Mentioned in 98 AI use cases across 15 industries
AI watches heat and power-use data from electrical systems to catch dangerous overloads before they cause outages or fires.
Instead of sending sensitive transformer data everywhere, the analytics can run on a separate local network so operators get AI-driven insights with lower cyber risk.
A digital copy of the aircraft’s battery and power system uses physics plus AI to track wear and predict what will happen next.
If operators change ingredients, quantities, or equipment during production, the system flags it as a serious issue, records it, and requires quality approval before the batch can move forward.
The utility used AI to watch hundreds of thousands of smart meters, spot missing or suspicious readings, and send teams to fix leaks, backflows, and unmapped consuming meters before revenue is lost.
Albemarle created many reusable equipment templates and dashboards so engineers spend less time digging through data and more time improving the plant.
The manufacturer used AI to watch machines, inspect parts, predict failures, and simulate factory changes so it could make more good parts with less downtime.
The platform looks through huge amounts of smart meter data to spot leaks, backflows, or suspicious patterns so utilities can stop losing money and respond faster.
Instead of forcing engineers to guess alarm limits, the meter can learn good threshold settings for events like voltage dips and spikes.
A turbine’s built-in controller watches generator signals to spot early signs of faults, so operators can fix problems before the machine fails underwater.
Instead of employees manually deciding what to do with every suspicious meter alert, the system automatically opens, filters, routes, and closes investigation cases based on predefined rules and statuses.
Let factory systems continuously share what is happening on the floor so planners can keep improving production and maintenance decisions.
A central platform collects data from pumps, motors, drives, and sensors, then turns it into recommendations and automation that help the whole station run better.
The company connects many scattered systems into one AI-ready view so the AI can understand what is happening with equipment and help people act faster.
After reducing false alarms, Siemens wants to use AI to fine-tune inspection settings and study how good boards move through the line so problems can be prevented earlier.
Sensors listened to a critical air-handling machine, AI noticed unusual vibration, and experts helped the plant fix the exact problem before the machine failed and triggered a very expensive shutdown.
The utility replaced paper and many disconnected tools with one outage system plus mobile apps so office staff and field crews can see the same outage information and coordinate faster.
Instead of disconnecting during short grid disturbances, smart inverters can stay online and help the grid recover by responding to frequency events.
When a market trade happens, the system instantly recalculates the plant’s schedule and sends the new instructions to the plant control system so the plant can actually deliver what was traded.
Before AI can help the grid, utilities need all their scattered data in one understandable place. This workflow gathers and organizes that data so AI apps can learn from it and operators can see the full picture.
Software watches how a waste-to-energy furnace is burning and continuously adjusts controls so trash burns more efficiently and cleanly.
AI studies how each customer actually runs a gas turbine and estimates whether certain parts can safely last longer before replacement.
A camera and AI watch every pipe on the line, spot bad wrapping instantly, and tell the factory to reject or rework it before scrap piles up.
Instead of people spending a long time building factory schedules by hand, AI can create a workable plan in a couple of minutes.
Equipment in a data center can tell operators when it is unhealthy before it fails, so teams can fix problems early instead of waiting for outages.
AI could watch emissions data like a digital environmental inspector, spotting unusual pollution patterns early and helping the factory stay within rules during commissioning and normal production.
It watches how pumps are running, spots waste or early problems, and tells operators how to run them better.
AI helps the mills react better to changing material properties so they grind cement more efficiently and waste less energy.
Match product defects with the machine settings and conditions present when they happened so teams can catch quality problems during production instead of after the fact.
One meter sends trusted energy and power-quality data into utility control systems using standard substation languages, while locking down who can change settings.
Build a live digital copy of factory operations so teams can test ideas and understand performance without guessing.
An AI controller learns how to adjust heating and cooling settings in a building so it uses less energy while still keeping occupants comfortable.
The utility built a live digital copy of its equipment so it can spot which assets are likely to fail and fix them before they cause outages.
Instead of compressors acting like isolated machines, they share data with the rest of the factory so operations and energy use can be coordinated better.
An AI system watches how a factory or commercial building uses electricity, predicts what energy it will need next, spots waste, and suggests or makes adjustments so the site uses less energy without hurting operations.
