Key Highlights

• AI will be a key source of revenue growth for utilities within the next three years.

• AI will play a strategic role in how the North American grid operates and responds to challenges.

• Regulators remain the determining factor in how utilities can deploy and maximize the capabilities of AI while minimizing potential downsides for utilities and consumers.

Artificial intelligence is transforming the utility industry through both its extraordinary demand for power and its potential to change how regulated utilities operate. The exponential growth in electricity demand is placing additional strain on the antiquated North American grid. In response, many North American utilities have increased their capital expenditure to develop additional generation, transmission, and distribution assets. In turn, AI could increase efficiency for utilities and create opportunities for grid optimization by making the system more resilient and responsive.

How AI ultimately shapes this industry relies mainly on regulators, who need to design frameworks that allow utilities to maximize the potential of AI and minimize adverse impacts on customers. Here, we focus on how AI is creating new opportunities for North American utilities and the grid.

As AI is increasingly deployed in the utilities sector, we expect it to drive efficiency and become a strategic element in shaping the future of the North American grid, delivering sustainable improvements in maintenance capabilities, cost reductions, and improved earnings. This expectation is shared by 94% of surveyed US utility executives, who agree that AI will likely contribute significantly to revenue growth within the next three years. Additionally, 88% of those executives believe AI will deliver measurable competitive advantages.

From a credit rating perspective, we continue to monitor the deployment of AI in the industry to gauge how it affects utilities’ business and financial risk assessments.

AI, Today’s Grid, and the Grid of the Future

The exponential growth of data centers across the United States is being driven by the rapid evolution and widespread adoption of AI. For the utility sector, AI presents a challenge, as it requires unprecedented volumes of energy that the grid in its current state struggles to meet. But it also presents an opportunity for a more efficient and effectively managed grid.

Today’s grid was designed decades ago to transmit power from traditional generation sources, such as coal, hydro, and natural gas, which generate stable, reliable baseload power. The variability in supply from renewable energy sources, such as wind and solar, complicates energy supply planning and operations and has prevented the smooth integration of renewable power to the grid. Now, with AI models, grid operators will be able to analyze historical data, weather forecasts, and real-time conditions to align grid operations with the expected availability of renewable energy, making the grid receptive to more variable energy sources sustainably.

Essentially, AI is expected to shape the entire grid operation into a proactive system. The technology could operate the grid of the future by troubleshooting problems, guiding decision-making, and autonomously resolving problems based on insights from reviewing complex data.

Opportunities for Utilities With Increased AI Adoption

Some utilities have begun to reap the benefits of AI’s predictive analytics capabilities by using performance data patterns to enhance grid reliability and efficiency. For example, Eversource Energy, a utility company serving 4 million customers in Connecticut, Massachusetts, and New Hampshire, partnered with Ernst & Young to create a platform to predict outages, making a significant advance in outage management. By breaking down data silos and combining information from multiple sources, the platform predicts the probability of outages, and it can identify and manage potential failure points before they occur. After two months of platform use, Eversource reported that it avoided 40,000 customer outages.

Supported by AI-driven tools, utilities can accurately predict the severity and location of a weather event, and what will be required to restore services to users. In June 2026, 3 Technosylva, a global supplier of wildfire and extreme weather mitigation software, announced its Multi Hazard Operations platform, which can predict the effects of extreme weather events on a utility’s system, including the number of customer outages, and identify the likely causes of damage and the resources needed to restore service. Houston-based CenterPoint Energy, a utility that serves customers in Indiana, Ohio, Louisiana, Minnesota, Mississippi, and Texas, deployed the platform in April, and other utilities have reportedly reached agreements to adopt it.

AI presents opportunities for accelerated load growth, in marked contrast to the flat and highly predictable demand seen in previous decades. However, an optimized grid is essential to fully capture this new level of demand, allowing utilities to maximize new opportunities through front-of-the-meter connections that don’t bypass the grid.

Some utilities are already benefiting from increases to their rate bases, and as a result, they’re experiencing better revenue visibility and more accurate forecasts through long-term contracts with data center clients, leading to improved earnings. These benefits could represent a structural change in their earnings. In jurisdictions where some data centers consume as much energy as a small city, we expect significant growth in revenues within the industrial segment of utilities, potentially leading to some revenue concentration risk. We may be cautious when recognizing and rewarding such earnings growth, choosing instead to monitor the stickiness of these industrial earnings.

AI combined with advanced technologies for environmental monitoring presents a cost-saving opportunity and a shift in how utilities manage environmental issues. Utilities and transmission systems are vulnerable to serious damage from wildfires, and their equipment failures can cause fires. To mitigate this risk and enhance resilience, many utilities in California use drones to monitor their physical assets and surrounding terrain while AI applications analyze the data collected on vegetation management, maintenance, and engineering. With AI-enhanced drone programs, these utilities can inspect a vast range of assets, identify risks, and accurately dispatch site crews to repair problem sites faster, more efficiently, and more cost-effectively than with the historical use of helicopters and trained personnel.

To remain competitive, utilities must embrace AI capabilities to reduce costs, enhance reliability, and conform with safety standards required by local regulators. These advancements in AI could be positive for the management of climate and weather risks within environmental factors under our environmental, social, and governance framework.

Challenges to the Industry’s Adoption of AI

The adoption of AI is new to regulatory authorities, which must now look beyond reviewing capex outlay to assess the AI investments utilities propose to increase their operational efficiencies. While some regulators may support adding certain technologies to the rate base, under a cost-of-service framework, a more cautious regulator may be slower to do so, possibly creating a competitive imbalance in the short term. Utilities operating under a performance-based incentive framework may have an opportunity to differentiate their services as they leverage AI tools to achieve increased efficiencies, further improving their business risk profiles.

Additionally, regulators may need to assess the extent of a utility’s dependence on AI for mission-critical activities. AI is fallible, and like other technologies, it requires multilayered guardrails to ensure security. Regulators may need to help utilities embrace technology for a competitive advantage without overrelying on AI, which could lead to unwanted outcomes.

The increased digitization of operations broadens a utility’s vulnerability to cybersecurity threats and attacks. While AI supports enhanced monitoring capabilities, it also expands system complexities and broadens attack surfaces. A cyberattack could disrupt operations, lead to service interruptions, trigger reputational damage, and cause financial losses. From a credit perspective, increased reliance on AI could lead to a more critical assessment of a utility’s technology-related risks as they affect energy grids, electricity distribution assets, and utility operations. We may assess the resilience of the utility’s technology and the adequacy of its insurance and risk management protocols against cybersecurity threats. From the social perspective within our ESG framework, increased cybersecurity risks could result in data privacy and security becoming more relevant for utilities.

The Future

AI is already having an impact on the operations of regulated utilities, from increased energy demand to changing how utilities operate the grid today and designing the grid of the future. In our credit rating analysis, we will assess whether, in the long run, increased use of AI leads to a structural impact on business risks or whether the impact is contained to a social issue. We will also consider the cost of implementation and its impact, if any, on a utility’s financial risk assessment. Ultimately, the proof of AI as a worthwhile investment will be assessed through the lens of improved efficiency, service quality, and a cost-recovery framework that regulators will approve.

Source: Morningstar DBRS: How AI Is Shaping the Energy Grid of the Future | Morningstar