AI Utilities: Top 20 Use Cases & Case Studies
AI utilities refer to AI solutions leverage real-time sensor, meter, and imagery data to forecast demand, automate inspections, optimize distribution, and improve customer service.
Learn 20 specific use cases with real-life examples of AI utilities:
AI utilities use cases & real-life examples
AI utilities for energy
1. Autonomous operations in power plants
AI automates plant inspections by analyzing data from cameras and sensors in real time, reducing reliance on human workers and enhancing safety by detecting leaks or other hazards promptly. This automation meets the demands of an aging workforce and enhances plant efficiency.
Real-life example:
Duke Energy, aiming to achieve net-zero methane emissions by 2030, faced challenges in monitoring natural gas pipelines for leaks. They partnered with Microsoft and Accenture to develop a new platform using Microsoft Azure and Dynamics 365 to integrate satellite, ground sensor data, and AI for real-time leak detection and response.
The platform assessed emissions data, prioritized repair areas, and dispatched crews promptly, helping to reduce greenhouse gas emissions.
- Provided graphic dashboards for prioritizing leak repairs
- Enabled precise geolocation data for quicker repairs
- Scalable to other emission sources and equipment. 1
2. Energy demand forecasting
Efficient utility distribution relies on accurately forecasting energy and water demand, which constitutes a major portion of operational costs. AI in energy demand forecasting helps utility companies manage supply and demand by analyzing factors such as weather patterns, user behavior, and market prices by:
- Forecasting energy demand and optimizes supply distribution
- Predicting renewable energy availability and balances with other sources
- Enabling price optimization based on historical data and potential competitor responses
- Encouraging efficient consumer behavior by notifying users about peak periods.
Real-life example:
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.
The platform enabled AES to anticipate component failures, optimize repair costs, and manage demand prediction, helping the company reduce costs and increase reliability.
- Saved $1 million annually by reducing unnecessary repairs
- Achieved a 10% reduction in customer outages
- Addressed 85 operational challenges over two years.2
3. Energy prosuming
AI solutions for energy prosumers (consumers who also produce energy, e.g., via rooftop solar) help users manage self-produced energy from sources like solar panels or wind turbines. These solutions optimize the use of renewable energy and enable users to sell surplus power back to the grid.
- Balances supply and demand based on consumption peaks and weather conditions.
- Integrates with smart meters for efficient energy management.
- Supports surplus energy trading or sharing with the local grid.
4. Industrial digital twins for power generation
AI-driven digital twins can simulate and predict maintenance needs, optimize performance, and reduce downtime of power generation sites like wind turbines. These models can accurately forecast issues like corrosion, minimizing disruptions and increasing reliability in power supply.
Real-life example:
For instance, Google’s neural network improved wind energy forecast accuracy, boosting financial returns by 20%. This predictive capability allows for efficient scheduling of energy production and consumption, maximizing resource utilization and profitability. 4
Real-life example:
Siemens Energy’s digital twin for heat recovery steam generators predicts corrosion, potentially saving utilities $1.7 billion annually by reducing inspection needs and downtime by 10%. Siemens Gamesa’s digital twin simulates offshore wind farm operations 4,000 times faster, optimizing turbine layouts and cutting energy costs. 5
5. Power grid simulation
AI-driven grid simulations allow utilities to model power flow, schedule outages, and test grid resilience, especially with the increased integration of renewable energy sources. This optimizes maintenance and outage management, ensuring minimal impact on customers.
Real-life example:
ElektroDistribucija Srbije (EDS), Serbia’s distribution system operator, needed to modernize its legacy electricity grid to support renewable energy integration and improve reliability across a network serving 3.8 million customers. To address this, EDS implemented EcoStruxure ADMS and EcoStruxure DERMS from Schneider Electric to digitize grid operations.6
Results:
- 10–15% reduction in network losses
- ~20% reduction in outages
- Improved integration of distributed renewable energy resources
- Condition-based maintenance improving operational efficiency
- Increased grid reliability for 3.8 million customers
6. Smart Homes as energy hubs
AI-based smart home systems help homeowners monitor and adjust energy usage, reducing costs and minimizing demand on the grid through better load management.
7. Smart meters for real-time power flow
AI-driven smart meters integrate with distributed energy resources to balance demand and supply in real-time, supporting grid resilience and decarbonization efforts.
Real-life example:
Con Edison, a utility company, aimed to reduce operational costs and environmental impact by leveraging artificial intelligence. AI-powered tools helped lower power generation costs and reduce CO₂ emissions, empowering customers with more control over energy usage.
