How to Get Practical, High-Impact Results From Utility Asset Management AI

In an era flooded with AI buzzwords, the utility sector faces a critical question: are we actually maximizing 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, 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 put to use 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 bring AI-fatigue. Without real, meaningful use cases of the benefits artificial intelligence can bring, we risk talking the talk, but not walking the walk.

Recent advancements show how AI can revolutionize asset management for utilities. Automated systems can now accurately identify and assess components on utility poles, streamlining inspections and minimizing 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. 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 organize 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 unorganized, siloed data which is unmanageable and expensive to store.

The next stage is truly AI-optimized 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 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 prioritize 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 customization 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 organizing data can be resource-intensive but is critical for maximizing 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. Such strategic integrations will enable early adopters to enhance their operational efficiencies without overhauling their entire systems.

Looking Ahead

Looking ahead, the role of AI in utility management is set to grow exponentially. Emerging trends such as the Internet of Things and smarter grids are expected to further enhance the capabilities of AI systems. These technologies will allow utilities to not only monitor but also automatically adjust their operations in real-time to optimize energy distribution and respond to potential disruptions before they escalate. Moreover, as utilities continue to face the challenge of extreme weather AI will allow them to stay one step ahead of the status of surrounding vegetation that could fall onto powerlines or catch fire. Advanced data analytics powered by AI will provide enhanced decision-making, as AI algorithms analyze vast amounts of data to predict potential failures before they occur.

From Discussion to Action

In 2024, AI is already transforming utility asset management from a reactive to a proactive discipline. By harnessing the power of AI, utilities are not only improving their operational efficiencies but are also setting the stage for a future where digital resilience defines utility industry leaders.

The journey from theoretical AI applications to practical, impactful implementations is complex but achievable with strategic planning, robust partnerships, and a clear focus on long-term goals.

As we move beyond the hype, it becomes clear that AI is not just a tool for innovation but a necessity for the sustainable, efficient, and resilient utility operations of today and tomorrow.