For over a century, the electric grid has powered the world—fueling homes, industries, and economies. Yet today, utility leaders face an unprecedented convergence of challenges: Surging energy demands, increasingly variable supply from renewables, growing climate volatility, as well as the growing need for resilience and cost control. At the same time, the rise of artificial intelligence (AI) could open a new era of opportunity, transforming grid management from a reactive, manual process into a proactive, intelligent system.

This article explores the impact AI could have on the electric grid, from boosting operational efficiency to enabling new business models like virtual power plants (VPPs), all while ensuring security and reliability.

Navigating a new era of grid complexity

Over the past couple of years, AI—a long-studied technology—has found its way to both mass-scale consumer adoption and enterprise implementation. To facilitate the capability and scale of AI, there needs to be a growing supply of reliable energy. Modern data centers require a greater power density than ever before—estimates show that use of commercial AI technologies like large language models (LLM) and AI agents will contribute to roughly 3-6% of the total energy demand by 2030[i] with a 40% CAGR from 2025-2030.[ii]  The growing deployment of ancillary devices such as uninterruptible power supplies (UPSs), backup generators, and cooling systems exacerbates this growth. However, there is a flip side: While AI is accelerating energy demand, it holds the potential to manage the grid more efficiently too.

AI can help reduce overhead operational costs for utilities, help manage outages efficiently, and—if done right—help avoid a considerable number of outages. Therefore, AI could offset its strain on the electric grid via tactical implementation. Add to the mix the increasing energy efficiency of GPUs, and software methods such as energy optimized algorithms and model pruning (e.g. selecting relevant parameters), and we can begin to see AI’s holistic positive impact on the electric grid.[iv] Hence, with AI, the electric grid is poised to turn from a static, reactive system into a proactive and agile system with unprecedented performance levels.

AI as the grid’s new operator

The electric grid is a complex network with millions of devices and systems, each behaving differently. Operators must make quick, high-stakes decisions based on incomplete data, sifting through vast information silos to maintain service safety and reliability.  This results in high-stakes scenarios and lead times to make decisions.

LLMs and AI agents can function as an “autopilot” for grid operations, augmenting human expertise to help operators with these decisions. The reasoning, information selection, and memory capability of LLMs make them perfectly suited for such scenarios. LLMs and AI can serve this purpose in three distinct use cases—each of which can have separate agentic workflows to further break attention and information down:

• Surface insights quickly—AI-as-Investigator could focus on surfacing relevant information efficiently to save Operators crucial research and troubleshooting time.
• Guide decision-making—AI-as-Advisor could unlock the power of specialized AI agents to learn from historical tasks, and thus automate workflows, and make informed recommendations.
• Autonomously execute tasks—AI-as-Operator could close the loop by taking autonomous actions and decisions based and AI-driven insights and unlock unprecedented levels of efficiency.

Vital components for a feasible solution in production include architecting a smart modern context protocol (MCP) client/server relationship for agent orchestration, LLMs to dissect user input, and resilient API design. The technical framework of how agents might interact with grid software products is shown below in Figure 2.

Unified grid network model: Topology, state estimation, and data accuracy

Modern utility software platforms—such as advanced distribution management systems (ADMS), outage management systems (OMS), and distributed energy resource management systems (DERMS)—depend on high-quality grid data to build accurate “digital twins” of the electric grid. At the core of these systems lies a sophisticated mathematical model, integrating the physics of the grid (such as impedance, voltage, and line connectivity) with its geographical details from geographic information systems (GIS). Ensuring that these digital representations mirror the actual grid is critical for safe and efficient operation.

AI can play a transformative role by not only validating but actively building these network data models from the ground up. AI-driven models can continuously monitor, identify, and correct discrepancies between digital models and real-world grid behavior, minimizing the risk of errors propagating through critical utility systems. Utility operators can now leverage extensive training datasets—both labeled and unlabeled—to enable AI systems to achieve near human-level detection and remediation of data abnormalities.

One powerful application is the use of graph neural networks (GNNs), which construct a dynamic, geometric representation of the grid’s topology. In this framework, nodes represent substations or transformers, and edges correspond to transmission lines. Each component carries features such as voltage, power injection, switch status, and impedance. GNNs excel at not only aggregating information from neighboring nodes but also validating the underlying physical relationships, making them well-suited for real-time topology error correction and data integrity checks.