This AI-driven approach supported Con Edison’s commitment to sustainability and customer-focused energy solutions.
- Reduced power generation costs and CO₂ emissions
- Enabled enhanced customer energy management
- Promoted eco-friendly and customer-centric operations.8
8. AI-managed EV charging
A neighborhood of electric vehicles (EVs) can easily overload the local grid, but AI can optimize charging schedules by shifting consumption to off-peak hours and drawing battery power back during peak demand. This collective pooling creates a “virtual power plant” (VPP), a software-coordinated fleet that behaves like a single, dispatchable power source.
Real-life example:
ChargeScape, a joint venture of BMW, Ford, Honda, and Nissan, aggregates EVs into a virtual power plant for a dozen-plus US utilities, using AI-driven load management to shift charging and export stored energy during peak grid events. This turns EVs from a grid liability into a dispatchable asset.
- Each vehicle delivers ~5 kW of managed-charging demand reduction and up to ~20 kW of export via bidirectional (V2G) charging.9
- Aggregator EnergyHub cut EV charging peaks by up to 50% through AI distribution optimization, shifting 38.3k+ MWh of load across its VPP fleet in 202510
- NV Energy used AI to rapidly identify EV-owning customers and target them for managed-charging programs.11
AI utilities for waste, water and wildfire
9. Waste management
AI in waste management collects, and analyzes data on waste types, volumes, and patterns, predicts future waste levels, identifies and sorts recyclable materials and discarded food types with computer vision and ML. It can inform pick-up schedules, optimize waste disposal and recycling processes, improve resource management.
Real-life example:
Materials recovery facilities (MRFs) struggle with contaminated single-stream waste, where less than half of inbound material is typically recovered and manual sorting is slow, hazardous, and hard to staff. Bryson Recycling and other European MRFs deployed Recycleye Vision and Recycleye Robotics, which use computer vision and deep learning to identify and robotically pick recyclables from mixed streams, leading to:
- Raised output purity on an aluminum line by 8% and output market value by around 20%,
- Increased purity on a fibre line by 12%,
- Delivered up to 33,000 robotic picks over a 10-hour shift, roughly twice manual sorting speed.12
10.Water quality monitoring
AI can enhance water quality monitoring by analyzing water flow and detecting contaminants in real time. AI-enabled sensors deployed in water systems identify harmful bacteria and particles, enabling faster responses to potential health risks.
- Monitors water quality continuously, detecting contaminants in real time.
- Improves transparency and control over water supply systems.
- Supports quick actions in response to health risks.
Real-life example
Fluid Analytics uses AI-powered software, robotics, and IoT to optimize urban water systems with predictive models trained on varied pipeline data. Cities, especially in India, sought their help to locate leaks, reduce water loss, and prevent flooding due to outdated infrastructure and inspection methods. Fluid Analytics’ results include:
- Monitoring over 400 million gallons of urban wastewater daily
- Mapping drainage channels to prevent severe flooding near Mumbai airport
- Facilitating early detection of waterborne diseases and preventing outbreaks, such as hepatitis-A.14
11. Wildfire risk detection and prevention
Computer-vision cameras scan for smoke plumes, while ML models rank circuits by fire risk using weather, fuel-moisture, and vegetation data.
Real-life example:
Pacific Gas & Electric (PG&E), facing severe wildfire exposure across its California territory, deployed 630+ AI-powered detection cameras from Pano AI and vegetation-intelligence models from Overstory to spot ignitions early in ~90% of its high-fire-risk territory.
- Reported a nearly 50% drop in ignitions with vegetation as a suspected cause in 2025 versus the prior year.
- Improved detection speed and helped cut vegetation-related ignitions.15
Industry-agnostic AI utility use cases
12. Automated asset maintenance
Energy and utilities companies struggle to detect defects in critical infrastructure, leading to costly breakages. AI analyzes aerial imagery, LiDAR, drone and satellite data to identify equipment issues or vegetation risks that could damage infrastructure.
For instance, AI-powered image recognition and computer vision can analyze drone-captured images of assets, allowing for rapid identification of potential failures. This proactive monitoring minimizes service disruptions and reduces fire hazards around power lines, eventually optimizing resource scheduling.
Real-life example:
Exelon, a large energy company, sought to improve its grid maintenance and inspection process. Using NVIDIA’s AI tools for drone inspections, Exelon enhanced its defect detection capabilities, creating labeled examples for real-time assessment.
