AI-driven predictive analytics is one of the latest trends
in helping telcos provide better services by utilizing data, sophisticated algorithms, and 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 issues prediction. Long term, these technologies will underpin more strategic goals, such as creating new customer experiences and dealing efficiently with business demands.
AT&T company is using machine learning to enhance its end-to-end incident management process by detecting network issues in real-time. The technology can address 15 million alarms per day, restoring service before the customers notice any break. The company is also relying on AI to support its maintenance procedures: the telecom giant is using drones to expand its LTE network coverage and to utilize the analysis of video data captured by drones for tech support and infrastructure maintenance of its cell towers.