Mentioned in 154 AI use cases across 26 industries
Workers or drones take pictures of equipment, AI checks the images for wear or damage, and the system creates alerts or maintenance work automatically.
Use AI to help maintenance teams learn from failures, track the right metrics, and show leaders why new technology is worth funding.
An energy company uses customer data to estimate which households are likely to leave, so it can intervene before they switch providers.
This setup lets an external AI assistant securely plug into Seismic so it can look up content, react to events, and help users complete content-related tasks.
A chat-based AI helps field and maintenance teams ask plain-language questions about equipment, maintenance, and reliability data, then returns answers through a dynamic dashboard.
AI runs smart tests automatically every time developers change code, so teams get quick feedback before releasing updates.
Instead of waiting for something like HVAC or plumbing to break and upset tenants, AI predicts failures early and automatically creates preventive work orders.
Use AI to compare a project’s software documents and test evidence against NASA’s required standards and flag what is missing.
A live dashboard connects what parts and materials are available with what military platforms need, so shortages can be seen and acted on immediately.
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.
Maintenance data is sent into company dashboards so finance, operations, and maintenance can all see the same numbers and make better decisions.
Let factory systems continuously share what is happening on the floor so planners can keep improving production and maintenance decisions.
Use automation to gather all the paperwork and records that prove a supplier followed the rules for safety-critical aircraft parts.
A software system helps utilities keep track of important equipment and infrastructure, like a smart filing cabinet and workflow hub for physical assets.
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.
A chat assistant is added around Maximo so workers can ask plain-language questions about maintenance and asset management and get useful answers.
Put all asset and work information in one place so managers can decide what to fix first, who should do it, and how money should be spent based on facts instead of opinions.
When a utility plans a job, this setup can connect the job to purchasing, inventory, accounting, and the crew that actually does the work.
A utility keeps a digital record of each field asset, tracks what happens to it, and updates its status as work is done so teams know where equipment is and what condition it is in.
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.
Investigators can search all claims-related data in plain language-like ways, including text and location clues, to quickly find suspicious patterns and linked entities.
Utilities connect Oracle systems so customer, billing, and operations data can move between applications without manual re-entry.
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.
A utility software vendor provides a centralized online guide hub so teams can find product documentation, videos, API references, and help for managing utility work and assets.
Use machine learning to watch how power-industry machines behave and warn teams before a breakdown happens.
A map-based mobile app tells tree-trimming crews exactly where to go, what work ticket to complete, and lets managers see progress live instead of relying on paper maps and forms.
Oil lab reports used to sit in email. The system now reads them automatically, links them to the right machine, compares them with temperature and vibration trends, and creates a repair job when the combined evidence shows wear.
Amazon uses AI to watch machine behavior, spot signs of trouble early, and help teams fix equipment before it breaks.
When a utility customer issue needs work on physical equipment, the billing/customer system can pass that service call to an asset management system so field and asset teams can act on it.
Utilities keep asset, maintenance, and inventory information in many systems. This partnership combines asset-management consulting with master-data tools so companies can clean up that information and make better operating decisions.
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.
AI helps government staff read benefit applications faster, pull out important details, and flag what needs attention so people can decide cases more quickly.
An AI assistant helps the new building owner receive the right logins, manuals, training, and system information so they can run the building from day one.
Instead of every mechanic doing paperwork, planning, material gathering, and coordination alone, assign one person to plan and schedule so the others can spend more time actually fixing equipment.
The system keeps a detailed card for each warranted piece of equipment, including where it is, what it is, who covers it, and how to contact them.
The company built one cloud analytics hub so teams can pull together outage and operations data, analyze it faster, and use past events to make better future decisions.
Utilities connect their maps, work orders, and asset databases so everyone sees the same asset details and discrepancies can be found during inspections.
The system finds which customers are most likely to stop buying, so the company can send them the right offer or message before they leave.
AI can study failure codes and past repairs tied to each asset to find patterns, helping teams adjust maintenance tasks and frequencies before repeat failures happen.
A chat assistant lets workers ask plain-language questions about maintenance, reliability, and asset management instead of digging through systems manually.
Machines send live readings to the maintenance system, which can trigger service when conditions show trouble instead of waiting for a fixed calendar date.
The company used analytics to decide how work should be distributed and standardized how teams process procurement and invoice tasks, helping clear backlogs and save money.
When the system detects signs of equipment trouble, it automatically alerts maintenance teams through the tools they already use so issues can be handled faster.
Use AI to sort outage events into the right categories and help prepare reliability reports regulators expect.
When AI figures out what is likely wrong, it can help managers make sure the right parts are available and required rules are followed.
The utility used a structured asset management system to prove to regulators that it runs the network reliably and cost-effectively, helping avoid penalties and earn incentive rewards.
A company rolls out AI agents carefully by choosing workflows, connecting systems, setting rules, and measuring results so the AI can act safely in customer service.
Evergy turned inspection photos and defect findings into a smarter way to decide which power-grid equipment needs money and attention first.
AI reads labels on utility equipment from photos to pull details like serial numbers and manufacturers, helping fix missing or incomplete records.