This AI-driven approach improved maintenance accuracy, minimized emissions, and increased the reliability of the energy grid.
- Enhanced grid defect detection through AI-driven drone inspections
- Increased maintenance efficiency and grid reliability
- Reduced emissions through optimized inspection processes.16
13. Automated customer service experience
Utility suppliers can enhance customer engagement by predicting water and energy consumption with AI, allowing for dynamic pricing strategies. By analyzing usage patterns, AI can suggest optimal usage times for cost savings, such as recommending later charging times for electric vehicles. This personalized approach improves customer satisfaction and supports targeted marketing efforts, increasing loyalty and revenue.
Real-life example:
Octopus Energy, an energy provider, sought to improve its customer service through enhanced email response quality. They implemented Generative AI to automate responses to customer emails, achieving an 80% customer satisfaction rate, surpassing the 65% rate of human agents.
By using Generative AI, Octopus Energy streamlined its customer support process, ensuring quick and accurate responses, demonstrating AI’s potential in the utilities sector.
- Achieved 80% customer satisfaction in AI-driven email responses
- Outperformed trained human staff’s satisfaction score by 15%
- Showcased potential for further AI integration to improve customer loyalty.17
14. Fleet optimization for utility trucks
The energy sector’s complex supply chains require efficient logistics management. AI enhances coordination between operations teams and warehouses, optimizing fleet management and route planning.
For instance, AI optimizes utility truck routes during outages and extreme weather, reducing travel times and improving response times to restore services more quickly. This leads to improved delivery times, reduced operational costs, and better alignment with market demand.
Real-life example:
Xylem Kendall, a vegetation management company operating over 5,500 fleet assets, faced safety concerns from distracted driving, which data linked directly to cell phone use.
To solve this, they deployed next-gen telematics and Motive AI Dashcams to track safety scores and installed hands-free phone holders nationwide, achieving the following results:
- 5% improvement in overall driver safety scores.
- Real-time risk mitigation by detecting distracted behavior and providing immediate in-cab alerts to drivers.
- Data-driven post-trip coaching that allowed supervisors to utilize camera footage for targeted safety training.18
15. Substation safety and security
AI-based video analytics improve substation security by detecting unauthorized intrusions and monitoring worker safety, enhancing compliance and reducing potential incidents.
Real-life example:
US electric utilities face a sharp rise in physical attacks and copper theft at substations, compounding the pressure to comply with NERC CIP-014 physical security requirements. Yet, conventional perimeter alarms drown operators in false triggers. For example, a large substation can generate 50 to 200 perimeter alarms per shift, with over 95% of them being nuisance triggers caused by wildlife, wind, or patrol vehicles.
Several utilities are installing AI video analytics with sensor fusion onto existing cameras to:
- Classify what tripped an alarm (person, vehicle, animal), cutting the nuisance alerts that cause operator alarm fatigue
- Fuse thermal and visual sensors so a second sensor must confirm before an alert is escalated, reducing false dispatches
- Run the same thermal and visual hardware for early detection of equipment temperature anomalies, adding predictive-maintenance value beyond security
- Enable remote, real-time monitoring and faster law-enforcement coordination during an incident.19
16. Virtual assistants in call centers
AI virtual assistants support customer service by managing call surges, assisting with FAQs, and providing usage insights, which improves customer experience and reduces operating costs.
Real-life example:
Ontario Power Generation (OPG), a major Canadian electricity producer, aimed to improve internal efficiency and support for its employees. In collaboration with Microsoft, OPG developed ChatOPG, an AI-powered virtual assistant that answers queries, provides information, and acts as a personal assistant.
The chatbot supports productivity, enhances safety, and streamlines performance by offering workers easy access to needed information.
- Improved employee productivity and access to information
- Enhanced safety and operational efficiency
- Promoted AI integration in daily operations for better performance.20
AI utilities for telecom
17. Zero-Touch Network Operations
Zero-touch network operations involve using AI to automate network management tasks, reducing the need for human intervention. This includes self-monitoring, self-healing, and automatic optimization of network resources. By integrating digital twins and machine learning, telecom operators can achieve higher service reliability and operational efficiency.
Real-life examples: Ericsson implemented AI-driven zero-touch operations, leveraging machine learning and digital twins for autonomous management. This enhanced service reliability and reduced manual tasks, boosting operational efficiency. As a result, Ericsson could
- Enable autonomous operation with minimal oversight
- Increase network reliability
- Improve service efficiency.21
18. Network Optimization and Management
AI-driven network optimization and management systems analyze large volumes of data to predict and address potential issues before they impact services.
