Kirsty McDermott from Sharper Sharpe explores
In an era flooded with AI buzzwords, the utility sector faces a critical question: are we maximising AI’s potential or merely discussing it?
Utilities face significant challenges with aging infrastructure, extreme weather, rising operational costs and increased demands for reliability. AI offers a promising solution to these issues.
Recent advances 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.
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. Comparably, think of the smart phone you carried with you in 2014 versus Apple’s latest iPhone 16.
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 In recent years, many utilities have already made the first step to limit reliance on outdated physical maps and instead found themselves relying on a digital equivalent – vast volumes of unorganised, siloed data which is unmanageable and expensive to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 the Asset Insights module from Shaper Sharpe. By automating the detection and assessment of infrastructure components, employing advanced machine learning algorithms to scan utility poles and other assets AI solutions provide exceptional accuracy. 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 in 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 integration process demands not only technical expertise but also a strategic approach to ensure that new and old systems communicate effectively without disrupting ongoing operations.
Additionally, for AI to be effective, it requires high-quality, structured data. Utilities often have vast stores of unstructured or inconsistent data, making it difficult to leverage AI effectively. Establishing robust data governance and quality control is essential to prepare for AI integration. The process of cleaning and organising data can be resource-intensive but is critical for maximising the benefits of AI.
Training and change management also play crucial roles in the successful implementation of AI.. Utility workers must be trained not only on how to use new systems but also on how to interpret AI-generated insights effectively. In an industry with an experienced workforce, the cultural shift towards data-driven decision making can be substantial and requires careful management to align staff with new technological processes.
Furthermore, the upfront cost of implementing AI can be a barrier, particularly for smaller utilities or regional cooperatives. However, the long-term cost savings, increased efficiency, and improved asset management performance justify the investment. To mitigate these costs, some utilities opt for phased implementation strategies, starting with the most critical assets to generate quick wins and establish the value of further investment.
Overcoming these challenges requires a proactive coordinated effort between AI solution providers and utility companies, focusing on seamless integration, comprehensive training, and strategic investment to ensure that AI tools deliver on their promise to transform utility asset management, while remaining flexible and scalable to best suit the utility’s needs.
For more information visit: www.sharpershape.com
