AI Utilities: Top 25 Use Cases & 48 Case Studies
AI utilities are AI tools that use real-time sensor, meter and imagery data to forecast demand, automate inspections, optimize distribution and improve customer service.
Learn 20 tools and 25 specific real-life use cases of AI utilities:
This chart is based on the 47 AI utilities case studies we compiled and grouped by their main use case:
- Grid & energy: EV charging and flexibility, demand forecasting, grid simulation, grid monitoring, and digital twins
- Customer & CRM: AI-assisted contact centers, field force coordination, sales insights, and personalized energy
- Back-office: meter-to-cash billing and low-code process automation
- Assets & environment: wildfire detection, asset inspection, substation security, water quality, waste sorting, and fleet safety
- Data & knowledge: internal AI assistants, utility data platforms
- Telecom: network automation, network optimization, network slicing and data traffic management
See the full case study list for sources and details.
AI utilities for energy
1. Autonomous operations in power plants
AI analyzes camera and sensor data in real time to spot leaks and other hazards, so fewer inspections need a person on site.
Real-life example for power plants
Duke Energy, which targets net-zero methane emissions by 2030, needed a better way to find leaks in its natural gas pipelines. With Microsoft and Accenture, it built a platform on Azure and Dynamics 365 that combines satellite data, ground sensors and AI to detect leaks and dispatch repair crews.
- Dashboards rank leaks by repair priority
- Precise geolocation sends crews to the exact leak
- The platform can extend to other emission sources. 1
2. Energy demand forecasting
Utilities buy and dispatch energy and water based on expected demand, so forecast errors raise costs directly. AI models combine weather, customer behavior and market prices to:
- Forecast demand and plan supply
- Predict wind and solar output and balance it with other sources
- Set prices using historical data and expected competitor moves
- Alert customers to peak periods so they shift usage.
Real-life example for energy demand forecasting
Plenitude, Eni’s energy retail and renewables company, uses Databricks to forecast customer energy demand and the output of its wind and solar plants, and to group customers by likely needs.
- Up to 60 use cases in production
- Up to 500 reports and dashboards running on the platform.2
3. Energy prosuming
Prosumers are customers who also produce energy, for example with rooftop solar. AI decides when they should use, store or sell that energy back to the grid.
- Schedules usage and storage around consumption peaks and weather
- Sells or shares surplus power with the local grid.
4. Industrial digital twins for power generation
A digital twin is a virtual copy of a plant or grid fed with live data. Operators use it to test changes and to predict faults such as corrosion before they cause outages.
Real-life example for industrial digital twins
Google DeepMind trained a neural network to forecast wind farm output, which raised the value of that wind energy by about 20%.4
Real-life example for power generation
AcegasApsAmga, an Italian distribution grid operator, began building a digital twin of Trieste’s medium- and low-voltage grid with Siemens’ Gridscale X. The twin can simulate, monitor and manage energy flows in real time to keep power stable for ships plugged in at the Port of Trieste.5
5. Power grid simulation
Grid simulation models power flow, so utilities can plan outages and test how the grid handles more renewables before making changes.
Real-life example for power grid simulation
ElektroDistribucija Srbije (EDS), Serbia’s distribution system operator, implemented to EcoStruxure ADMS and EcoStruxure DERMS from Schneider Electric to digitize grid operations and modernize its legacy electricity grid to support renewable energy integration. 6
Results:
- 10–15% reduction in network losses
- ~20% reduction in outages.
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
Smart home systems track appliance energy use and shift loads such as heating and EV charging to cheaper, off-peak hours.
Real-life example for smart meters
SMUD, which serves 1.5 million people, couldn’t see EV charging behind its meters and relied on DMV data to track EV adoption. It tested NET2GRID’s AI app, which runs inside Itron smart meters and detects charging sessions in real time.
- Detected EV charging in 97% of homes with EVs (387 of 840 households)
- 87% detected within 7 days, compared with 3+ months for earlier tools
- Data now supports transformer protection and demand response8
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. Pooled together, these cars form a virtual power plant (VPP): a fleet that software controls like a single power source.
Real-life example for AI-managed EV charging
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.
- 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 Bidgely’s AI-based EV detection on smart-meter data to find EV owners charging during peak hours. From 20,241 customers, the AI identified 419 EV users and narrowed them to 103 high-value customers responsible for 63% of the load-shifting potential.