Real-life example: Nokia’s AVA platform used AI-based predictive analytics for real-time network management, leading to:
- Enhanced real-time network performance
- Reduced downtime
- Improved user satisfaction.22
19. 5G Network Slicing
By enabling network function virtualization, AI supports 5G network slicing and allows telecom operators to create and allocate network segments for different use cases and customer needs.
Real-life example: Huawei used AI to support 5G network slicing, dynamically allocating resources to provide tailored services and maximize network utility. This way, Huawei achieved:
- Tailored services for different use cases
- Improved resource management
- New revenue opportunities.23
20. Data Traffic Management
AI-powered data traffic management optimizes the allocation of network bandwidth based on real-time demand. This ensures that during peak times, network performance is maintained, leading to a better user experience and more efficient use of resources.
Real-life examples: Ericsson’s AI solution optimized data traffic management by adjusting bandwidth allocation in real-time, ensuring consistent network performance. This way,
- Optimized bandwidth usage
- Stable network performance during peak times
- Enhanced service quality.24
Why should we use AI in utilities?
Here are the reasons to deploy AI in utilities:
Electricity demand surge
Electricity demand requires capacity expansion without compromising supply reliability or affordability. AI technologies can support this transition through smarter demand forecasting and operational optimization.
- Electricity demand is projected to increase 1.4% annually through 2032, resulting in a 46% cumulative rise.25
- In the US, 120 GW of additional electricity demand is expected by 2030, including 60 GW from data centers, roughly equivalent to Italy’s 2024 peak energy use.26
- In the US, residential electricity prices rose about 13% from 2022 to 2025.27
- AI-driven scheduling can deliver 25–30% improvements in field productivity, enhancing workforce and asset management.28
Investment opportunities in utilities
The convergence of digitalization and infrastructure modernization is creating significant investment potential within the utilities sector. AI-enabled analytics can drive smarter capital allocation, helping utilities capture value from emerging demand trends and optimize asset performance.
- Utility stocks are currently undervalued by 5%, not yet reflecting the growing impact of data center demand.29
- U.S. electric companies are expected to invest $1.1 trillion between 2025 and 2029 to upgrade aging infrastructure and expand grid capacity.30
- Through machine learning insights, utilities can reallocate up to 80% of capital based on asset health, strengthening reliability and resilience.31
AI analytics can uncover consumption and pricing trends, driving smarter investment decisions and improving ROI. AI-driven asset management can help utilities prioritize where to invest and prevent overbuilding, particularly as infrastructure constraints and inflation raise costs across the supply chain.
Data center demand growth
Data centers operations increase energy consumption which can be optimized with AI:
- Data center electricity demand could double by 2030, with a 131% increase expected by 2032 in a high-growth scenario.32
- Large plans by AI industry consume as much power as entire cities.
- For example, OpenAI and Nvidia’s recent 10-gigawatt data center partnership demanding as much electricity as New York City during peak summer use.33
- Renewable projects now make up over 90% of all new capacity waiting for grid connections, highlighting how AI-enabled planning and predictive tools will accelerate the clean energy transition.34
- AI has improved the heat rate or yield of fossil and renewable generation assets by 2–5%, delivering measurable efficiency gains.35
AI-driven optimization enables energy efficiency gains without sacrificing performance. Predictive analytics can balance workloads to reduce operational waste and enhance sustainability.
Solutions under AI utilities
Energy companies can benefit from these cutting edge technology advances:
Automation solutions for utilities
Automation tools include:
- Workload Automation: Workload automation solutions streamline and manage repetitive tasks across various systems, enabling utilities to increase operational efficiency and reduce manual errors while ensuring critical processes run smoothly.
- Batch Scheduling: Batch scheduling software organizes and executes large volumes of tasks or processes in groups at scheduled times, allowing utilities to optimize resource allocation and ensure timely completion of jobs without disrupting ongoing operations.
- Enterprise Job Scheduling:Enterprise job scheduling software coordinates and prioritizes tasks across an organization’s IT landscape, helping utilities improve service delivery, enhance system utilization, and maintain consistent performance by ensuring that jobs are executed in the correct order and on time.
- AI-driven cybersecurity automation: As utilities become increasingly digitized, AI-powered threat detection systems autonomously identify anomalies and neutralize cyber risks in real time. These solutions strengthen operational resilience and regulatory compliance across digital infrastructures.
