Over the past few decades, the electricity grid evolved from the traditional network of poles, transformers, and wires to a modern grid where data, analytics, and automation play a central role. A recent report from Itron, Inc. provides a powerful outlook on this transformation declaring that around 81% of North American electric utilities have already integrated or are in the process of integrating artificial intelligence within their systems. This number underscores the rapid and deep penetration of AI into power grid sector. This article explores why utilities are embracing AI, how they are putting it into action, what market trends are underpinning this shift, and what it means for the future of the grid.

Beyond a technological upgrade, this transformation reflects a redefinition of what a utility is. AI is not only automating processes but also reshaping decision-making, investment priorities, and customer relationships. In many ways, the grid is gradually becoming a living digital organism: capable of learning, predicting, and adapting in real-time.

What Caused the Sudden Shift to AI?

Multiple factors have accelerated AI integration in utilities, including aging infrastructure, growing complexity and demand, the need for safety, reliability, and efficiency, and improvements in data and digital maturity.

Many utilities still rely on assets that have outlasted their design lifespans. Due to this, maintenance costs are rising, failures have become more disruptive, and the traditional reactive maintenance model has become less effective. AI offers predictive and condition-based maintenance, allowing utilities to anticipate component failures and optimize maintenance schedules to minimize downtime and cost. This shift also supports sustainability goals by extending the useful life of existing assets, reducing waste, and minimizing the environmental footprints of unnecessary replacements.

According to the Itron release, utilities today face demand from manufacturing, electrification, and AI data centers, straining the grid even as it modernizes. At the same time, growth of distributed energy resources (DERs) like solar, batteries, and EVs is adding new layers of complexity to grid planning and operations. Each new solar panel or EV charger acts as both a consumer and potential producer of electricity, requiring the grid to balance millions of transactions every second. Traditional manual control systems cannot scale to this complexity.

According to research, 57% of the executives view AI as a key to “grid optimization” and 53% as “safety,” identifying AI’s role in detecting hazards faster than physical inspection alone. These figures indicate the need for tools that can assist in keeping communities safe, providing reliable energy, and do so more cost-effectively.

With improved sensors, increased real-time data streams, and enhanced connectivity, the technological foundation for AI is strong. Utilities which “unify systems and leverage AI tools” can significantly improve operational efficiency and compliance. Thus, the demand for a smarter grid, technology readiness and organizational pressure all point towards AI adoption.

Itron’s Findings on the AI Boom in North American Utilities

A survey of 500 electric utility executives showed that across the U.S. and Canada, 41% of executives reported that AI-related technologies have been fully integrated into operations, more than the prior year’s forecast of 27%. Another 40% reported major investments and mature AI projects underway. This integration focuses on grid optimization (57%), grid resilience and safety (53%), and demand forecasting (51%). These figures indicate a paradigm shift from experimentation to operational deployment of AI. It also demonstrates a rapid cultural shift inside the sector. Five years ago, utilities often viewed AI as a pilot-stage innovation; now, many consider it a strategic necessity for regulatory compliance, climate-resilience planning, and competitive performance in liberalized electricity markets.

How Utilities are Deploying AI?

Companies use AI in various systems and analysis to enhance performance, reduce operational risks or equipment failures, and provide a better user experience for customers.

Predictive Maintenance & Asset Management

AI models gather data from sensors (historical failure records and external factors like weather and load patterns) to predict when equipment is likely to fail or when sections of the network are stressed, this shifts repair strategy to “fix when broken” to “fix before failing”. Utilities using such systems have reported substantial reductions in outage minutes per customer and maintenance costs. By continuously learning from asset data, these models become increasingly accurate over time.

Load Forecasting & DER Integration

As the integration of renewables, batteries, and EVs enter the grid, forecasting demand and supply becomes more complex. AI tools help utilities predict load, volatility, and manage dispatch of distributed resources. For instance, research suggests AI can help utilities streamline operations, improve resource management, and support new services.