- 3x higher load shift per car than the average population
- ~1 kW per car across 20+ managed-charging events, compared with 0.2–0.3 kW typical in the industry.11
AI utilities for waste, water and wildfire
9. Waste management
AI tracks waste types and volumes, predicts how full bins will get, and uses computer vision to sort recyclables. Collectors use the forecasts to plan pick-up routes.
Real-life example for waste management
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 for water quality monitoring
Phoenix’s Water Services Department uses Kando’s AI-powered sewer sensors to monitor wastewater quality in real time. The system flags sudden changes in pH and temperature caused by illegal dumping, so the team can find the source and isolate pollutants before they reach the treatment plant.
- 16 sensors installed across the sewer network in the initial pilot
- Automated alerts and a dashboard of dumping hotspots
- Supports the city’s water reuse goals.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 for wildfire risk detection
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. The firm reported a nearly 50% drop in ignitions with vegetation as a suspected cause in 2025 versus the prior year.15
Industry-agnostic AI utility use cases
12. Automated asset maintenance
Utilities inspect long networks of lines, poles and pipes, and missed defects lead to outages and fires. AI analyzes aerial imagery, LiDAR, drone and satellite data to identify equipment issues or vegetation risks that could damage infrastructure.
Real-life example for automated asset maintenance
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. AI-assisted contact centers & customer experience automation
Legacy billing and customer service platforms force utility reps to switch between disconnected systems, causing high billing error rates and delayed resolutions. With customer data in one CRM, AI agents can answer routine questions on web, mobile and email, and human reps get automatic call summaries.
Real-life example for full platform & billing automation
Regional Water Authority (RWA) replaced legacy systems with Microsoft Dynamics 365 and Copilot Studio for agent assistance, call transcription, and self-service billing.
- 14,000 billing errors eliminated
- 10 hours saved weekly per customer service employee
- Disconnection identification time cut from 30 hours to 7 hours per week.17
Real-life example for autonomous mobile AI agents
ENGIE Belgium deployed Salesforce Agentforce in its mobile app to explain tariffs, analyze usage, and automate smart EV charging via APIs.
- 71% autonomous case resolution rate over 1,500 daily conversations
- 83% response accuracy across customer inquiries.18
Real-life example for generative AI email responses
Octopus Energy uses generative AI to draft email replies to customers. These replies scored 80% customer satisfaction, beating staff-written emails by 15%.19
14. Fleet optimization for utility trucks
AI plans truck routes and monitors driver behavior across large utility fleets. During outages and storms, it reroutes crews to cut travel time and restore service faster. Dashcams with computer vision also flag distracted or risky driving, which matters for fleets that work long shifts in bad weather.
Real-life example for fleet optimization
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 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
- In-cab alerts when a driver is distracted
- Supervisors use camera footage for targeted coaching.20
15. Substation safety and security
Video analytics at substations detect intruders and check that workers follow safety rules.
Real-life example for substation safety and security
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. Conventional perimeter alarms make this worse by flooding operators with 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
- Enable remote, real-time monitoring and faster law-enforcement coordination during an incident.21
16. AI internal workplace & knowledge assistants
Utility staff search long manuals, safety procedures and HR policies every day. Internal AI assistants answer these questions in a chat window.
Real-life example for virtual assistants
Ontario Power Generation (OPG) collaborated with Microsoft to launch ChatOPG, an AI-powered internal virtual assistant designed to give employees rapid access to operational information and administrative support. 22
AI utilities for telecom
17. Zero-Touch Network Operations
In zero-touch operations, AI monitors, repairs and tunes the network without people stepping in.
Real-life examples for network operations
Danish network operator TDC NET uses Ericsson’s Predictive Cell Energy Management, an AI system that predicts traffic and automatically adjusts radio network energy use without human input. By 2025, the company became the first deployment worldwide verified at Level 4 network autonomy, the second-highest level in the TM Forum’s scale for self-running networks, achieving 5% less energy needed per GB of data transmitted.23
18. Network Optimization and Management
Self-organizing network (SON) software adjusts radio settings automatically as traffic changes.
Real-life example for network optimization
Saudi operator stc deployed Nokia’s AI-powered MantaRay Cognitive SON in its commercial network to optimize the radio network automatically during the Hajj pilgrimage, one of the world’s most extreme traffic peaks.
