AI-driven predictive analytics is one of the latest AI trends
in the telecom industry, and is used to help telcos provide better services. They do this by utilizing data, sophisticated algorithms, and the latest techniques for forecasting future results based on historical data. Telecoms can use data-driven insights to monitor the state of equipment, predict failure, and proactively fix problems with communications hardware, such as cell towers, power lines, and data center servers.
In the short-term, network automation and intelligence will enable better root cause analysis and fault prediction. Long term, these technologies will underpin more strategic goals, such as creating new automating customer service experiences and dealing with business demands more efficiently.
American telecom giant AT&T is using machine learning to enhance its end-to-end incident management process by detecting network issues in real-time. This predictive maintenance AI technology can address 15 million alarms per day, restoring service before customers notice any break. The company is also relying on AI to support its maintenance procedures, using drones to expand its LTE network coverage and utilizing the analysis of video data captured by drones for tech support and infrastructure maintenance of its cell towers.