| Transmission & distribution
Turning data into decisions: AI’s role in next-gen utility asset management
The utility sector faces a critical question: is it maximising AI’s potential or merely discussing it? Kristy McDermott Vice President of Sales at Sharper Shape
Utilities face significant challenges arising from ageing infrastructure, extreme weather, rising operational costs and increased demands for reliability. AI can help, but it’s time to move beyond theoretical discussions, the ‘what ifs?’, to embrace the practical benefits, the ‘what now?’. AI tools like ChatGPT have already laid foundations as household names, receiving both good and bad reviews for varying applications and being used for everyday tasks. But the world is also abuzz with what feels like everyone pushing their latest and greatest AI solution that promises the world. With this can come AI-fatigue. Without real, meaningful use cases setting out the benefits artificial intelligence can bring, we risk talking the talk but not walking the walk.
Recent advancements show how AI can revolutionise asset management for utilities. Automated systems can now accurately identify and assess components on utility poles, streamlining inspections and minimising human error. These systems not only detect defects with high precision but also enhance maintenance efficiency, reduce downtime, and extend the lifespan of critical assets.
This shift from talk to action signals the next step towards the future of utility management. After all, actions speak louder than words.
AI in action
To turn the theoretical into the practical, we can examine how AI technologies are already impacting the utility sector through innovative applications. For utilities especially, AI technology is no longer merely an experimental tool, but a proven solution. At Sharper Shape, we’ve been embedded in automating asset management for utilities for over a decade, honing what AI can look like and what it can provide for businesses with hundreds of miles of powerlines in the most remote locations. And of course, the industry has come a long way in the past ten years. The most advanced software goes far beyond the machine learning that your summer intern could train in a couple of hours. And that’s not to undermine the work of interns, but today we’re deploying highly sophisticated tools that organise huge quantities of reality data into useable workflows.
In recent years, many utilities have taken the first steps to limit reliance on outdated physical maps and instead have found themselves
relying on a digital equivalent – vast volumes of unorganised, siloed data which is unmanageable and expensive to store.
The next stage is truly AI-optimised systems which work through the entire inspection process from planning through to reporting, combining and interconnecting data as it does so, providing a clear and actionable plan giving utilities move oversight than ever before.
One such platform is our Asset Insights module. By automating the detection and assessment of infrastructure components, employing advanced machine learning algorithms to scan utility poles and other assets, exceptional accuracy can be achieved. This technology not only identifies defects such as cracks or corrosion but also assesses overall asset health, allowing utilities to prioritise maintenance and repair tasks effectively. The real world application of such technologies is already showing promising results. For example, utilities using AI-driven systems report a substantial reduction in both the time and cost associated with routine inspections. By
automating these processes, AI helps utilities redirect valuable human resources towards more complex issues that require human insight.
Addressing the challenges While AI offers substantial improvements, adopting it for utility asset management is not without its challenges. Integrating advanced AI systems into existing operational frameworks often presents hurdles in the form of employee training and data compatibility and system integration.
Utilities, and AI service providers, must address these technical challenges head-on, ensuring that their existing processes can seamlessly connect with AI technologies to fully leverage their capabilities.
Merging AI technology with legacy systems poses a significant challenge. Many utility companies operate on outdated platforms that are not readily compatible with the latest AI software, requiring extensive customisation and sometimes complete system overhauls. This
www.modernpowersystems.com | June 2025 | 25
Source: Sharper Shape
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