- Over 10,000 optimization actions carried out without human intervention
- Stable connectivity maintained despite a 40% increase in traffic.24
19. 5G Network Slicing
Network slicing splits one 5G network into virtual networks, each with its own guaranteed speed and latency. AI adjusts each slice as demand changes.
Real-life example for network slicing
Nokia and AWS launched AI-driven 5G network slicing, which du (UAE) and Orange are trying out in their live networks. AI agents track network performance along with outside data such as events, traffic and maps, and adjust each network slice automatically to keep its guaranteed service level.
- Network slices adapt automatically to real-world demand
- Use cases include extra capacity on demand and reserved capacity for large events.25
20. Data Traffic Management
AI assigns bandwidth based on live demand, so speeds hold up during peak hours.
Real-life example for data traffic management
Optus trialed Ericsson’s AI-native link adaptation, which replaces fixed rules with real-time AI decisions on how data is sent over the radio link, to improve 5G download speeds where signal is weak.
- Over 20% higher throughput per cell in medium-to-poor signal conditions
- No extra hardware or spectrum needed.26
AI utilities for CRM
AI agents and copilots built into customer relationship management (CRM) resolve routine requests, summarize interactions for human agents, and coordinate field work.
21. AI-driven field force optimization & resident visit coordination
When scheduling and customer messages run on separate systems, technicians drive inefficient routes and arrive at homes where no one is in. Field service platforms plan dispatch and send residents reminders so visits go ahead.
Real-life example for internal dispatch & incident optimization
Ukrtelecom consolidated contact center, incident management, and field force management onto Creatio’s platform to automate routing and task prioritization.
- 39% higher operational productivity
- 30% increase in field force productivity
- 25% reduction in error incidents
- 20% decrease in service time costs.27
Real-life example for resident engagement & appointment coordination
Sureserve deployed Creatio Service with WhatsApp-first resident messaging to send automated visit reminders, confirmations, and self-service rescheduling options.
- Mobilizes 4,000 engineers daily across 1.2 million properties
- Significantly improved first-time fix rates and workforce utilization
- Enabled meeting-time data updates that previously took weeks of development..28
22. AI-guided customer onboarding and contract activation
Complex contract terms, start date confusion, and meter activation timelines create high inbound call volumes and failed customer onboardings. AI agents in the CRM use contract data and the knowledge base to answer onboarding questions right after signing.
Real-life example for AI-guided customer onboarding
ENGIE Italy embedded Salesforce Agentforce into welcome emails, offering new customers a direct self-service portal link to ask specific onboarding and contract questions.
Results:
- 3x increase in click-through rates (rising to 4.5%) on emails
- Fewer failed on-boardings.18
23. Unified CRM for clean-energy sales and field installation
Expanding into solar, heat pumps, and EV chargers requires utilities to unify customer data across sales, service, and field crews. A central CRM links sales pipelines with field scheduling, while AI work-order summaries eliminate duplicate back-office tasks.
Real-life example for Unified CRM
To scale its solar and EV installation services, E.ON UK needed to connect sales pipelines with field service scheduling and eliminate back-office inefficiencies. The utility deployed Microsoft Dynamics 365 (Sales and Field Service) along with AI-driven Copilot work-order summaries to streamline technician workflows.
- Improved first-time install success rates across field operations
- 20% reduction in field installation job duration
- Eliminated a 20-minute manual property handoff process per site.29
AI utilities for back-office operations
24. Meter-to-cash and billing automation
Meter-to-cash covers every step from meter reading to collected payment: validating meter data, calculating bills, sending invoices and processing payments. Utilities run these steps as large nightly batch jobs, so one failed job can delay thousands of bills.
Workload automation tools run these jobs end-to-end and flag delays before customers notice. They also deliver clean billing data on time to the AI models that depend on it.
Real-life example for SAP meter-to-cash automation
Stadtwerke München (SWM) supplies Munich with electricity, gas, district heating and water. Its staff started 60–70 SAP billing processes by hand every day. SWM moved to Redwood’s RunMyJobs to automate these runs.
Results:
- Meter-to-cash processing time cut from 14 hours to 8 hours
- 100% accurate daily invoicing,
- 60–70 manually started billing processes per day replaced with automated runs.30
25. Low-code process automation
Utilities rely on many small manual processes, such as safety forms, equipment tracking and approvals, that often live in spreadsheets and email. Low-code platforms allow employees to build apps and workflows without waiting for IT.
