Attention! Machines are learning telecommunications and they're good at it!
Technology revolutionizes the world every single day. Telecommunications are here with us for a whole century.
November 02, 2020
Somehow they are the pioneers of this tech revolution. Somehow, they are changing and growing even more in the present. Somehow, they are going further into thefuture with the help of AI.
Practically every telecom is investing heavily in AI. There's a consensus about this, as 93% telecom representatives see ML as a game-changing technology and 76% are planning to incorporate it into the business within three years, according to a survey by Digitalist Magazine at Mobile World Congress. Telecoms need Machine Learning to be able to process and analyze the data in many areas: customer experience, network automation, business process automation, new digital services, and infrastructure maintenance.
Hello, this is your Chabot speaking
For telecoms, which serve millions of people every day, customer service automation creates substantial savings. Each day telecoms receive more tickets from their customers, and the structure of each case is getting more complicated.
Chatbots are a machine learning trend that offers a valuable solution to this problem. As human consultants cannot process all the data, telecoms need chatbots to make customer service faster and more scalable and improve client satisfaction.
Maintain and predict
Mobile towers are the perfect object for ML predictive maintenance solutions. They are difficult to access and require time-consuming on-site inspections of complicated modules such as power generators or air conditioners. Moreover, towers are vulnerable to intrusions, as they contain a lot of valuable equipment.
There are various possible applications of ML in the maintenance of mobile towers, such as empowered surveillance, where video and image analysis can help detect anomalies. The telecommunications infrastructure is already equipped with various sensors. The data those sensors collect can be used for training ML models, which will predict possible failures – this would reduce downtime and repair costs, and also improve the coverage.