AI in utilities: Key use cases transforming the energy and utilities sector

At 2:14 a.m., a transformer begins running hotter than expected. Nothing has failed yet. No customer has reported an outage, and no crew has received an alert.

A conventional monitoring system records the temperature. An AI system can compare it with load, weather, maintenance history, vibration readings, and similar assets. It estimates the risk, checks available operational information, and recommends an inspection before the morning demand peak.

This is where AI in utilities starts to matter. The technology becomes useful when it moves past a dashboard and supports a real operational decision.

The pressure to make those decisions faster keeps rising. According to the International Energy Agency’s Electricity 2025 report, global electricity demand is forecast to grow at close to 4% annually through 2027, driven by electrification, industrial activity, air conditioning, and data centers. Renewables and nuclear are expected to cover the additional global demand over this period.

For utility leaders, the question is no longer whether artificial intelligence belongs in the sector. The question is where to place it, which decisions require human approval, and how to connect AI with the physical systems customers depend on.

AI in utilities: Key takeaways

The use of AI covers far more than chatbots and automated reports. Its strongest role sits close to costly, repeated, and time-sensitive decisions.

• AI can compare meter, weather, asset, market, and network records to forecast demand and detect abnormal conditions.
• Utility companies can use predictive maintenance to identify equipment risk before a pump, turbine, transformer, or line component fails.
• AI agents can gather evidence and prepare actions, but people should retain authority over safety, pricing, switching, and other high-impact decisions.
• Generative AI can support work with manuals, incident reports, regulatory documents, and field-service knowledge.
• AI implementation depends on dependable data, system access, security controls, and clear ownership.
• The benefits of AI should appear in reliability, restoration time, asset life, forecast error, customer service, or cost, not simply in the number of models launched.

These principles provide a practical basis for deciding where AI fits into utility operations.

Why AI in the utility industry is moving closer to core operations

The utility industry already produces much of the information AI needs. Smart meter records, supervisory control systems, weather feeds, inspection images, work orders, market prices, and customer data provide signals about how assets and demand behave.

The hard part is connecting this information at the speed required for utility operations.

Traditional analytics often explains what happened. Machine learning can estimate what may happen next. Generative AI can interpret documents and prepare explanations. An AI agent can complete several approved steps, such as retrieving maintenance records, checking spare-part availability, and preparing a work order.

A 2025 review of AI and smart-grid technologies highlights applications across renewable integration, grid performance, reliability, energy management, cybersecurity, and demand response. The research also points to privacy, security, infrastructure, and implementation challenges that utilities need to address as adoption grows.

This distinction matters because AI in the utility industry spans several levels of authority:

Level	Typical AI use	Human role
Observe	Detect unusual voltage, temperature, or consumption	Review the alert
Predict	Estimate demand, equipment failure, or renewable output	Check assumptions
Recommend	Suggest a switching plan, dispatch, or load adjustment	Approve or reject
Act	Complete a low-risk task within defined limits	Monitor and audit
Coordinate	Connect several tools and workflows	Set authority and escalation rules

AI can help utilities process more signals, but operational authority must remain explicit. Useful intelligence should support accountable decisions, not create uncontrolled automation.

Benefits of AI for energy and utilities

The benefits of AI depend on the decision being supported. A highly accurate model brings little business value if its prediction reaches the operator too late or cannot connect with the existing workflow.

Well-designed AI solutions can support four broad goals.

Earlier detection and stronger reliability

A utility can monitor thousands of assets, but engineers cannot manually inspect every measurement. AI-powered anomaly detection can flag changes in vibration, heat, pressure, oil quality, voltage, or other operating characteristics.

Earlier warnings can help utilities schedule inspections before a fault develops into a service interruption. Predictive systems can also rank alerts by urgency instead of sending every anomaly to the control room with equal priority.

The result is not perfect prediction. It is more time to respond before a fault becomes an outage.

Better forecasts and energy efficiency

AI models can compare historical demand with weather, tariffs, occupancy, local events, electric vehicles, and industrial activity. A better forecast helps coordinate power generation, storage, procurement, and energy distribution.

The same methods can analyze consumption patterns at building, feeder, or regional level. This can support demand-response programs and help customers understand unusual energy usage.