Imagine a scenario where a substation erroneously reports “zero current” while adjacent nodes indicate active power flow. Instead of requiring manual intervention, an AI-enabled network model can analyze neighboring data and rely on physics-based reasoning to quickly flag this as an anomaly, or even resolve the issue autonomously. In this way, AI significantly improves data integrity, enhances the fidelity of digital twins, and ensures the smooth flow of energy across network currents, all while reducing the burden on human operators.

Predictive maintenance: From passive monitoring to active forecasting

When optimizing the grid, monitoring grid assets for issues is as important as keeping tabs on supply and demand. Having accurate forecasts is one piece of the puzzle. The other piece is to ensure the assets that both provide and consume supply are performing safely, efficiently, and reliably. These grid components are subject to latent risks that precipitate failures, such as corrosion, thermal fatigue, and encroachments. AI could revolutionize asset management by transforming passive monitoring into active prognostication.

For AI to monitor grid elements, forming virtual replicas of these assets is important, and that’s where digital twins come into play again. Digital twins can simulate stress under load or seismic events. These models run against millions of simulations of how assets might perform under stress, lack of maintenance, and emergent events to give strategic vantage points for utilities to act upon.

From outages to opportunities: Proactive grid management

Perhaps more compelling is how AI can shift asset management from a curative to a preventative paradigm. Instead of reacting to issues after they escalate, utilities can anticipate and preempt failures, which can help them curb unplanned outages, extend asset life, and prioritize capital investments. Powered by AI, utilities could defer costly upgrades while still meeting reliability targets. As a result, even as the grid faces mounting pressure, it becomes more agile and responsive, supporting both operational efficiency and strategic transformation.

Routine updates on infrastructure status can leverage AI's pattern recognition. For example: Computer vision algorithms, specifically convolutional neural networks (CNNs), can analyze drone footage to catalog pole lean or insulator cracks and flag interventions before escalation with object detection models. In addition, using multimodal fusion networks, AI can fuse thermal imaging and oil analysis to preempt transformer dielectric breakdowns. Lastly, anomaly detection can benefit from AI to spot deviations in vegetation management. Vegetation-related impacts to the power system account for more than 20% of U.S. power outages.[v] Its management could benefit from geospatial AI through LiDAR scans and satellite multispectral data train CNNs to predict growth trajectories, optimize trimming schedules, and can reduce vegetation related damage by as much as 63%. [vi]

Load and flexibility forecasting: Solving the balancing act

One of the major concerns for electric grid operators is balancing supply and demand, often in real time. The limited amount of generation and distribution power available to the grid and growing demand make balancing the grid challenging. Forecasting the load and amount of generation can help alleviate the challenge and is the first touchpoint for most decision making with respect to switching, outage management, and load-reduction events. AI can elevate the current approaches to forecasting.

Tackling traditional load forecasting for short-term intervals can answer the question: What expected supply should the utility worry about creating at a future time? AI frameworks can ingest multivariate inputs like weather patterns, economic indicators, and historical loads to extrapolate grid states hours or days ahead. Emerging transformer-based models—such as those adapted for time-series data—could further enhance this by processing sequential dependencies with self-attention mechanisms. In other words, they can weigh the importance of different parts of an input sequence relative to each other, capturing contextual relationships and long-range dependencies.

Focusing on the added complication of flexibility raises the question: Can the electric grid get some help from customer-owned devices such as thermostats, PV cells, generators, and energy storage? Load flexibility (or demand flexibility) is the ability to adjust electricity use (demand) to match supply, shifting it to when power is cheap/clean or reducing it during grid stress. Such events add another variable to traditional load forecasting and are seen as one of the ways to answer the energy demand of data centers. With highly accurate flexibility forecasts, utilities could construct VPPs out of these flexible devices to provide power to data centers during high-peak usage. This would reduce the cost of energy for end-customers and stress on the grid. Orchestrating both flexibility events and providing that flexibility to data centers at peak-times hinges entirely on producing accurate forecasts. AI could achieve this, and much more, if deployed in orchestrating energy exchange between generation devices and consumers.

The road ahead: Unlocking the grid’s full potential

AI is not a cure-all, nor will it replace the deep expertise of grid operators. But as a force multiplier—delivering speed, scale, and precision—it could open new possibilities for utility leaders worldwide. By embracing AI in grid operations, asset management, forecasting, outage prevention, and customer engagement, utilities can drive an evolution from static, siloed infrastructure to intelligent, integrated systems.

The electric grid’s future will be defined by its ability to anticipate, adapt, and optimize. In this, AI is both the challenge and the solution—responsible for new demands, yet uniquely equipped to unlock the flexibility, efficiency, and security required. For those ready to harness its potential, the grid stands on the brink of a new, brighter era.