Out of Duke’s $103 billion capital spending plan—and counting—about 60% is dedicated to building new power generation, while the rest is going to grid expansions and upgrades—essentially the poles and wires. Explore the enhanced functionalities in Reality AI Tools and the new features integrated into Renesas e² studio. Reality AI Utilities reduce the complexity of creating lightweight ML models while improving and stabilizing results. As AI adoption accelerates, the race to build and supply power infrastructure may become one of the most important.

Zero-Touch Network Operations

• There is also an increasing body of evidence pointing towards the need of utilities to embrace a pay-for-performance (P4P) approach, particularly to their energy efficiency programs.
• Additionally, AI empowers predictive analysis, where data from smart meters and IoT devices are used to predict potential issues and schedule maintenance.
• The future of utilities is not just digital—it is AI-powered, adaptive, and autonomous.
• KPMG has market-leading alliances with many of the world’s leading software and services vendors.
• Doing so means improved decision-making, optimized operations, reduced risk, improved customer service and increased safety.

⦁ Optimize energy production and consumption, reducing energy waste and lowering costs. Resistance to change and skepticism about AI’s effectiveness can hinder the adoption of AI technologies, especially in an industry as traditionally conservative as utilities. With the increasing use of customer data in AI applications, adhering to data privacy laws and regulations becomes a complex challenge. The sensitivity of customer data and the stringent requirements of regulations like GDPR or CCPA complicate the deployment of AI models that rely on vast amounts of personal information.

Industry Software

Instead of a service call to check the customer’s meter, Bidgely’s software analysis identified the customer usage causing the bill spike, he added. Future workforces will shift from executing operational tasks to orchestrating autonomous ecosystems. Employees will act as “AI supervisors,” guiding decision frameworks, validating outcomes and ensuring that systems operate within ethical and regulatory guardrails. As autonomy scales, governance must become real-time, data-driven and embedded, with humans holding final accountability.

• They schedule maintenance based on actual wear patterns instead of calendar dates.
• Transform complex datasets into intuitive visual formats that reveal actionable patterns and trends.
• “But any company that has not taken its internal knowledge base into a generative AI model that can be queried as needed is not leveraging the data it has long paid to store,” said NVIDIA Senior Managing Director Marc Spieler.
• The platform offers operators a comprehensive view of their utility network and allows them to monitor real-time data to minimize waste, detect maintenance issues early, and recommend cost-saving measures.

Preparing electric utilities for the energy transition

The same climate conditions that have made wildfire seasons longer and more destructive also increase the mechanical stress on poles and conductors through wind, drought-driven ground movement, and pest pressure on wood structures. In February 2025, it launched Cloneable Field for automated infrastructure inspection targeting the energy sector. Alongside the fundraise, the company is now launching an agentic product that codifies expert knowledge and deploys it as scalable AI agents. According to the World Resources Institute, Virginia is the state that houses the most data centers, with 566 in the state as of January 2026. Over the past few years, interest in generative artificial intelligence has ballooned – and so have the resources required to support it.

• But with better targeting – and marketing messages tailored to individual homeowners – a utility would only need to sign up 700 homes.
• More recently, one of the key drivers of change in the utility industry is the rise of artificial intelligence (AI).
• AI can also monitor the performance of energy assets in real-time, identifying areas where energy is being wasted or where equipment needs maintenance, reducing energy losses and downtime.
• This transformation leads to cost reduction through the use of AI, while also enhancing service reliability for customers.
• It’s about taking a Netflix kind of approach to personalization to forge a mutually beneficial relationship with utility customers.

But you can’t avoid utility sector digital transformation because competitors are already moving. At KUBRA, we’re partnering with utilities to build intelligent, scalable solutions that meet tomorrow’s energy demands today. From AI-powered IVR integrated with KUBRA EZ-PAY+™ to predictive billing analytics and late payment detection, our tools are designed to streamline operations and enhance the customer experience.

Making Renewable Energy Less Chaotic

Electricity demand is accelerating at an unprecedented pace, putting significant https://travelusanews.com/advanced-technologies-in-telephone-technology-the-emergence-of-new-smartphones.html pressure on utilities to expand capacity without compromising supply reliability or affordability. AI technologies can support this transition through smarter demand forecasting and operational optimization. AES, transitioning from fossil fuels to renewables, needed predictive tools for energy output, maintenance, and load distribution. Collaborating with H2O.ai, AES deployed predictive maintenance programs for wind turbines, smart meters, and optimized its hydroelectric bidding strategies. Most companies waste money on AI in utility industry projects that sound impressive but don’t solve real problems.

Ongoing Evaluation: Optimizing AI in Utilities

In collaboration with Microsoft, OPG developed ChatOPG, an AI-powered virtual assistant that answers queries, provides information, and acts as a personal assistant. By using Generative AI, Octopus Energy streamlined its customer support process, ensuring quick and accurate responses, demonstrating AI’s potential in the utilities sector. This AI-driven approach improved maintenance accuracy, minimized emissions, and increased the reliability of the energy grid. This AI-driven approach not only streamlined operations but also supported Con Edison’s commitment to sustainability and customer-focused energy solutions. With 71% of utility executives recognizing the promise of intelligent, adaptable robots, AI-powered machines will become integral to field operations and infrastructure maintenance.