PwC’s 2026 discussion of technology and AI across evolving energy systems points to forecasting, maintenance, operations, predictive analytics, and smarter grid management as areas where digital capabilities can improve reliability and system performance.

Forecasting creates the clearest benefit when it changes scheduling, purchasing, capacity planning, or load management.

Faster field and customer decisions

Field teams often search across manuals, inspection histories, asset records, and safety procedures. Generative AI can retrieve relevant passages, summarize previous work, and prepare a first response.

Customer teams can apply similar techniques to billing explanations, connection requests, high-usage alerts, outage messages, and smart meter questions.

The goal should be faster access to approved information, not replacement of professional judgment.

Support for the energy transition

Solar, wind, battery storage, heat pumps, and electric vehicles create a more distributed energy system. Production and demand can change quickly.

AI in energy can forecast renewable output, coordinate battery charging, estimate local congestion, and manage a distributed energy resource within defined operating limits. AI could also support virtual power plants by coordinating many smaller assets as one flexible resource.

These functions can help integrate renewable energy sources while giving operators more information about expected variability.

AI use cases transforming utility operations

The strongest AI use cases begin with one recurring decision, one accountable team, and one measurable outcome.

Grid forecasting and power flow management

Forecasting sits at the center of the energy sector because supply and demand must remain balanced.

Utilities can combine historical load with weather, calendar events, industrial activity, distributed energy, and market information to predict what the grid may need.

AI models can produce forecasts for:

• Total system demand
• Substation and feeder load
• Solar and wind generation
• Electric vehicle charging
• Battery availability
• Market purchases
• Congestion risk

For example, a distribution operator may expect a hot evening, high air-conditioning use, and weaker-than-planned wind output. The model can identify a local capacity risk and compare demand response, battery discharge, and switching options.

This use case can optimize energy planning, but dispatchers still need confidence ranges and source information. A single forecast number without context can create false certainty.

Pro tip: Track forecast bias alongside average error. A model that repeatedly underestimates demand peaks may create more operational risk than one with slightly higher average error.

Forecasting earns trust when planners can understand what changed and why.

Predictive maintenance and asset management

Maintenance schedules commonly depend on age, operating hours, or fixed inspection intervals. AI-driven asset management can add actual equipment condition to the decision.

Relevant inputs may include:

• Thermal and visual inspection images
• Acoustic and vibration readings
• Transformer oil data
• Pump pressure and flow
• Weather exposure
• Failure history
• Work orders
• Technician notes

A model can rank assets by failure risk and recommend an inspection sequence. Digital twins can add another layer by testing how equipment may behave under different load, weather, or operating scenarios.

The IEA’s Energy and AI analysis identifies forecasting, fault detection, maintenance, electricity-network management, and renewable integration among practical AI applications already emerging across the energy sector. The IEA estimates that AI-based fault detection could reduce grid outage durations by 30% to 50% in applicable settings.

The output should support engineers, not hide the evidence behind a risk score. Maintenance teams need access to the signals, history, and confidence behind each recommendation.

Outage prediction and restoration

An outage may start with weather, vegetation, equipment damage, construction activity, or cascading grid faults. AI can combine forecasts, sensor alerts, incident histories, and customer reports to estimate where damage is most likely.

During an event, a system can:

1. Group related customer reports.
2. Estimate the probable fault location.
3. Identify critical loads.
4. Compare available crews and equipment.
5. Prepare restoration estimates.
6. Update recommendations as field information arrives.

Computer vision can also examine imagery collected by unmanned aerial vehicles after storms, fires, or other incidents. Models can flag poles, insulators, conductors, or other assets that may require inspection.

This form of automation can reduce investigation time, but operational switching and field safety require governed delivery. The person approving an action should know which information and rules produced the recommendation.

Renewable energy and distributed resource coordination

Renewable energy creates cleaner power generation, but solar and wind production varies with weather. Batteries, smart buildings, electric vehicles, and other distributed assets add flexibility while increasing the number of resources operators need to coordinate.

AI can forecast solar production, wind output, local demand, battery state, and charging requirements. Mathematical optimization can then compare charging, discharging, curtailment, and purchasing options.