AI acts like a smart conductor for buildings and electric vehicle fleets, deciding when to charge, store, or use energy so sites save money, stay comfortable or operational, and help the grid at the same time.
Software watches thousands of heating-network valves, spots when one is behaving strangely or wearing out, and tells engineers which ones to fix first.
The system helps desalination plants do more of their power-hungry work when electricity is cheaper, lowering the cost of making fresh water.
Ameren Illinois uses an APM system to combine data about substations and transformers so it can spot which equipment is most likely to fail and fix the riskiest ones first.
The plant uses AI and digital monitoring tools to spot equipment and structural problems early, so teams can fix issues before machines fail or production slows down.
Ameren and SAS used smart meter data to infer when neighborhood transformers are overloaded, failing, or causing outages, so crews can fix problems earlier without installing expensive sensors on every small transformer.
Sensors watch the grid all the time, and AI spots signs that equipment may fail soon so crews or automation can act before the lights go out.
Use machine learning to watch how power-industry machines behave and warn teams before a breakdown happens.
The system sends plant-floor data to business dashboards so executives and analysts can see what is happening almost immediately and use AI to spot patterns and improve decisions.
Show operators the right how-to guide, pictures, text, or 3D model at the exact production step so they know how to build and inspect a product correctly.
The system watches harmonic distortion levels and warns when they are likely to cross accepted limits, helping the facility stay within power-quality rules.
Amazon uses AI to watch machine behavior, spot signs of trouble early, and help teams fix equipment before it breaks.
A digital copy of the vehicle is kept from design through real-world operation, so teams can learn from how cars behave in the field and keep improving future versions.
The system helps the grinding line run at better settings so mills produce more cement, use less power, and avoid stoppages caused by unstable operation.
Set exact timing rules between furnace steps so materials move at the right moments, helping temperatures stay more even and production finish sooner.
LeddarTech replaced scattered Word and Excel files with one system that tracks product requirements, tests, and changes for LiDAR used in autonomous vehicles, making safety audits and teamwork much easier.
An AI system watches factory equipment data to spot signs of wear or failure early, like noticing a machine is starting to get sick before it breaks.
Track one combined score for how well equipment is running, then use AI to find the biggest reasons it is underperforming and what to fix.
The software decides when each production order should run and on which resources so equipment is used efficiently and customer orders are more likely to ship on time.
AI watches how turbines, panels, and related equipment behave so operators can spot problems early and run assets more efficiently.
The company tracks how much solar power it makes and uses, and how much water gets recycled, so it can waste less and hit sustainability goals.
AI automatically records inspection results for audits and uses final inspection feedback to get smarter over time.
Use a cloud system to track, review, and approve product design changes so teams do not lose information or make inconsistent updates.
Companies use structured approval processes so imaging AI can be legally and safely used in hospitals.
The software helps utilities prepare and track the exact switching steps needed to safely disconnect equipment, create safety zones, authorize work, and keep a full audit trail.
Sensors watch water pressure in the network and a controller tells pumps to slow down or speed up so customers still get water without over-pressurizing the pipes.
An AI copilot helps power grid operators read procedures, think through what to do next, test options in a grid simulator, and suggest the safest or best action.
The plant uses sensors to keep track of water levels and quality in loops that feed hydrogen production. AI looks for warning signs that equipment or water treatment performance is drifting, so maintenance can happen before something breaks or production suffers.
A digital twin brings together data from renewables, electrolyzers, and storage so operators can continuously tune the whole hydrogen system to cut waste and cost.
Plug Power is installing a large machine system at Galp’s refinery that uses electricity to make clean hydrogen on-site, so the refinery can use less fossil-fuel-based hydrogen in its daily operations.
A dashboard looks at meter history and weather-related conditions to find transformers that were overloaded before and warns which ones may overload again, so utilities can upgrade them before customers lose power.
Using smart computer programs to watch and check airplane parts for damage or wear so they can be fixed before problems happen.
It acts like a shared brain for utility data, connecting analytics with billing, customer, meter, asset, and other Oracle utility systems.
AI systems predict when airplane parts might fail so they can be fixed before breaking.