The utilities industry is at a critical stage in its evolution. While technological advancements have revolutionized numerous sectors, utility companies have historically lagged in quickly adopting the newest technologies, including artificial intelligence.
Despite AI’s immense potential for optimizing operations, reducing costs, and enhancing service delivery, energy and utility (E&U) organizations have been slow to universally embrace its power. Interest, however, is incredibly high, with 74 percent of utilities embracing AI per a recent study by IBM.
In this blog, we explore the top use cases and potential opportunities AI for utilities offers and the potential reasons behind the reluctance to fully adopt AI. We also provide insights into overcoming the common barriers to adoption.
Top AI Use Cases for Utility Companies
While AI has the potential to impact all areas of operations, there are several incredibly strong use cases for E&U organizations.
Use Case 1: Predictive Asset Maintenance
One of the primary applications of AI for utilities lies in predictive maintenance for assets. By analyzing vast amounts of data from sensors and equipment, AI algorithms can predict potential equipment failures before they occur, enabling organizations to schedule proactive maintenance activities.
Imagine a scenario where a utility can use the power of AI to support proactive pole maintenance. Using a combination of drone- or helicopter-captured video and GIS data, AI models can accurately identify common pole defects or risks such as leaning poles, heavy vegetation, decaying wood, and transformer issues like corrosion.
AI can analyze vegetation growth trends to predict elevated risks, such as extremely dry conditions that could exacerbate wildfire risks that require trimming services. Data from previous inspections and maintenance are another layer added to the picture. By inputting this data into AI models, utilities can proactively identify areas that need support before more severe issues develop that may impact service delivery and result in high costs.
Moreover, companies can use generative AI for utilities in the field to support the maintenance activities. For example, providing field service works with a generative AI bot via an app that provides detailed information on how to fix a specific piece of equipment can eliminate the need for manually reviewing often lengthy maintenance manuals.
Many utilities have embraced the opportunities AI presents to support asset maintenance initiatives. For example, Duke Energy, in partnership with AWS, utilizes AI to detect anomalies in wood poles using a computer vision-based solution. This program has resulted in high levels of accuracy in issue detection.
Use Case 2: Grid Optimization and Demand Forecasting
Another strong use case of AI for utility companies is in grid optimization. AI can analyze customer consumption patterns, weather forecasts, historical data, equipment performance, and other data to optimize energy distribution, minimize transmission losses, and reduce peak demand load.
Furthermore, AI-powered demand forecasting enables utilities to predict future energy demands accurately, facilitating better resource planning and allocation. Utilities can reduce operational costs and ensure reliable service delivery by optimizing generation and distribution, particularly during peak demand periods.
This is particularly pertinent and timely as utilities make the shift to electrification. Take, for example, the proliferation of electric vehicles (EVs), which has presented unique challenges for the electrical grid. EVs tend to be clustered in certain geographies, most heavily in urban areas, putting strain on regional grids.
By using available data – like usage patterns, time of charge, weather forecasting, car model information, and charging duration – utilities can now identify the optimal charging times for customers to reduce grid load. They can use customer digital channels like text messages to recommend optimal charging times. They can also offer incentives like demand pricing to customers who adopt these practices.
Several utilities, such as PG&E and DTE, have already used similar programs to address the challenges and opportunities that increased EV adoption amongst customers presents.
Use Case 3: Customer Engagement and Personalization
One of the most prevalent use cases of generative AI for utility companies supports the customer experience. AI can enhance customer engagement and satisfaction by analyzing customer data and developing profiles to personalize services and offerings. For example, companies can use AI-powered chatbots to provide personalized recommendations, promptly address customer call center and chat inquiries, and offer proactive assistance without adding call center headcount.
By analyzing customer interactions and preferences, AI algorithms can tailor future communication strategies, anticipate customer needs, and improve overall satisfaction. Generative AI technology can shine in this area, developing personalized content with features such as easy multilingual support.
Use Case 4: Regulatory Compliance Monitoring
AI can monitor operations for safety and regulatory adherence, an often time-consuming task for utility employees. AI can analyze real-time data to detect anomalies, predict maintenance needs, and automate regulatory reporting, ensuring accuracy and efficiency. For example, in a nuclear power plant, AI can track equipment performance, radiation levels, and environmental conditions to prevent safety risks and ensure compliance with regulations like those set by the Nuclear Regulatory Commission (NRC).
Use Case 5: Cybersecurity Monitoring and Threat Detection
By using AI-powered monitoring and threat detection tools, organizations of all sizes can dramatically scale their ability to adhere to cybersecurity guidelines. According to the World Economic Forum, “By combining interoperable and manufacturer-agnostic AI technologies, and efficiently leveraging OT-native human expertise, small and medium-sized energy companies can gain access to monitoring, detection and cyberattack-prevention capabilities, a level of protection only previously attempted in-house at companies with large budgets.”
