Harnessing AI's Potential: A Practical Guide for Utilities

The promise and reality of AI

Artificial Intelligence (AI) is rapidly transforming industries around the world, but for utilities, effectively harnessing its potential presents unique opportunities and challenges. 

As regulated monopolies responsible for delivering essential services, utilities operate under a capital-intensive business model with a strong emphasis on reliability. This environment fosters a cautious approach when evaluating emerging technologies. 

But despite the industry's traditionally low appetite for change, utilities are doubling down on AI.   

Ninety-four percent of utility CIOs plan to increase AI investments, with average spending rising more than 30 percent, according to Gartner.   

Utility leaders now describe AI as a strategic capability rather than a collection of isolated pilots, with expected impact across back-office functions, planning, operations and customer engagement. 

Still, with budgets remaining tightly coupled to ROI, utilities must approach AI adoption with care.  

Here, we explore some key areas where we saw utilities make progress with AI in 2025 and where we expect traction to continue in 2026.  

Focus use cases on reliability and resilience  

AI use cases tied to reliability and resilience remain the most practical place for utilities to push forward. These improvements are straightforward to measure, which makes them easier to justify to regulators and tie to business outcomes. 

Utilities are using machine learning to detect anomalies, predict equipment failures and prioritize maintenance before issues turn into outages. Models draw on sensor data from transformers, relays and substation equipment to surface emerging problems and reduce the need for time-consuming manual inspections.  

At DISTRIBUTECH 2025, executives described predictive maintenance as an operational requirement for managing aging infrastructure and the growing complexity of the grid. Early adopters noted that AI is improving equipment visibility and helping teams intervene earlier in asset failure cycles. 

Another area where AI is delivering value is in wildfire prevention, a focus at EEI 2025. There, utility leaders emphasized vegetation management and line inspections as some of the most immediate and high-impact areas for AI investment.  

Computer vision models can analyze various types of imagery, often gathered by drones, to identify overgrowth, leaning trees and structural issues before they become hazards. Some utilities are pairing this with thermal or acoustic sensors to detect hotspots or early signs of equipment stress, creating a more comprehensive picture of emerging risks.  

The result is more targeted inspections, reduced field exposure for crews and meaningful reductions in wildfire and storm-related incidents.  

Additionally, computer vision capabilities help support condition-based maintenance, allowing utilities to move away from fixed schedules and focus fieldwork where data shows the greatest need, leading to both reduced operating expenses and more efficient use of capital. 

Increase operational efficiency with AIOps  

Utilities are turning to AI for IT operations (AIOps) to reduce operational noise, streamline workflows and respond faster when issues arise, especially for their grid IT assets. By bringing IT and OT telemetry into a single view, operations teams spend less time chasing false alerts and more time heading off issues that have the potential to disrupt service. 

When one of our clients consolidated disparate monitoring tools into a unified AIOps platform, they cut alert noise by 85 percent. Operators were able to focus on issues that mattered. Further, critical incidents triggered immediate engagement from the right teams, reducing response times and eliminating costly war-room scenarios.   

Some utilities are extending these gains by pairing monitoring platforms with generative AI (GenAI) assistants. When combined with machine learning and other types of AI this can be a first step toward more complete intelligent situational awareness. 

Southern California Edison, for example, partnered with WWT to deploy Project Orca, a GenAI-driven incident-management platform. The platform's chatbot lets network operations teams pull up relevant documents, past incidents, real-time telemetry and troubleshooting steps in seconds.  

Introducing AI into operations can raise understandable concerns among tenured staff who are used to established workflows. In order to increase adoption, it helps to emphasize that AIOps is designed to support human operator decisions by reducing overwhelming alarm volume and manual tasks, it does not replace operators.  

The goal is to give teams more time to focus on work that truly requires their real-world expertise.  

Support employees and customers with GenAI   

Much of a utility's work hinges on extensive documentation and institutional knowledge, making GenAI a practical tool for reducing administrative burden and improving back-end processes.  

Utilities are finding success with GenAI in several ways:   

• Streamline rate case filings: AI models can draft initial sections, pull data from past cases and perform consistency checks. In recent analyses, GenAI-enabled workflows have shown potential cost reductions of up to 25 percent per rate case and compressed filing cycles by automating large portions of repetitive work.
• Strengthen workforce readiness: Utilities are creating internal knowledge assistants that let employees ask natural-language questions and receive answers drawn from manuals, maintenance logs, project documents and internal reports. Assistants help preserve institutional knowledge as experienced workers retire and make it easier for newer or rotated staff to ramp up.
• Improve customer experience with virtual agents: Virtual agents can handle routine calls, providing outage updates and guiding customers through billing questions. Utilities piloting these capabilities report higher satisfaction scores for outage calls and reduced call-center workload.