This can support:

• Distributed energy management
• Virtual power plant dispatch
• Battery energy storage scheduling
• Local congestion control
• Demand response
• Energy market participation

The IEA expects electricity generation needed to supply data centers to increase from around 460 TWh in 2024 to more than 1,000 TWh in 2030 in its base scenario. Renewables are expected to provide nearly half of the additional electricity required through 2030.

AI can help coordinate a more varied energy supply. It cannot replace transmission lines, substations, storage, generation assets, or other physical infrastructure.

Customer service and smart meter analysis

Smart meter data can show how energy consumption changes over time and location. With suitable privacy controls, AI systems can identify unusual bills, abnormal usage, or households that may benefit from an efficiency program.

Possible AI applications include:

• High-bill explanations
• Payment and tariff guidance
• Usage anomaly alerts
• Demand-response recommendations
• Service connection support
• Customer service summaries
• Energy conservation suggestions

A customer might receive an alert explaining that overnight energy usage has increased and suggesting a check of heating or charging equipment.

The model should explain why it reached the conclusion and avoid presenting assumptions as facts.

Smart meter intelligence can make communication more relevant, but trust depends on accuracy, privacy controls, and clear limits.

Generative AI and AI agents for utility work

Using generative AI can reduce the time employees spend finding documents or preparing routine material. It can summarize incidents, compare inspection records, draft regulatory material, or answer questions from approved technical manuals.

AI agents take the process further. An agent may retrieve asset information, check maintenance rules, find an available technician, and prepare a work order. The agent should stop before completing an action beyond its approved authority.

A 2026 academic study of generative AI adoption inside an energy company used 16 interviews across nine departments. Researchers identified 41 potential AI-related use cases, including reporting, forecasting, maintenance, data handling, and anomaly detection. Employees generally favored incremental adoption connected with existing work processes.

AI earns its place in utility operations when it helps teams act earlier, explain each recommendation, and remain accountable for the final decision. The goal is not automation for its own sake. It is stronger reliability, faster response, and better service for customers.

Hana Chundelova Sulcova, VP of Technology at Avenga

The value of agentic work comes from bounded authority, traceable actions, and clear human control.

Put AI at the core of utility forecasting, asset intelligence, and governed operational workflows.

Learn more

Where AI in the utility sector needs stronger controls

Utilities operate critical infrastructure. A weak recommendation can affect safety, equipment, customer bills, reliability, and public trust.

The main risks include:

• Incomplete data: Missing sensor readings or inconsistent asset identifiers can distort predictions.
• Model drift: Demand, equipment condition, weather, and customer behaviour change over time.
• Algorithmic bias: Customer models may reproduce unfair patterns found in historical information.
• Cybersecurity: Connected models and data pipelines add possible attack paths into sensitive environments.
• False confidence: A convincing generative answer can still contain an incorrect conclusion.
• Unclear ownership: Teams may not know who can reject, revise, or stop an automated process.
• Skills gaps: Utilities need people who understand both engineering systems and AI behaviour.

Responsible AI requires access controls, testing, human approval, monitoring, and incident procedures. Avenga’s cybersecurity services can support security work around connected AI, operational, cloud, and data environments.

The aim is not to remove every risk. It is to make risk visible and establish who owns the response.

Implementing AI in the utility industry

AI adoption works better when utility companies begin with a defined operational problem rather than a broad technology program.

1. Select one decision

Choose a use case with a clear owner, available information, and measurable cost or operational effect.

Suitable starting points may include load forecasting, inspection analysis, outage classification, high-bill support, or maintenance prioritization.

Document how the decision works today, including delays, errors, manual steps, and escalation rules.

2. Check the data foundation

Identify required sources, owners, refresh rates, and access policies. Resolve duplicate asset IDs, missing readings, and timing mismatches before model development begins.

Avenga’s data services support the engineering foundation needed to connect operational, customer, asset, and market information.

Good data does not guarantee a strong AI system, but poor data can undermine one very quickly.

3. Match the method to the task

Use forecasting models for prediction, computer vision for inspection images, mathematical models for constrained scheduling, and generative AI for language-heavy work.

Do not use a large language model for a calculation or rule-based task that a simpler method can handle more predictably.

4. Define authority before automation

List which actions the system may recommend, prepare, and complete.