Real-life example for enterprise-wide low-code adoption
Pacific Gas & Electric (PG&E) worked with Cognizant to roll out Microsoft Power Platform across the company. Employees built apps to track equipment at emergency sites, an AI chatbot for service-desk questions, and bots that reconcile billing data.
Results:
- 300+ solutions generating about $75 million in annual savings,
- 132 solutions delivered in under a year across nine business units.31
Solutions and tools under AI utilities
Below are the tool categories behind the use cases above, with the example vendors:
CRM automation
AI-powered CRMs connect the contact center, meter-to-cash and field operations in one customer record.
Check out CRM pricing comparison for more detail.
Creatio
Creatio is a no-code platform with CRM apps (Sales, Marketing, Service) built on top. For utilities, the main draw is that contact center, incident and field workflows can be changed by business teams rather than waiting on development cycles.
- AI agents included, not metered: Creatio AI Studio, a platform for managing the full agent lifecycle. Agents can be combined with human approval steps, for example an agent proposes a visit reschedule and a dispatcher confirms it.
- No per-seat ceiling for large workforces: The Unlimited plan includes unlimited users, custom agents, applications, workflows, custom objects and API calls.
- One platform for service and field work: Contact center, incident management and field force management can run together, as at Ukrtelecom.
Salesforce Agentforce Sales (formerly Sales Cloud)
Salesforce Sales Cloud was officially rebranded to “Agentforce Sales”, with autonomous AI agents built into the core.Utilities usually buy it as part of Agentforce Energy & Utilities, which adds a utility data model on top of Sales and Service. Some of Agentforce Sales features are:
- Prebuilt utility actions: Prebuilt sub-agents can fetch bills and bill details, create and update billing accounts in the external billing system, start and stop service, and get or submit meter readings. Much of the integration work other CRMs require is already scoped.
- Ready-made energy use cases: Agentforce resolves billing queries, outage updates and service requests, recommends tariffs and products based on billing history and case context, and compares plans and incentives for clean-energy decisions.
- Utility data model: Agentforce agents reference industry objects such as accounts, service points, billing accounts and energy service agreements.
Dynamics 365 Sales
Dynamics 365 Sales is a model-driven sales application built on Microsoft Dataverse. For utilities, Dataverse is the key point where sales, contact center and field crews work from one customer record. Some of its features are:
- Sales agents for commercial and clean-energy pipelines: Premium includes access to the Sales Qualification Agent, Sales Opportunity Agent, Sales Research Agent and Sales Close Agent (in preview). These suit B2B energy contracts and solar, heat-pump or EV-charger sales.
- Copilot included in every tier: Included capabilities cover contextual insights and recommendations, lead and opportunity summaries, meeting preparation summaries, email summaries and reply drafts, and natural language chat.
- Utility billing on the same platform: Itineris’ UMAX solutions are developed on and natively integrated with the Dynamics 365 platform, so meter-to-cash can live next to CRM. Regional Water Authority uses this setup (see use case 13).
WLA tools
Workload automation and enterprise job scheduling software can run billing and data jobs on time with fewer manual errors.
IoT
IoT devices, sensors and smart meters report how the grid and plants are running, so crews can fix equipment based on its condition rather than on a fixed schedule.
ThingWorx
ThingWorx is an industrial IoT platform for building monitoring dashboards, alerts and apps on top of equipment data. The platform is acquired by Velotic.
- Reads utility equipment directly: Kepware supports a library of more than 150 drivers and advanced plug-ins. These include DNP, IEC 61850 MMS and IEC 60870-5-101/104 drivers, the protocols used in substations and remote terminal units.
- Anomaly detection per sensor: Anomaly detection is enabled by default to create an alert for a specific property to start detection, such as on a pump’s vibration or a heat exchanger’s temperature.
- Agent-ready operational data: ThingWorx AI Assistant, AI runtime and agent services, and a generally available MCP Server that exposes ThingWorx services as AI-accessible tools.
IoT for utilities case study
Vattenfall Heat manages about 1,800 heat assets in the Netherlands, such as heat plants, production units and cold plants, serving over 120,000 households. It used ThingWorx and Kepware to monitor these sites from one place, starting with a pilot in Amsterdam.
