspection would likely have missed entirely: 86 loose transformer mounting bolts scattered across their service territory. Not one or two isolated cases. 86. Under a typical inspection cycle, problems like these usually don't get flagged until they've already caused an outage. This time, they were caught before a storm ever put them to the test.

"Having every asset captured with recent imagery lets us stay proactive, spotting conditions we would have otherwise never known about." says Nick Lyons, Director of Power Supply & Transmission at FKEC.

Human Expertise, Applied Where It Counts

Automated detection is a big part of what makes proactive maintenance possible, but it's not the whole picture. The imagery and asset inventory Noteworthy AI produces creates a new, far more efficient layer of work for the experienced staff already on the team.

Subject matter experts, acting as human-in-the-loop reviewers, can filter the full asset inventory down to specific components, poles with transformers, for example, and quickly review imagery for conditions that the automated models haven't been trained to catch yet. Work that would have taken weeks or months in the field now takes hours at a desk, with better coverage and a complete image record behind every finding.

That combination, automated detection plus expert review, is what turns a one-time inspection into an ongoing process. It puts the same team to work on higher-value problems, with better information in hand and more ground covered than manual inspection alone could achieve.

Why Freshness Matters

One finding from FKEC's program says a lot about why frequent data refresh matters. A transformer that showed no signs of trouble in imagery collected in December 2025 turned up with an electrical flash mark in the most recent scan. That's a condition that emerged in the gap between inspections, and it wouldn't have shown up in any older record.

"Noteworthy AI has been invaluable in helping us identify emerging issues in our imagery, so we can be more proactive instead of reactive." says Lyons.

Infrastructure doesn't wear out on a predictable schedule. Conditions develop between inspection cycles, and even a thorough one-time record can create a false sense of security. Refreshing imagery regularly is what turns an asset inventory from a static document into something utilities can actually operate on.

Resilience Before The Storm

For utilities in the Florida Keys, proactive maintenance isn't optional. When a hurricane or tropical storm makes landfall, distribution infrastructure takes on serious mechanical stress. A loose transformer mounting bolt is exactly the kind of small issue that turns into a single point of failure, and one that can knock out power for everyone downstream of it.

By resolving issues flagged through automated detection and expert review ahead of storm season, FKEC isn't just checking a box. They're building resilience into their grid, one pole at a time.

A Flywheel, Not A One-Time Fix

What's easy to overlook is how this kind of workflow compounds over time. Every condition an expert reviewer flags, confirms, or categorizes adds to the training data behind the next generation of automated detection models. The models improve, the automation covers more ground, and reviewers get to spend their attention on the next set of conditions the models haven't learned yet.

That improvement is what keeps proactive maintenance, proactive. A one-time inspection gives you a snapshot. A system that gets sharper with every pass gives you an asset inventory that keeps pace with a grid that's always changing.

The Takeaway

Grid maintenance doesn't have to mean waiting for something to break. For FKEC, shifting to a proactive model meant catching 86 potentially dangerous issues before they caused an outage, spotting an emerging condition on a transformer before it became a failure, and giving their team the tools to focus their expertise where it counts most.

This isn't a story about a single deployment. It's a story about what happens when utilities get ahead of their grid instead of reacting to it.

To learn more about Noteworthy AI, reach out to our team today.