Common Barriers to AI Implementation
Despite these compelling use cases, barriers hinder widespread AI adoption in the E&U industry. Centric Consulting’s AI Practice Lead Joseph Ours remarks, “There isn’t a single universal barrier to adoption — it will depend on where your organization is on its journey.” We typically observe a mix of the following barriers when discussing AI solutions with our utility partners:
Barrier One: Outdated and Legacy Technology
One significant challenge is utility IT’s legacy infrastructure. Outdated systems and siloed data obstruct implementing AI solutions seamlessly. Integrating AI into existing infrastructure requires substantial upgrades to technology and data management processes.
With utilities facing budget restrictions, this is one of the highest blockers to AI implementation. When building an AI use case, it is important to highlight the potential ROI of the initiative. By using existing software solutions to support their AI needs, organizations can reduce the upfront costs associated with building out their own AI models and solutions.
Barrier Two: Data Quality, Availability and Privacy
Data quality and availability pose another significant hurdle. While utilities generate vast amounts of data from smart meters, sensors and other sources, ensuring data accuracy, consistency and accessibility across platforms remains a challenge.
In addition, poor-quality data can undermine the effectiveness of AI algorithms, leading to incorrect insights and decisions and potentially introducing bias. Moreover, utilities must consider the importance of preserving data privacy and be aware of evolving compliance standards such as the California Consumer Privacy Act (CCPA). 
Barrier Three: Regulatory, Security, and Safety Concerns
Regulatory constraints and concerns about data privacy and security inhibit the adoption of AI in utilities. Compliance with stringent and often unclear regulations adds complexity to implementing AI solutions, requiring utilities to navigate a complex legal and regulatory requirements landscape. Data privacy and cybersecurity concerns also raise apprehensions about sharing sensitive information and adopting AI-driven technologies.
Regulators and federal and state governments have been slow to define the exact parameters of AI usage within the sector. In addition to data use and cybersecurity considerations, E&U organizations still need to address important questions, including whether AI technologies are capital investments or operating expenses.
That said, clearer recommendations and regulations are on the horizon. In an executive order issued in 2023 regarding the “Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence,” the Biden White House directed the DOE, alongside other executive agencies, to assess AI threats to critical infrastructure, including utilities. The result, a report entitled, “AI for Energy: Opportunities for Modern Grid and Clean Energy Economy,” outlines an extensive case for the use of AI solutions within the sector, while exploring the challenges.
Barrier Four: Recognizing AI Capacity Already in Place
Many utility companies have been using solutions rooted in AI for years without officially designating it as a solution. As a result, many teams within an organization may be implementing AI solutions without centralized governance, limited data sharing, and little cross-resource sharing. As a result, solutions can become siloed, expensive, and not as effective as if there were a centralized AI strategy and governance mechanism.
Strategies to Overcome Barriers
Several strategies can facilitate the adoption of AI within utilities.
First, fostering a culture of innovation and digital transformation is paramount. Utility companies need to invest in employee training to build AI capabilities and cultivate a workforce that embraces change rather than fears it. Creating cross-functional teams, not only within IT, dedicated to AI implementation can facilitate collaboration and knowledge sharing across departments.
Addressing data challenges is also essential for successfully deploying AI in utilties. Organizations should prioritize data quality initiatives by investing in data cleansing, normalization and validation processes. Implementing robust data governance frameworks and using advanced analytics tools can help utilities derive actionable insights from data while ensuring compliance with regulatory requirements.
Collaboration and partnerships with third-party technology providers and industry experts can accelerate AI adoption in utilities. External expertise, versus building out full in-house teams, can help utilities access AI solutions without significant investment, accelerating the implementation process and mitigating risks.
Furthermore, regulatory engagement and advocacy are crucial for overcoming regulatory barriers to AI adoption. Utility companies should actively engage with regulators and federal, state, and local government agencies to advocate for policies that facilitate innovation while ensuring compliance with regulatory requirements. Build trust and transparency with regulators and stakeholders to alleviate data privacy and security concerns, paving the way for broader AI adoption.
Conclusions
AI holds immense promise for revolutionizing the utilities industry, offering opportunities for cost savings, operational efficiencies, and enhanced service delivery. However, several barriers hinder widespread adoption, including legacy infrastructure, data challenges, regulatory constraints, and internal resistance.
By addressing these barriers through strategic investments, collaborative partnerships, and regulatory engagement, utility companies can take advantage of the potential of AI.
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