- Monitoring: Dashboards show process data and manage alarms from generators, remote measurement points and transfer stations.
- Planning: The data helps the team forecast heat demand, plan network expansion and reduce installation downtime.32
AWS IoT Core
AWS IoT Core is a managed service that connects devices to AWS without running servers. For utilities it is mainly the ingestion layer for smart meters and sensors. Analytics and ML run in other AWS services.
- Managed LoRaWAN network server: AWS IoT Core for LoRaWAN is a fully managed LoRaWAN Network Server for low-power, long-range devices. Utilities can set up public or private LoRaWAN networks without building or running their own network server.
- Rules Engine for multi-vendor meters: Rules filter, transform and decode device data with a SQL-like language. Messages can be routed to more than 20 AWS and third-party services. Each meter vendor’s payload can be decoded by its own rule.
AWS IoT Core for utilities case study
Madrileña Red de Gas is Spain’s third-largest gas distributor by supply points. It needed to integrate 900,000 smart meters from different vendors into one platform to meet 2030 telemetry regulations. It used AWS IoT Core’s native integration with public LoRaWAN networks in collaboration with operator Everynet, with an IoT Core rule for each meter manufacturer. The results include:
- 25% lower operational costs through real-time monitoring and data integration
- 30% lower infrastructure costs, since LoRaWAN avoided deploying proprietary gateways
- Over 900,000 smart meters managed without performance compromise
- Meter readings, customer information and weather data combined in one reporting system.33
Azure IoT Hub
Azure IoT Hub is Microsoft’s managed cloud gateway for connecting, authenticating and managing device fleets. It is AWS IoT Core’s direct counterpart. It fits fleets of distributed, stand-alone devices with fixed functions, such as water monitoring stations and distributed fluid level sensors. Some of its utility relevant capabilities are:
- Two-way control of meters and sensors: IoT Hub keeps a device twin for every connected device and can invoke direct methods on devices from the cloud. Utilities can change meter settings, trigger an on-demand reading or check a sensor’s reported state without a site visit.
- Zero-touch onboarding and long-lived security: The Device Provisioning Service enrolls devices using X.509 certificates or a Trusted Platform Module. IoT Hub can now renew device certificates directly, so connected devices stay authenticated over time without downtime.
- Route data straight into analytics and AI: Telemetry from IoT Hub can be routed into analytics platforms like Microsoft Fabric for visualization or AI modeling.
OT & grid cybersecurity
hese tools discover grid and plant assets, detect threats in OT networks, and control remote access to substations, pumping stations and power plants.
Claroty
Claroty is a cyber-physical systems (CPS) protection platform. It covers exposure management, network protection, secure access and threat detection, and can run in the cloud (Claroty xDome) or on-premises (Claroty CTD).
- Secure vendor and staff access to remote sites: Secure Remote Access protects industrial networks from risks introduced by remote users, including third-party vendors, contractors and technicians.
- AI-driven risk prioritization: xDome uses AI to highlight specific attack vectors and rates them by likelihood of exploitation, business impact and existing compensating controls.
- AI agent for OT security work: Claroty launched Claire, an AI security agent built for cyber-physical systems. It also added AI-generated dashboards and reports, a CPS Library and a Model Context Protocol server for xDome.
Armis
Armis Centrix is a cyber exposure management platform. The platform is acquired by ServiceNow which links Armis’ asset data to ServiceNow’s ticketing and remediation workflows.
- One view across IT and OT: It discovers and monitors connected assets across IT, OT, IoT devices, medical equipment, code and cloud environments.
- Full OT exposure lifecycle: Armis Centrix for OT/IoT Security uses the AI-driven Armis Asset Intelligence Engine and covers the entire exposure management lifecycle in OT, ICS and building management environments.
Dragos
The Dragos Platform is built only for OT environments. It combines OT asset visibility, threat detection, vulnerability management and investigation and response.
- OT-specific threat intelligence: WorldView threat intelligence is built exclusively for OT. Dragos tracks 26+ OT-specific threat groups, so detections are tied to known attackers that target utilities.
- Collective defense across utilities: Neighborhood Keeper is an opt-in network where Dragos Platform users share intelligence anonymously. Customers can receive context on new vulnerabilities or threat activity, often before it is disclosed publicly.
