The artificial intelligence (AI) boom is poised to fuel a rapid—and drastic—surge in electricity demand, placing unprecedented pressure on utilities to modernize their grids, integrate distributed energy resources, and reduce mounting supply chain and customer costs.
Ironically, perhaps, one way to address the AI-driven demand for power is to adopt AI tools. AI platforms have the potential to help monitor for failures in the grid, predict periods of high demand and run systems more efficiently.
This is Part 2 of a three-part series on the impact of artificial intelligence (AI) on electric utility operations; you can read Part 1 here. For more information related to AI and power generation, follow POWER’s continuing coverage of AI and its effect on the electric power sector here.
Yet as IBM’s energy industry GM put it, utilities “love to be a fast follower” when it comes to implementing new technologies, due to the profound risks involved for organizations charged to literally keep the lights on.
By understanding how other sectors are beginning to leverage AI today, utilities can identify proven applications, accelerate deployment and begin to move from experimentation to value creation.
Use Cases from Other Industries and How Utilities Can Apply Them
Utilities can learn valuable AI adoption lessons by exploring applications in different industries, from automotive and manufacturing to firefighters and retailers. Here are a few key use cases they should keep top-of-mind.
Visual Detection of Anomalies—Insights from the Auto Industry: In automotive manufacturing, AI-driven computer vision is being used to inspect parts and assemblies while also detecting defects such as misaligned welds, paint blemishes or missing components. For example, major auto OEMs like Toyota have applied AI in paint inspection processes and final vehicle checks.
How utilities can apply it:
- Deploy high-resolution cameras, drones or video feeds along transmission lines, substations and remote infrastructure; then, feed live image/video data into AI models trained to detect anomalies such as bent hardware, loose fittings, vegetation encroachment or corrosion.
- Use real-time alerts when anomalies are detected, enabling early intervention rather than waiting for scheduled inspections or failures.
- Combine visual inspection with sensor and environmental data to prioritize field crews, reduce downtime and extend asset life.
Wildfire Monitoring—Borrowing from High-Risk Landscapes: In fire-prone Western states, governments and third-party providers are beginning to leverage imagery, drones and AI to monitor for wildfire ignition risk.
How utilities can apply it:
- Use AI models to continuously scan video or still-image feeds of corridors, remote lines and assets in high-risk zones, flagging signs of risk like heat anomalies, smoke, sagging lines or compromised structures.
- Automate the decision chain: when a risk threshold is crossed, trigger immediate inspection based on the exception rather than waiting for a planned physical system review.
- Combine internal maintenance data with weather, vegetation growth and asset data to generate predictive risk maps that help allocate resources more effectively.
Enhanced Customer Service and System Design Optimization—Lessons from Retail and Telecoms: Industries like retail and telecoms are using AI bots and algorithms to improve customer experience and design processes. For example, in system design, AI tools can propose system layouts potentially using less materials—optimizing cost, accuracy and schedule time.
How utilities can apply it:
- Deploy conversational AI systems (chatbots/virtual agents) for routine customer inquiries—billing, service requests, outage reports—allowing human staff to focus on complex issues.
- Use design-automation AI in system planning: for instance, when designing grid expansions, AI can generate alternative layouts, cost-performance tradeoffs and materials requirements.
- Adopt generative design programs to simulate “what-if” scenarios for system architecture, enabling planners to evaluate more options faster.
Supply Chain, Inventory and Materials Tracking—A Page from Manufacturers’ Playbooks: In manufacturing, AI is used to track inventory in real time, forecast parts demand, identify supply-chain bottlenecks and optimize inventory levels.
How utilities can apply it:
- Use AI to monitor critical spare-parts inventories; forecast demand based on outage history, asset age, weather cycles and project timelines; and automatically trigger restocking or supplier sourcing.
- Implement AI to evaluate multiple suppliers, lead times, delivery risks and cost options in near real-time.
- Integrate inventory data, project schedules and asset-condition monitoring to reduce over- and under-stocking inventory.
Data-Driven Decision Support—From Static Dashboards to Industry 4.0’s Prescriptive Analytics: Utilities have already invested heavily in sensors, smart meters, grid software and dashboards over the past decade. The challenge now is to act on that data like the best of Industry 4.0 and use it to improve decision-making.
How utilities can apply it:
- Move beyond static dashboards to AI-driven analytics that recommend next steps or automatically trigger next-best actions.
- Use AI to analyze historical operational data and real-time sensor streams to forecast equipment failure, system stress, load imbalances or customer demand peaks—then proactively intervene.
- Identify operational inefficiencies that human analysis might miss, enabling cost reductions and reliability improvements.
Getting Over the Fear Factor
The adoption of AI across utilities isn’t a fad. As power demand skyrockets, it is fast becoming a strategic necessity.
But utilities are understandably hesitant. They run critical infrastructure and collect sensitive customer data that raises the stakes for any new technology implementation, especially emerging tools like AI.
Bumps along the road are to be expected. After all, utilities are creating something new. Yet learning from other industries can be an efficient and exciting first step in charting a path to future success.
—Andrew Bordine is Grid Automation Practice Head at Actalent.
