Why AI accessibility matters to rural and regional power utilities

Imagine you’re in charge of a small, rural utility company, responsible for hundreds of kilometres of power lines that snake through forests, valleys and mountains. You have maybe one hundred and fifty employees, infrastructure built in the 1930s, and a major storm on the horizon. As rain starts to fall, you flash back to the work done on system hardening, consider remote roads and resources, and think about the communities that depend on the power you provide. 

How is an outage going to impact them? Using technology innovation to improve reliability is not an opportunity reserved for the largest investor-owned utilities. Rural and regional operators face the same reliability challenges as the big players, but with fewer people, smaller budgets and often more limited resources. For these utilities, it’s essential to make targeted technology investments to maximise reliability improvements.

Some electric cooperatives have discovered a winning formula, using new technology to modernise an existing core process  – modernising inspections - with aerial image capture and AI-enabled image analysis, allowing these utilities to modernise on their own terms, without breaking budgets or disrupting operations.

Challenges of rural and regional utilities

The first is geography. Service areas are vast and dispersed, and sending a crew on a service call can require long travel times. Harsh terrain adds to the challenge, slowing inspection times and increasing safety risks. 

Equally pressing are the resource constraints. The 2025 EY Future of Energy Survey reveals that while 92% of energy companies plan digital investments, only 27% currently retrain staff for new tech demands. That’s a problem for all utility companies, but especially smaller ones. Many rural utilities run lean, where one person may juggle inspections, reporting and maintenance themselves. 

Tools must be user-friendly out of the box (drag-and-drop uploads, simple tagging, etc.) because in rural power, there’s just no time for lengthy pilots or mass training.

And the stakes are high. According to Deloitte, there were a record 27 extreme weather events in 2024, each costing more than $1 billion in recovery. A single equipment failure can darken entire communities for days, and can cause disproportionate disruption in rural areas where repair crews are already stretched thin. Prioritisation tools like condition tagging and severity scoring already help, but AI promises to take triage to a new level of efficiency.

Drones and AI a game changer

Adopting drones and AI-enabled analysis software has a measurable impact on inspection efficiency. Drones are five to ten times faster than traditional methods, and the imagery they capture typically uncovers five times more defects, all while cutting the carbon footprint of inspections by over 90%. 

Once captured, the data can be processed at scale: for instance, manually reviewing and annotating 33,538 images covering 340km of lines would take a single inspector about 28 weeks (assuming two minutes per image and a standard 40-hour workweek). With AI-enabled software, that same analysis can be completed in just days – dramatically accelerating decision-making while reducing both cost and risk.

Together, drones and AI shift inspections from a manpower-intensive, reactive process to one that is proactive and efficiency-driven. Recent Gartner analysis shows that 40% of power and utilities providers will deploy AI-driven operators in control rooms by 2027, and the industry has already been steadily increasing investments in data infrastructure, advanced analytics and cloud-based services to prepare for this transition.

For rural utilities, the benefits are especially clear: detecting hairline cracks or early corrosion before outages; reducing image review times from months to days; setting maintenance priorities based on real asset condition rather than estimates; and limiting dangerous pole climbs through remote data capture. Modernising inspections also supports knowledge transfer – capturing and codifying the insights of experienced linemen before they retire. 

Additionally, centralised, AI-powered data management makes it easier to analyse trends and compare current inspections with historical data side by side, enabling better long-term planning. For smaller operators, these capabilities offer a practical, high-impact way to improve reliability within the realities of limited staff, funding and time.

Practical advice for small utilities

Modernisation may sound daunting for those with less resources, but the path forward can be manageable with the right mindset.

Here are four practical steps for rural and regional utilities just getting started.

1. Audit current inspection processes. Map out how images and reports move from capture to decision-making. Identify bottlenecks or inefficiencies.
2. Start a drone and/or data collection programme. Get the licenses, buy drones, develop flight plans and standardise shot lists and metadata tags.
3. Design the end-to-end workflow. Centralise data storage, integrate structure with GIS, image analysis with human-in-the-loop tools, push results to work order systems.
4. Create metrics to measure ROI. When issues are detected and repaired before outages SAIDI/SAIFI metrics will improve plus O&M costs are reduced.

Big advantage of being small

Anyone who has ever been doubted because of their size will tell you that there are benefits to being overlooked and counted out – qualities like grit, resourcefulness and pride. Rural and regional operators might be smaller and leaner, but they also have fewer entrenched systems, more autonomy and the ability to pivot faster. And despite physical distance, smaller utilities are often more connected to the needs of the communities they serve, giving them an added layer of motivation to come through for their neighbours. 

New aerial unmanned systems and accessible AI-ready platforms mean even the smallest utility can start building a smarter, safer, and more resilient grid today. And with each step, they prepare for the future – one where predictive analytics and automation are no longer the privilege of the largest players, but the everyday tools of community-driven power providers.

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