- Expert threat hunting: OT Watch adds expert-led threat hunting. OT Watch Complete extends this to 24/7 coverage, which helps utilities without a dedicated OT security team.
- AI built on OT expertise: EmberAI is built on the Dragos Intelligence Fabric and draws on a decade of OT cybersecurity expertise.
- Regulatory fit: Dragos also positions its network monitoring to support compliance with NERC CIP-015, the new internal network security monitoring requirement.
ML algorithms & AI
ML platforms build, deploy and monitor models for computer vision (asset inspection), predictive analytics (failures, demand), NLP (customer inquiries), generative AI, and agentic AI.
Data infrastructure and cloud platforms
Data platforms unify meter, Smart grid, customer and weather data so AI models train on one consistent source. Streaming platforms add live data for outage detection and for RAG-based assistants. Since models are only as good as their inputs, data quality checks belong in this layer.
Data infrastructure for utilities case study
AusNet Services runs electricity and gas networks for 1.5 million customers in Victoria, Australia. Its data sat in separate warehouses, so it couldn’t assess asset condition before failures happened. AusNet moved its data to Databricks on Azure and used it for machine learning. The results:
- Data platform costs fell by 50%.
- Data processing became 3x faster.
- Predictive maintenance on three asset classes reduced risk and cost, and AusNet is now expanding it to all its assets.34
Decentralised energy management
Smart grid solutions such as ADMS, DERMS and EMS balance energy flow, integrate rooftop solar, batteries and EVs, and optimize production and storage. Digital twins simulate grid and asset performance under different scenarios before changes go live.
Add as preferred sourceWhy should we use AI in utilities?
Electricity demand surge
Demand is rising faster than utilities can build new capacity. Better forecasting and scheduling help them serve it with the assets they already have.
- Electricity demand is projected to increase 1.4% annually through 2032, resulting in a 46% cumulative rise.35
- 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.36
- In the US, residential electricity prices rose about 13% from 2022 to 2025.37
- AI-driven scheduling can raise field productivity by 25–30%.35
Investment opportunities in utilities
Utilities are about to spend heavily on grid upgrades, and AI helps decide where that money goes.
- U.S. electric companies are expected to invest $1.1 trillion between 2025 and 2029 to upgrade aging infrastructure and expand grid capacity.38
- ML-based asset health analysis allows utilities to reallocate up to 80% of capital. 35
Asset health data also shows where not to invest, which helps avoid overbuilding as construction costs rise.
Data center demand growth
Data centers are adding large new loads to the grid:
- Data center electricity demand could double by 2030, with a 131% increase expected by 2032 in a high-growth scenario.35
- Single AI projects now need as much power as a large city. For example, OpenAI and Nvidia’s recent 10-gigawatt data center partnership requires the electricity equivalent of New York City during peak summer use.36
- Renewable projects now make up over 90% of all new capacity waiting for grid connections.36
- AI has improved the heat rate or yield of fossil and renewable generation assets by 2–5%.35
AI utilities challenges
- Data privacy: Smart meter data shows when people are home and which appliances they use, so training models on it raises privacy and consent issues.
- AI bias: Models that flag fraud or prioritize disconnections can treat some customer groups unfairly, so these decisions need human review.
- Legacy system integration: Grids tend to run 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 only 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 can be targets of data poisoning or model manipulation.
Discover other AI risks and challenges.
AI utilities case studies
Further reading
Explore more on AI in other industries:
Cite this research
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@misc{dilmegani2026,
author = {Dilmegani, Cem and Şimşek, Hazal},
title = {{AI Utilities: Top 25 Use Cases & 48 Case Studies}},
year = {2026},
month = oct,
howpublished = {\url{https://aimultiple.com/ai-utilities}},
note = {AIMultiple. Retrieved October 5, 2026}
}Results and timestamps of 74 data points. Download the summary data shown in this article's charts and tables as a ZIP file containing 3 CSV files.
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Changelog
5 updatesAdded two use cases: AI-managed EV charging and wildfire risk detection.
Removed Figure 1, the Google keyword search chart for AI utilities.
Added a real-life example to the section on smart homes as energy hubs.
Reference Links
Cem's work at AIMultiple has been cited by leading global publications including Business Insider, Forbes, Morning Brew, and Washington Post, global firms like Deloitte and HPE, NGOs like World Economic Forum, and supranational organizations like European Commission. [1], [2], [3], [4], [5]
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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