Cell towers across the radio network may unfortunately crash our PC or laptop. This can have a serious impact on service, especially within busy regions or busy times of the day.
For network operators, all types of customers are now a common existence as competition has increased and new deals are continuously coming to the marketplace.
As we know the telecom industry is well known for creating user profiles to enable targeted marketing of new services.
Another major machine learning application in telecoms is finding fraud and revenue assurance, which directly impacts the bottom line.
It involves Network Planning and Design, Network Tuning, Network Audit and BenchMarking, improves customer loyalty, End-user performance optimisation, Network Planning, and investment
It involves AI-driven Predictive Analytics, Telco Data Analytics, Interactive Voice Response Systems, Data Center Management, Predictive Equipment Analytics.
It involves 5G networks, Cloud migrations, increased number of OTT, Automatic Network Planning, Configuring and Controlling. Low Latency, End-to-End Intelligence and Complex Network Deployments.
It involves maintenance of mobile tower functions, network quality, troubleshooting connectivity issues, network optimisation, AI-driven Video Analytics for surveillance and IoT sensors to enhance network utilisation and Asset Management
AIOps enables 5G network Optimization and Transformation with Churn Prediction, Hyper Personalization, New Product and Service Analytics, Intelligent Chatbots, Customer Retention and Advanced Analytics.
It involves System Architecture Innovation, Autonomous Driving Networks, Network Function Virtualization(NFV), Performance Management, Radio Access Networks, Cloud Based Network Management.
It includes Service Based Architecture, Optimizing Networks, Robotic Process Automation, Intelligent Wireless Networks, Intelligent Service Centers, Service Quality Monitoring, Autonomous Optimization.
It involves Network Configuration Management, Field Service Management, Real-Time Network Inventory Management, Service Orchestration, Network Auto-Discovery, Intelligence Assurance and Analytics, IoT Connectivity Management.
End-to-end federated AI across product & service portfolio. As previously stated, they have embedded AI into every layer, and they use it for predicting customer behaviour, dynamic orchestration, predicting incidents, etc.
As Gaurav mentioned earlier, the first and ultimate guideline is their customers. “Everything starts with the customer”, emphasises Gaurav. They work in a use case-driven, customer-centric, fast prototyping manner.
AI adoption is not the goal itself, it’s a journey and should be treated as such. Ericsson has been on this journey several years and can proudly say they have reached the AI and machine learning use cases industrialisation.