In recent decades, communications service providers (CSPs) have shifted from voice and text services to the Internet, supporting a large number of mobile devices, from smartphones to wearables. With the deployment of new services and associated complexities, artificial intelligence (AI) and machine learning (ML) have become increasingly crucial to the enhancement of customer experience in the telecom industry.
CSPs need systems to learn quickly and autonomously to optimize the network architecture, control, and management as well as to enable more efficient operations to meet increasing customer expectations. The industry itself is, therefore, a candidate to adopt AI and automation solutions that improve network reliability, customer satisfaction, and retention and optimize their business processes for higher profit and efficiency. This post will touch some of the significant use cases of AI in telecom.
Network monitoring and management
AI can address a lot of network-related challenges while adopting software-defined networks (SDN), network function virtualization (NFV), cloud-based applications, and 5G technologies. By incorporating AI into network automation platforms such as ONAP, telecom companies can deliver efficient, timely, and reliable management operations.
First, AI can support network operations to identify problems, e.g., faults and breaches of Service Level Agreement (SLA), diagnose root causes, correlate across multiple event sources, filter noise out (false alerts), and recommend solutions. Besides, AI systems are designed to predict and identify anomalies or network problems so that companies can proactively take measures to address those anomalies before they are even made aware of or affected by them.
Another network use of AI is predictive maintenance. Any downtime for providers of telecommunications services may spell disaster. AI systems are a great solution to this challenge because they can identify patterns indicating a failure in the equipment’s routine maintenance checks. It allows companies to take proactive action before any downtime occurs.
Examples of network-centric applications of AI include:
- Anomaly detection for operations, administration, maintenance and provisioning (OAM&P)
- Performance monitoring and optimization
- Alert/alarm suppression
- Trouble ticket action recommendations.
- Automated resolution of trouble tickets (self-healing)
- Prediction of network faults
- Network capacity planning (congestion prediction)
- Intelligent initial access and handover; dynamic scheduling; resource optimization
Customer service and virtual assistants
Customer service is a critical component of the telecom industry, and the use of chatbots to increase or replace human call centers is one of the key AI applications to date. Several customer actions require specific support from customers – whether it is to change bill plans or refunds, making payments or raising complaints. Instead of increasing the number of agents, AI can handle customers directly with messaging applications such as WhatsApp. Chatbots can very quickly provide superior customer service. They provide the customers with a single point of contact and are available 24/7 so that they can take the necessary steps without having to rely on the help of a human agent. It reduces costs and enhances efficiency.
AI uses different chatbots and other mechanisms for customer interaction to address support requests and improve self-care activities. It can also be used to automate customer impact resolution of network and service problems. AI can even prevent issues by continuously analyzing the development of the health of networks and services and triggering preventive actions. AI can also improve customer-related data analysis to identify and predict customer satisfaction in the immediate future. It is critical for DSPs who want to anticipate and respond to problems or to provide their customers with the right service at the right time. AI may be used for CRM in areas such as custom promotions, cross-selling / up-selling opportunities, prediction of churns, and mitigation.
Other examples of AI usage in customer service/support include:
- Knowledge portals and AI assistants for human agents
- Contact center optimization and compliance
- Customer voice and text sentiment analysis
Security is one of the most popular AI applications in telecom. For identifying threats, traditional security technologies depend on rules and signatures. However, the information may soon be outdated. Tactics of adversaries are evolving rapidly, and there are more and more advanced and unknown threats to CSP networks. Algorithms can be trained to adapt to the changing threat landscape, take independent decisions on a malicious anomaly, or provide a context for helping human experts.
For many years, AI techniques such as neural networks and ML have been used to improve malicious code detection and other threats to telecommunications traffic. And AI can go even further, for instance, to take automatic remediation measures or present the right type of information to inform a human security analyst about a decision or maybe a recommendation. Many established suppliers and AI startups are developing solutions that help CSPs manage their IoT devices and services more securely and use automatic profiling.