AI in Utility Technology: Use Cases and Benefits

The New Power Source: Why Utilities Turn to AI

In the 2000s, a power plant in Florida, while preparing for a “cold start” (a complete system restart after shutdown), faced a massive catastrophe. Everything seemed under control: automation, tested software, clear instructions. But hidden in the turbine control system code was an error that nobody noticed. The program incorrectly calculated steam flow, ignored emergency signals, and within minutes, a routine technical process turned into a major accident.

Today, energy companies no longer rely solely on rigid programming, they use Digital Twins and AI-based modeling. Before starting a turbine or pump, engineers “run” hundreds of scenarios through a digital copy of the station. AI evaluates system behavior under different loads and detects potential errors before they lead to losses.

And this isn’t just about safety, it’s also about huge savings, it’s more environmentally friendly, it’s about reputation and trust. According to analysts, predictive analytics can reduce energy losses by 15-20% in large power grids. Now imagine the scale if this were implemented everywhere.

That’s precisely why AI for utilities is becoming the undisputed standard today. The energy sector is shifting from reactive management to proactive: don’t wait for the system to break, predict it in advance.

Read also: Welcome to the agentic AI era: Why African businesses are poised to leapfrog the competition

From Data Chaos to Smart Control: The Core of AI for Utilities

Just 10 years ago, a power grid dispatcher could only see part of the picture: readings from a few dozen sensors and even old or outdated data due to recording delays. Now, in a modern company, there are millions of such sensors: meters, voltage sensors, SCADA systems, IoT consumption monitoring, and all of this can be controlled in real time without any delays. But this data stream would be meaningless noise without AI utilities.

Artificial intelligence has become the “brain” that structures chaos and turns it into action. It sees deviations that a human won’t notice: detects water or energy leaks (anomaly detection), automatically balances network loads, and sometimes even predicts exactly where an accident might occur.

Its power lies not only in algorithms but in flawless execution. AI for utilities doesn’t get tired, doesn’t make mistakes, and doesn’t miss details. These aren’t “smart machines,” these are tools that help engineers work with a confidence that simply didn’t exist before.

Companies around the world have already transitioned to this level. For example, Enel started using AI to manage distribution networks back in 2018. National Grid in the UK uses machine learning models to forecast loads and prevent overloads. And Duke Energy in the US implemented AI for equipment diagnostics: the system analyzes turbine vibrations and signals malfunctions in advance.

More details on how AI solutions transform the utilities sector: https://dxc.com/us/en/industries/energy/utilities 

Smart Grids, Smarter Cities

In big cities today, the energy system is a complex ecosystem where solar panels on roofs, wind turbines on the outskirts, batteries near buildings, and energy-saving devices in every home all work together seamlessly. And everything operates in harmony thanks to AI in utilities, which connects these scattered parts into a single network.

1. Integration of renewable sources. For example, solar or wind energy is unstable: when the wind is too strong or too weak, when it’s cloudy. AI analyzes weather forecasts, data from panels or turbines, and automatically regulates when to connect batteries or backup generators to reduce overloads or failures.
2. Self-healing grid is a concept where the network automatically detects damage (wire breaks, voltage drops, line overloads) and redirects energy flow through other segments, minimizing outages. AI detects deviations in near real-time, and algorithms launch that, say, isolate the damaged section and redistribute the load.
3. Analytical insight on loss reduction. One report mentions that in large cities with dense networks, implementing smart grids and intelligent control systems allows reducing electricity losses from 5% to 10%. This is significant savings, both for suppliers and for end consumers.

One such real city is San Diego (USA). They’re actively working on implementing a “smart grid” with integration of solar panels, battery stations, and load forecasting systems. They collaborate with regional electricity commissions to establish standards for integrating renewable energy into the grid. The EU also has directives and regulations on energy efficiency and RES (renewable energy sources) integration that encourage the use of such technologies.

Residents of such cities get benefits: more stable voltage, fewer outages, the ability to sell excess solar energy, lower rates. Service providers can better forecast demand, reduce peak loads, and optimize network maintenance costs. Major players (producers, grid operators, city administrations) gain greater reliability, the ability to avoid expensive accidents, and accelerate recovery after failures.

AI for Customer Experience: From Call Centers to Chatbots

Okay, technology is great, but who is this all for? For people. And this is where AI for utilities can improve people’s user experience.