Machine learning algorithms for utilities
These algorithms enhance decision-making by identifying patterns in consumption data, facilitating demand-side management strategies and personalized energy solutions for consumers. Here are some of these tools:
- Natural Language Processing (NLP): NLP can improve customer service chatbots and virtual assistants, providing instant support and improving customer engagement by understanding and responding to inquiries in real time.
- Computer Vision: Computer vision leverages image analysis from drones and cameras to inspect infrastructure, enabling faster and safer identification of equipment issues compared to manual inspections.
- Predictive analytics: Predictive analytics tools use historical data to forecast demand and detect potential failures in infrastructure, allowing utilities to preemptively address issues and optimize resource allocation.
- Reinforcement learning (RL): RL enables systems to learn optimal strategies for energy distribution and pricing through continuous feedback loops. Utilities can leverage RL for adaptive grid management, dynamic pricing, and real-time optimization of decentralized assets.
- Explainable AI (XAI): As AI models become more complex, explainable AI ensures transparency and interpretability in decision-making, supporting regulatory compliance and building stakeholder trust in automated systems.
Internet of Things (IoT) for utilities
IoT devices and sensors for real-time monitoring of grid performance and energy consumption, including examples like:
- Smart meters: Smart meter solutions provide real-time data on energy consumption, enabling accurate billing and efficient energy management.
- Real-time monitoring systems for grid reliability: These systems track grid performance continuously, allowing utilities to detect issues early and maintain reliable service.
- Condition-based maintenance (CBM): CBM monitors equipment health to schedule maintenance when needed, reducing costs and preventing unexpected failures.
- Edge computing integration: Edge computing processes IoT data locally, minimizing latency and enabling immediate action. This is particularly valuable for grid fault detection, substation automation, and decentralized control where milliseconds matter.
- 5G Connectivity: High-speed, low-latency 5G networks enhance the responsiveness of IoT-enabled devices and sensors, ensuring reliable data flow for mission-critical energy operations.
Generative AI for utilities
Generative AI uses advanced algorithms and machine learning to create predictive models and simulations from historical data and various scenarios. In the utility sector, this technology optimizes energy distribution and improves forecasting accuracy. For example, generative AI helps with:
- Renewable energy integration to evaluate how to incorporate renewable energy sources by simulating their impact on overall grid stability and reliability.
- Asset management by allowing utilities to schedule repairs or upgrades based on projected performance and risk factors.
Agentic AI for utilities
Agentic AI can autonomously plan, act, and adapt to achieve defined goals with minimal human intervention by combining capabilities of generative AI and predictive AI. In the utility sector, agentic AI can coordinate complex, multi-step processes that traditionally required manual oversight. The goal is to create self-governing energy systems that can balance reliability, sustainability, and cost efficiency. For example:
- Autonomous operations orchestration: Agentic AI can independently monitor grid conditions, forecast demand, and trigger necessary control actions in real time, enhancing system resilience and reducing downtime.
- Dynamic decision-making: By continuously evaluating data from sensors, IoT devices, and predictive models, agentic agents can optimize resource allocation, reroute energy flows, or prioritize maintenance activities without waiting for human input.
- Collaborative multi-agent systems: Multiple AI agents can work together across generation, distribution, and customer management systems, enabling self-optimizing networks that enhance efficiency and sustainability outcomes.
Data infrastructure and cloud platforms
Some of the data solutions include:
- Cloud-native platforms: Provide the agility and scalability to manage massive data volumes from connected assets, enabling real-time analytics and AI deployment at enterprise scale.
- Data lakes and data mesh architectures: Consolidate heterogeneous data sources, from grid sensors to customer systems, into unified, accessible environments that empower predictive modeling, GenAI, and digital twin development.
- Streaming analytics and event processing: Process and analyze high-velocity data streams from IoT networks and smart grids to enable real-time operational insights and automated decision-making.
- Data governance and quality management: Ensures data integrity, traceability, and compliance across distributed systems, building trust in AI-driven decisions and regulatory reporting.
Digital twins for utilities
Digital twins create virtual models of physical assets, allowing utilities to simulate and analyze performance under various scenarios, leading to better asset management and operational efficiency. By processing various data sources, these models enhance operational efficiencies and compliance with environmental standards.
These systems can result in energy savings and carbon footprint reductions, supporting sustainability goals.
