Leveraging Artificial Intelligence in the Telecommunication Sector

Telecom Business Review | Thursday, October 27, 2022

Artificial intelligence offers several prospects in telecommunications. Companies can utilize these opportunities and provide better customer service by deploying AI effectively.

FREMONT, CA: Artificial intelligence (AI) offers several prospects in telecommunications. Businesses can maximize these opportunities by deploying artificial intelligence effectively.

AI can drastically enhance quality factors. According to PRNewswire, the global market for AI in telecommunications will increase at a CAGR of 49.8 percent between 2021 and 2026, from USD 773 million in 2019 to USD 1,3450 million. Thus, it is apparent that Telecom firms can achieve greater heights by utilizing AI.

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Here are examples of how AI has affected telecom companies in various ways:

Increases Service Quality: The telecoms industry might employ machine learning and AI systems to review millions of Call Detail Records in real-time, identify trends that may suggest difficulties, and eliminate dropped calls, poor quality, and other issues using predictive maintenance techniques. These tactics can be applied to many situations that impede consumer pleasure.

For example, GeakMinds assisted a Fortune 500 firm with Network Capacity Planning. The client's problem was managing thousands of servers transporting internet traffic and preventing overutilization and traffic loss.

The issue was resolved by developing a capacity planning strategy that forecasts utilization using Machine Learning algorithms. This enabled the client to maximize server utilization and save CapEx, ensuring service quality.

Enhances Client Satisfaction: The telecom industry faces a great deal of difficulty in maintaining a high level of client satisfaction in addition to offering great services and being available 24 hours a day. There is a range of AI options in the telecom industry that can assist in maintaining a high level of customer satisfaction and, consequently, increase profit generation.

For example, consider the case of Verizon. Verizon's strategy consisted of shifting 17,000 customer service representatives to a virtual work paradigm while retaining physical retail locations. Staff at its stores use AI-enabled technologies to operate a touchless sales environment, ensuring worker safety and high customer satisfaction.

AI is not meant to supersede humans in the workforce. Instead, Verizon encourages its employees to view AI as a tool that enables them to provide more imaginative, comprehensive answers to the problems they face by eliminating repetitive job duties.

Increases efficiency: Managing complexity to provide the greatest customer experience while remaining cost-effective and efficient is challenging for telecom firms. Nokia's new digital services leverage AI and machine learning to save network maintenance time and effort while ensuring engineers have access to the most current network information and capabilities.

Consider a real-world instance. In 2021, Nokia and Vodafone announced a jointly created machine learning tool that operates on Google Cloud and detects and resolves network problems before they harm Vodafone customers. The software rapidly detects and fixes anomalies that can influence the quality of client support, such as mobile site congestion, interference, and unexpected latency. The Anomaly Detection Service is anticipated to detect and automatically address around 80 percent of Vodafone's atypical mobile network issues and capacity needs.

Fraud Detection: Telecom network engineers can detect instances of unwanted access and bogus caller profiles using machine learning techniques. These algorithms monitor CSPs' global telecom network activities to achieve this objective. Consequently, the network traffic on these networks is closely watched.

AI algorithms can recognize patterns again, which allows network administrators to identify potentially hazardous situations, such as a large volume of bogus calls from suspicious numbers. Telecom frauds include Scam Calls, Mobile Money Fraud, SMS Fraud, Subscription Fraud, and Spoofing, among others.

For instance, Vodafone's relationship with a data science-based company, which analyzes the telecom giant's network traffic for intelligent, data-driven fraud management, is one of the most famous instances of a telecom company using data analytics for fraud detection and prevention.

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