Technicians once eyeballed turbines and guessed at efficiency. Now Copilot flags anomalies like falling oil pressure automatically.
A natural gas energy provider with more than 6,000 employees, most of them working in the field on inspections, repairs, and maintenance.
Frontline workers spent hours or days in the office pulling permits, finding maintenance manuals, and manually filling out forms, time that could...
“We have a team dedicated to Copilot that helped us with the initial training, and they’re available if we need them. But as far as the instructions and knowledge bases, we’re doing that in the field.”
Natural gas processing and transportation infrastructure company.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Williams's Equipment monitoring is part of this use case:
Related implementations across industries and use cases
Across 7,500 fuel stations, AI was stuck in tech teams. When VPs used Copilot openly and daily, skepticism broke. 1,300 employees followed.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.
Finance hunted IBANs by hand; legal had no self-service; HR drowned in FAQs. Six AI agents—one chat interface—freed 7,500 hours annually.
Shift reviews flew blind; diagnosing faults needed experience. Agents scan prior-day data each morning and brief leaders before rounds.
336 hours a month combing 8M daily logs by hand, always one step behind. Now engineers ask in plain language and act in minutes, not hours.
Forecasts that took 10+ hrs on shared CPU clusters now run in 150 sec—giving grid planners time to act, not just react.
No-code tools worked for chatbots, not agents. Coding each from scratch took weeks. Microsoft Foundry cut that to a day.
Multiple staff, one spreadsheet, inconsistent logs. Staff now describe incidents; AI classifies each, improving with every game.
Equipment faults left technicians hunting for technical data while machines sat idle. A chatbot now puts the answer in their hands.
Technicians once eyeballed turbines and guessed at efficiency. Now Copilot flags anomalies like falling oil pressure automatically.
A natural gas energy provider with more than 6,000 employees, most of them working in the field on inspections, repairs, and maintenance.
Frontline workers spent hours or days in the office pulling permits, finding maintenance manuals, and manually filling out forms, time that could...
“We have a team dedicated to Copilot that helped us with the initial training, and they’re available if we need them. But as far as the instructions and knowledge bases, we’re doing that in the field.”
Natural gas processing and transportation infrastructure company.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Williams's Equipment monitoring is part of this use case:
Related implementations across industries and use cases
Across 7,500 fuel stations, AI was stuck in tech teams. When VPs used Copilot openly and daily, skepticism broke. 1,300 employees followed.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.
Finance hunted IBANs by hand; legal had no self-service; HR drowned in FAQs. Six AI agents—one chat interface—freed 7,500 hours annually.
Shift reviews flew blind; diagnosing faults needed experience. Agents scan prior-day data each morning and brief leaders before rounds.
336 hours a month combing 8M daily logs by hand, always one step behind. Now engineers ask in plain language and act in minutes, not hours.
Forecasts that took 10+ hrs on shared CPU clusters now run in 150 sec—giving grid planners time to act, not just react.
No-code tools worked for chatbots, not agents. Coding each from scratch took weeks. Microsoft Foundry cut that to a day.
Multiple staff, one spreadsheet, inconsistent logs. Staff now describe incidents; AI classifies each, improving with every game.
Equipment faults left technicians hunting for technical data while machines sat idle. A chatbot now puts the answer in their hands.