Artificial Intelligence in Telecom
In today’s digital world, things are getting smarter, more connected and more sophisticated. A major role in this evolution plays innovative technologies, especially artificial intelligence. It is predicted that the AI–based solutions in the telecommunication market will reach up to 1 billion U.S. dollars by 2023 globally. Sounds impressive, right? Network solutions that can be driven autonomously are in great demand in the telecom sector that makes it a key driver for artificial intelligence growth. AI usage in the telecommunications industry helps companies to boost growth and revenues, while also opens up unique opportunities to improve the customer experience, enable self–service, improve equipment maintenance, and reduce operational costs. According to the recent stats, 63.5% of the telecommunication companies started to invest in AI–powered solutions. The reason is the development of highly personalized products, improved fulfillment processes and enhanced network management. Let’s delve into details and find out how modern telco companies use AI–driven solutions today?
AI Use Cases in the Telecommunications Industry
Below we have outlined key areas where artificial intelligence is changing telecommunications industry. Let’s delve into details below:
Virtual assistants and customer service
Undoubtedly, customer service is an essential component in any industry, and the telecom industry isn’t an exception. Several customer actions require specific support from the staff. Whether customers want to change bill plans, have difficulties in making payments or raising complaints. Intelligent virtual agents powered by AI technology are gaining traction in the telecommunication and helping to increase or replace human call centers. According to a recent survey, virtual assistants allow telecom service providers to save 1.2 billion U.S. dollars on customer services and support. Not only can companies to optimize a variety of support requests for billing inquiries, maintenance, etc, but also AI–powered assistants can solve all service–type questions and process transactions fast and efficiently. Other examples of AI usage in customer service/support are the following:
- contact center optimization;
- customer voice and text analysis;
- knowledge portals and AI assistants for the staff.
Robotic Process Automation (RPA)
A variety of challenges currently faced by telecom companies can be addressed by RPA solutions. When implemented successfully, RPA can provide companies with a huge variety of robot helpers. Not only can RPA solutions help to minimize pressure on humans by freeing up time to focus on revenue generation, but also they deliver better customer experience and high–quality services as well. In addition to that, RPA allows network teams to discover faults faster, better deliver and maintain service quality, retrieve data for revenue forecasting, decision making and planning. Only by automating business processes through RPA can repetitive and time–consuming operations and tasks be done more accurately and more efficiently.
Better monitoring and management of networks with artificial intelligence systems
Adopting software–defined networks (SDN), network function virtualization (NFV), cloud–based applications, and 5G technologies can be done thanks to AI technology. Only by incorporating AI into network automation platforms can companies in the telecom environment deliver efficient and timely management operations. AI–powered solutions can support network operations to identify problems, such as Service Level Agreement (SLA) faults and breaches, diagnose root causes, correlate across multiple event sources, detect false alerts, etc.
What’s more, AI–based systems can predict and identify anomalies or network problems that helps companies to proactively take measures before problems arise. Also, AI is really helpful in predictive maintenance. Telecommunications services providers may face downtime that is a disaster for them. AI–powered solutions can identify patterns indicating a failure in the equipment’s routine maintenance checks that allows companies to take proactive actions before any downtime occurs. Let’s take a look at examples of network–centric applications powered with AI:
- anomaly detection;
- performance monitoring and optimization;
- prediction of network faults;
- network capacity planning;
- dynamic scheduling and resource optimization.
Artificial intelligence to detect frauds
There is a great number of users in the telecommunication environment that makes it attractive for fraudsters and thefts. The fraudulent activity involves theft or fake profiles, illegal access, cloning, behavioral fraud, etc. that has significantly impacted the relationship between a telecommunication service provider and a user. Only by using smart machine learning (ML) algorithms to a wealth of data retrieved from customers and operators can help providers to define the “normal” behavior and prevent fraud. Mentioned above algorithms learn how “normal” activity looks like and define the anomalies quicker than humans. These algorithms are highly sophisticated and can provide a real–time response if there are any suspicious activities.
The data-driven approach in decision making
Retrieving and processing terabytes of customers’ data can be tedious and time–consuming for any industry, especially for telecoms. Thanks to AI and ML-driven solutions, companies can elicit meaningful data and provide business insights that helps them to make better decisions in business. What’s more, received data help with customer segmentation, churn prediction and prevention, increasing margins, optimizing prices, and so on.
To sum up, applying artificial intelligence and machine learning-driven solutions, the telecom landscape generates insights and extract conclusions from the vast amounts of data. Not only can that help them to resolve issues and business problems, but also companies manage daily processes more efficiently and deliver enhanced customer services. Moreover, the adoption of AI and machine learning (ML) is essential for the survival of the company and its ability to outperform competitors.
AI Applications in the Telecom Industry
Below you can read how companies implement high–value use cases that transform the telecommunication sector.
- Telia: this company applies AI and ML–powered technologies to detect the most valuable accounts based on available data, keeping the company’s database always up–to–date. Moreover, the company has implemented virtual assistants to deliver better customer services and has saved up to €1 million.
- Vodafone: incorporating virtual assistant app TOBi, company has improved customers’ engagement and delivered more unique sales journey. An intelligent assistant solves the problems customers face by answering FAQs, suggesting and offering products to meet customers’ needs.
- China Mobile: implementing AI–powered solutions, the company better identifies frauds by detecting anomalies and fraudulent activities, distinguish them and prevent fraud.
Final thoughts: Is your telecom company powered with AI?
With the deployment of new services and associated complexities, artificial intelligence has become increasingly crucial for the telecom industry. Not only can AI–powered systems learn quickly and autonomously to optimize the network architecture, control, and management, they also exclude workforce repetitive operations, detect critical cyber–attacks and meet customer expectations. Reshaping the telecommunications industry, AI provides numerous advantages for telecom companies of any size by eliminating overload and efficiently dealing with customer inquiries. In addition to that, intelligent AI–driven virtual assistants deliver convenient and personalized services that significantly improves customer satisfaction. Don’t hesitate to develop and implement AI–based systems to maximize efficiency and drive business growth.telecom