Across internal and customer-facing workflows, GenAI is giving utilities practical ways to simplify complex processes and free up time for work that requires human judgment. 

Extend intelligence to the field with edge AI 

Utilities increasingly need real-time awareness at the edge of the grid. As automated reclosers showed, edge AI brings processing power closer to where data is generated, allowing utilities to make faster decisions even when connectivity is limited.  

Newer use cases for edge AI include: 

• Local, real-time power quality monitoring and management
• Real-time equipment monitoring and anomaly detection using cameras, sensors and computer vision.
• Automated safety and compliance checks on job sites.
• Remote inspections where drones or fixed cameras flag issues without waiting for human review.

These capabilities reduce latency, improve safety and help utilities respond faster to emerging issues. 

Edge AI is also reshaping smart metering.   

In deployments of AMI 2.0, the next generation of advanced metering infrastructure, meters equipped with embedded compute can process data locally, enabling instantaneous outage reporting, tamper detection and voltage optimization. Edge processing also reduces the volume of raw data needed to be transmitted over the AMI network. 

Local inference allows meters to raise alerts in seconds and gives operators a clearer picture of system performance. 

Utilities are also using edge AI to pilot tools such as augmented-reality guidance and voice-enabled field assistants that run directly on ruggedized devices. These tools give technicians step-by-step support in low-bandwidth environments.  

Looking ahead, utilities that invest in edge AI will be better positioned to build accurate digital twins that rely on high-quality field data and real-time system insights.  

Strengthen planning and capital allocation with AI-driven modeling 

AI is reshaping long-term planning by accelerating simulations that once required weeks or even months of deterministic modeling. This matters in a risk-averse sector where planners need confidence in their assumptions and the flexibility to evaluate a wider range of scenarios. 

At GridFWD 2025, leaders emphasized that faster, more comprehensive modeling is becoming essential as load growth accelerates and infrastructure decisions become more complex.  

Teams increasingly need to stress-test assumptions tied to electric vehicle (EV) adoption, distributed energy resources, data center interconnects, and extreme weather — and they need to do so without pushing existing modeling tools beyond their limits. 

One of our utility clients discovered that its operations teams were running forecasting models on standard PCs over residential internet connections. Staff often kicked off simulations late Friday and hoped they would finish by Monday without their machines crashing. 

By dedicating GPUs within its VDI environment to handle compute-intensive planning, the utility was able to cut simulation runtimes to hours instead of days.  

These advances also strengthen regulatory discussions. Instead of relying on a narrow set of manually generated studies, planners can bring a broader range of modeled outcomes into rate cases, providing clearer evidence for why specific investments deliver the best reliability, cost and customer benefits. 

Preparing for what comes next 

While AI is already improving operational efficiency, the industry is still far from a fully AI-powered grid. Even so, utilities can start laying the groundwork for how AI will shape future grid operations. 

Federal agencies are actively modeling how AI could support a more dynamic and distributed grid. Utility leaders should stay connected to this research, as well as the work of groups such as the Electric Power Research Institute, to ensure long-term investments stay aligned with emerging best practices. 

Because technology adoption cycles in the utility sector stretch across years, this research should inform strategic roadmaps and multiyear upgrade plans. Building AI into these plans offers clarity for internal stakeholders, provides transparency to regulators and ensures the grid is prepared when more advanced capabilities become viable. 

Conclusion 

AI is becoming an essential tool for utilities, not through sweeping bets but through practical improvements across the business. By applying AI in areas that strengthen reliability, streamline operations, support employees and improve planning, utilities can capture meaningful value now while laying the groundwork for more advanced capabilities ahead. 

Close collaboration between utilities, technology partners and regulators will be critical as AI becomes more embedded in grid operations. Our experts can help you put the right organizational structures in place to guide AI adoption at scale. We'll work with you to define clear strategy and policy, establish guardrails for responsible use and create the conditions for sustained AI innovation, including: 

• Organizational strategy and policy: Aligning AI initiatives to business priorities, risk tolerance, regulatory obligations and operating models.
• Organic innovation and culture: Enabling teams to experiment, learn and apply AI responsibly through training and leadership support.
• AI factory assessment and workload planning: Evaluating current infrastructure, data readiness and operating processes to understand what workloads can scale today and what investments are needed next.