Outage Detection & Restoration

AI enhances situational awareness across the grid. Machine learning models can rapidly identify anomalies, isolate faults, and automatically reroute power to minimize the scale and duration of outages. This automation not only improves reliability but also frees operators to focus on critical decisions during emergencies. In regions prone to wildfires, hurricanes or ice storms, predictive outage models are already proving vital.

Grid-Edge Intelligence

The “grid edge” includes feeders, substations, local controllers and DERs, embedding intelligence at this edge allows real-time control and local optimization. This enables utilities to manage rapid fluctuations from EVs, solar, and batteries more effectively. Eventually, it will result in improving voltage stability, reducing congestion, and enhancing situational awareness in areas with increased DER production.

Customer Engagement & Services

While the operations side dominates, there is growing AI usage in customer analytics (usage patterns, demand response, peak demand times), personalized services, outage notifications, and dynamic pricing/enabling of flexibility markets. Through these tools, customers themselves become active participants in energy management. Smart homes can automatically adjust usage based on AI-predicted peak periods, tuning customers into “prosumers” who help balance the grid rather than merely draw from it. Similarly, houses with solar panels that generate excess energy can support the grid by sending back surplus generation: a step towards green generation.

As cumulative, these applications reflect a structural transformation in how utilities think about operations, customers, and business models.

Implications for the Grid and Utility Business

Improved Reliability & Resilience

With AI-driven monitoring, forecasting, and automated responses, utilities will be better positioned to anticipate failures, respond rapidly, and operate in more dynamic conditions.

More Adaptive & Service-Oriented Grid

The traditional model (generate -> transmit -> distribute) is shifting toward an intelligent and bidirectional network. AI helps utilities move beyond simply delivering power to offering value-added services (DER management, flexibility markets, edge solutions)

Workforce & Organizational Shift

Utilities will increasingly need data scientists, AI engineers, and operational technology specialists to handle exceptions and manage system design rather than manual monitoring, which is tedious and less effective. Training programs and academic partnerships are already emerging to bridge this skills gap, ensuring the next generation of engineers can interpret AI outputs, manage algorithms ethically, and integrate them safely into mission-critical infrastructure.

Business Model Evolution

With better analytics and control, utilities can monetize new services: predictive maintenance contracts, grid-edge intelligence, DER orchestration, and customer-facing digital platforms.

Global Inspiration

While the survey focuses on North America, other areas like the Middle East have started to focus on AI integration within utilities. North America being an early adopter can lead the path for the rest of the world. For example, utilities in Europe are experimenting with AI for renewable forecasting, while those in Asia are using AI-powered grid twins for urban energy management. This cross-regional learning loop is accelerating innovation worldwide, reinforcing AI as a universal pillar of grid modernization.

Increased Regulatory Focus

As utilities deploy AI deeper into operations, regulators will scrutinize issues of transparency, reliability, and cybersecurity. AI must remain a decision-support tool rather than an unsupervised decision-maker, with humans maintaining ultimate oversight.

Concluding Remarks & Insights

From an industry perspective, the integration of AI by major companies represents a structural transformation. The “grid of the future” is not just stronger hardware, more renewables or more storage, but a grid layered with intelligence: sensors, analytics, automation, edge control and human oversight.

For stakeholders, including utility executives, investors, regulators, and technology vendors, the implications of this shift are profound. Grid investments, operations strategy, workforce development, and regulatory frameworks must be reframed from intelligence as a core dimension. AI can just be the solution to the energy trilemma: ensuring that energy is reliable, affordable, and sustainable with respect to generation, transmission and distribution.

Ultimately, AI is positioning itself as the central system of tomorrow’s power ecosystem. Its algorithms will synchronize renewables, stabilize microgrids, and anticipate both human demand and environmental behavior. As adoption deepens, the invisible intelligence behind the grid will become its most valuable infrastructure.

Written By:
Michael Sheppard
Editorial Advisor
Inertia Media