• Automated services. Instead of waiting an hour to connect with an operator, a consumer can get answers to typical questions (bills, rates, consumption schedule) through a chatbot or voice assistant. This works 24/7, no days off.
• Personalized rates. AI analyzes consumption history, weather trends, usage patterns (when the consumer uses air conditioning, heaters, consumption profile), and offers rates or plans that can save money or give bonuses for “green energy,” time-of-use pricing, etc.
• Faster problem-solving through NLP bots. Bots don’t just answer “yes/no,” they understand context: “My lights flickered twice and then went out,” the bot can check network status, whether there’s an outage in that area, or forward it to dispatch service with an “urgent” tag if voltage data confirms the problem.

Real examples of companies and solutions.

1. Streebo has an AI chatbot/agent solution that integrates with various channels (chat, voice, social media) for energy companies, answers billing inquiries, service requests, outages, and even gives energy-saving tips.
2. Another example is Actionbot, which works in the utility sector, allowing customers to check bills, submit requests, report accidents or outages, all through chat or other digital channels.
3. Perhaps some basic phone operators or lower-level support staff will change roles: instead of handling standard requests, they’ll focus on complex cases, checking deviations, analyzing complaints, and improving systems. New roles emerge: dialogue designers for chatbots, consumption analysts, NLP/machine learning specialists, customer digital experience (CX) managers.

AI for Customer Experience: From Call Centers to Chatbots

The energy sector isn’t just about generators, transmission lines, and substations. It’s millions of customers who receive bills every month, report outages, search for rates, and ask questions. And this is where AI for utilities shows the human side of technology.

Just a few years ago, support services of large companies were literally drowning in calls. Today, a customer can write in a messenger and get a response from an intelligent bot within seconds. Such systems based on natural language processing (NLP) no longer just recognize a request but understand context. If a user writes: “The lights have been flickering for three days now,” the bot doesn’t send a template response but checks the line status in that area, verifies consumption, and only then offers a solution.

Streebo created an AI assistant for energy companies that handles over 80% of typical customer requests without human participation. In Canada, Hydro-Québec launched a virtual assistant “Hilo” that doesn’t just answer questions but also offers personalized energy efficiency tips. And in the US, Duke Energy uses an AI platform to analyze customer behavior, it helps predict when a consumer might exceed their rate or need technical consultation.

Another interesting story is Actionbot, a platform for the utility sector that allows reporting accidents, submitting requests, or getting bills right in chat. The system connects to CRM and IoT data and can even “understand” if a customer is frustrated, offering to transfer the conversation to a live operator.

And no, this isn’t about replacing people. This is about smart role distribution. AI takes on repetitive routine work, while operators focus on complex cases, empathy, and building long-term relationships with customers.

Yes, some positions (such as first-level helpline operators) have become a thing of the past. But instead, new ones have appeared: customer data analysts, dialogue designers for bots, model training specialists, customer digital experience (CX) managers. In other words, where artificial intelligence takes on routine work, people become service architects.

Challenges Ahead: Human + Machine Collaboration

Despite all the advantages, implementing AI in utilities isn’t an easy walk. And not just because of technological nuances.

• Shortage of specialists. Today, there’s a lack of people who simultaneously understand energy, data analytics, and machine learning. Engineers are learning to think like data scientists, and analysts like energy professionals.
• Integration of legacy systems. Many power grids still operate equipment that’s 20-30 years old. Connecting it to new AI platforms is like trying to put a 5G modem on a VHS cassette.
• Trust in algorithms. When it comes to critical infrastructure, engineers aren’t inclined to simply “believe” in machine decisions.

We’ve all seen “Terminator” or “Black Mirror,” so we’ll never be able to trust artificial intelligence 100%. And maybe that’s even good. Because people will always be needed to control, train, and improve AI.

The real power of AI utilities isn’t in replacing humans but in creating a “Human + Machine” team. When an algorithm detects a problem and an engineer decides how best to act, when a bot accepts a request and a service specialist understands what’s behind it. This isn’t automation, it’s a new format of collaboration.

The Future of AI in Utilities: Sustainable, Predictive, Human-Centric

AI in utilities today stands where electrification stood a hundred years ago. It’s changing the very logic of energy management. Networks are becoming predictable, services are becoming personalized, solutions are becoming ecological.

Companies implementing AI are already reducing losses, cutting CO₂ emissions, and moving closer to “net zero” goals. But the main thing is the human dimension: operators get better tools, customers get better service, cities get more stable infrastructure.

Technologies that once seemed complex are now becoming invisible, like the very air that modern energy systems “breathe.”