Decentralized energy and resource management
One way to centralize and manage energy resources is integrating to them by using tools like:
- Smart Grids: Smart grid solutions analyze real-time data to balance energy flow and integrate renewables. Leverages AI to analyze data from connected devices, facilitating real-time adjustments to energy flow, improving grid resilience, and enhancing integration of renewable energy sources.
- Distributed Energy Resource Management Systems (DERMS): These systems can manage decentralized resources like solar and battery storage. Coordinates the management of decentralized energy resources like solar and batteries, optimizing their contribution to the grid while ensuring reliability.
- Energy Management Systems (EMS): EMS can integrate AI algorithms to optimize energy production, storage, and consumption, leading to more efficient operations and reduced costs.
- Blockchain and distributed ledger technologies (DLT): Enhance transparency and security in decentralized transactions. Utilities can implement blockchain for peer-to-peer energy trading, automated settlement, and carbon credit tracking, ensuring accountability and trust in distributed networks.
Benefits of AI in utilities industry
AI helps utility companies to:
- Simplifying complexity: AI can simplify intricate workflows within the energy and utilities sector by using AI assistants to optimise processes, simulate operations, diagnose real-time issues, ensure supply chain traceability, and provide immediate technical support. This leads to increased efficiency, reduced costs, and minimized downtime.
- Driving cost and energy efficiency: Generative AI solutions enhance energy efficiency and significant cost savings by offering a holistic view of operations. This allows power companies to accurately measure emissions and optimize processes, thereby accelerating the energy transition and promoting sustainability and operational excellence.
- Scaling innovation: Collaborations like those with AWS leverage a vast partner network and industry expertise to rapidly adopt advanced technologies, including generative AI. This helps utility companies scale innovative clean energy technologies efficiently, allowing them to meet energy demands while facilitating the sector’s transition to cleaner practices.
- Generating data-driven strategy: AI assists with data strategy, helping utilities make risk-based replacement and maintenance decisions by analyzing customer risk, safety, and environmental factors. For instance, generative AI combined with ML can process images and videos to identify defects in supply lines, reducing maintenance costs and maintaining reliability.
- Ensuring maintenance: Generative AI combined with ML improves maintenance by detecting and predicting equipment issues. It offers interactive troubleshooting, helping field workers quickly resolve technical issues.
Add as preferred sourceAI utilities challenges
Here are some challenges of adopting AI in utility industry:
- Data privacy: Training AI systems requires large amounts of data, raising concerns about customer data privacy. While there’s potential to optimize this data to better understand customer needs, ensuring privacy protection remains a significant challenge.
- AI bias: AI systems can exhibit biases, which may lead to unfair treatment of customers or employees. Human oversight is necessary to address AI biases and ensure that AI implementation meets ethical standards. Although training systems can reduce bias, it may not eliminate it entirely, making human supervision crucial.
- Legacy system integration: Much of the grid runs on decades-old SCADA, OT, and billing systems. Connecting AI models to this infrastructure is often the single largest cost and delay in a utility AI project.
- Regulatory and compliance constraints: Utilities are rate-regulated. New AI-driven investments, dynamic pricing, and automated decisions must satisfy public utility commissions, explainability and audit requirements. This slows deployment relative to unregulated industries. Learn more on AI compliance.
- Data quality and availability: Models are as good as the meter, sensor, and asset data behind them. Sparse, inconsistent, or poorly labeled historical data limits forecasting and predictive-maintenance accuracy. Explore more about AI data quality challenges and solutions.
- Cybersecurity and expanded attack surface: Connecting more assets (IoT sensors, smart meters, edge devices) and automating control actions increases exposure. AI systems themselves can be targets (data poisoning or model manipulation), making security a prerequisite, not an add-on.
Discover other AI risks and challenges.
Conclusion
AI is transforming the utilities sector by enhancing efficiency, optimizing energy use, and enabling advanced simulations through technologies like digital twins. From power grid modeling to predictive maintenance, AI use cases are proving their value in both operational and strategic domains.
Still, effective adoption depends on addressing key challenges such as data quality, integration with legacy systems, and regulatory constraints.
Further reading
Explore more on AI in other industries:
Cite this research
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@misc{dilmegani2026,
author = {Dilmegani, Cem},
title = {{AI Utilities: Top 20 Use Cases & Case Studies}},
year = {2026},
month = jul,
howpublished = {\url{https://aimultiple.com/ai-utilities}},
note = {AIMultiple. Retrieved July 13, 2026}
}Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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