Business function
Scheduling, dispatching and supporting technicians and engineers on site.
19 organizations in 8 countries run these use cases in public. 55% of those 20 deployments disclose a measurable outcome.
An AI agent that diagnoses and fixes a customer's broadband, mobile, TV or device problem by conversation on the phone or in chat, running line tests and remote resets through the operator's systems, guiding the customer step by step, and booking an engineer or handing over to a technician when the fault needs a person.
An AI agent that turns network alarms into a clear picture of which customers are affected by an outage and why, tells them proactively by message, app or phone with a cause and an estimated fix time, answers their questions during the incident, and updates them until service is restored.
AI that decides whether a fault needs a site visit at all, predicts what work and parts a job will need, helps plan and update appointments, and gives technicians on site guided diagnosis and instant answers from manuals and past jobs, so more jobs are fixed on the first visit.
Machine learning that spots the early signs of network failure, such as degrading cells, faulty customer equipment, ageing hardware or planned digging near fibre, and triggers a preventive fix, a remote reset or a targeted intervention before customers lose service.
Machine vision and machine learning that inspect freight railcar wheels, bearings and other running gear as trains pass wayside sensors and camera portals at track speed, learn what a healthy wheel or a healthy reading looks like, and flag the ones that need attention before a crack, an overheating bearing or a worn wheel causes a service failure or a derailment.
Machine learning that learns the normal behaviour of industrial and energy equipment from sensor and process data, flags early signs of degradation weeks or months before a failure, and turns them into prioritised maintenance work, so plants and utilities plan repairs instead of reacting to breakdowns.
AI that analyses satellite, aerial or lidar imagery of the land along power lines to estimate where and how fast vegetation will grow into the lines or fall onto them, and turns that into a risk based trimming and hazard tree removal plan, replacing fixed trimming cycles and manual patrols.
Computed from the 20 public deployments behind these use cases. Numbers update with every checked record.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Containment rate | 70% | 44% to 73% | 3 | 1 organization, 2 vendor |
BNSF Railway, Comcast, Duke Energy, Entergy, Georgia-Pacific, KDDI, National Grid, Norfolk Southern, Openreach, Orange, Shell, Singtel, Telefónica España, Telstra, Verizon, Virgin Media O2, Vodafone, Vodafone Germany, nbn.