Add thresholds for:

• Cost
• Safety
• Model confidence
• Customer impact
• Grid impact
• Regulatory implications

High-risk actions should require named human approval. Lower-risk steps can receive more autonomy after repeated testing.

5. Test real failure conditions

A pilot should include missing readings, unusual weather, conflicting records, cyber incidents, and inputs outside expected ranges.

Training, validation, and test data should represent the conditions the production system may face.

Utility teams should also test how quickly they can disable the model and return to the previous process.

6. Connect AI with daily work

The integration of AI should fit tools that operators, field crews, engineers, and service teams already use.

A separate portal often adds another task. A connected workflow can deliver a forecast, inspection recommendation, or alert where the employee already makes the decision.

Avenga’s cloud services can support the infrastructure, integration, deployment, and monitoring layer behind production AI.

The model becomes useful when the right result reaches the right person at the right point in the process.

7. Measure operational results

Track measures tied to the original use case:

• Forecast error
• Avoided outage minutes
• Inspection time
• Asset failure rate
• Restoration time
• Manual review time
• Customer call duration
• Recommendation override rate

Override rate deserves special attention. Frequent rejection may point to weak accuracy, unclear explanations, or rules that do not reflect operational reality.

A successful pilot proves an operational result and defines the conditions for wider deployment.

The future of AI in energy and water utilities

The future of AI will involve closer coordination between forecasting, control systems, field work, markets, and customer programs.

Utilities are likely to see more:

• AI agents working across approved operational systems
• Physics-informed models combining engineering rules with learning
• Synthetic data for rare failure scenarios
• Digital twins for grid and plant planning
• Local intelligence at substations and field devices
• Human review inside control-room workflows
• MLOps practices for monitoring, rollback, and audit

The energy and water context also matters. Water utilities can apply similar approaches to pump maintenance, pressure management, leakage, treatment assets, and consumption forecasting.

The physical processes differ, but the engineering principle remains the same: AI should help people make better decisions inside systems where reliability and accountability matter.

Avenga works with organizations across the energy and utilities sector on data, AI, infrastructure, connected systems, and operational engineering.

The strongest programs will place AI at the core of a defined engineering process while keeping people accountable for the outcome.

FAQ

What is AI in utilities?

AI in utilities refers to the use of prediction, computer vision, language models, analytics, and software agents across electricity, gas, and water operations. Common applications include forecasting, asset monitoring, outage response, customer support, and renewable resource coordination.

What are the main AI use cases for utility companies?

The main AI use cases include demand forecasting, predictive maintenance, fault detection, restoration planning, smart meter analysis, renewable generation forecasting, and document support. Each use case should connect to an operational measure and an accountable team.

Can AI improve grid reliability?

AI can support grid reliability by identifying abnormal asset behaviour, forecasting demand, locating probable faults, and helping operators plan a response. Operators should retain authority over switching, safety, and other high-impact decisions.

How can utilities use generative AI safely?

Utilities can use generative AI more safely by restricting access to approved information, requiring source references, recording outputs, and adding human review. Generative systems should not become the authoritative source for asset, billing, or control information.

What data does an AI utility system need?

An AI utility system may use meter readings, asset information, weather data, maintenance history, work orders, market information, and network events. The required inputs depend on the operational decision the system supports.

How should an AI implementation begin?

An AI implementation should begin with one decision, a measurable baseline, and a named owner. The utility can then test the system in a limited environment before granting access to production workflows.

Conclusion: Put AI at the core of accountable utility operations

AI in utilities works best when it connects better prediction with better engineering decisions.

The technology can help forecast energy demand, detect equipment risk, coordinate renewable generation, support outage response, and give field and customer teams faster access to information. None of these gains requires removing people from the process.

For utility leaders, the practical path starts with one costly or time-sensitive decision. Build the data foundation, define authority, test difficult scenarios, measure the result, and expand only when the operating evidence supports it.

That approach reflects Avenga’s AI Native Engineering logic. AI belongs at the core of how a business works, but engineering for outcomes requires clear accountability from prediction through action.

If you are evaluating where AI can create measurable value across your grid, asset, field, or customer operations, contact Avenga to discuss the engineering path.