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Top 3 Applications of AI and Machine Learning in the Telecom Industry

The telecommunication industry is riding the waves of the tech revolution and digital transformation to offer a wider variety of services to its consumers. However, consumers in today’s digital world are not going to be happy with run-of-the-mill products and services – they also demand a better quality of services and more responsive service providers. Solutions powered by artificial intelligence and machine learning can help telecom companies to fulfill these expectations.

Artificial Intelligence is transforming the way the telecommunication industry operates. The adoption of AI technology based solutions has grown, especially to drive efficiencies and to fulfill the consumers’ demand for contextualized experiences. According to Transparency Market Research, “the global market for artificial intelligence is estimated to post an impressive 36.1% CAGR between 2016 to 2024, rising to a valuation of US$3,061.35 billion by the end of 2024 from US$126.14 billion in 2015.”

Telecommunication companies have traditionally battled challenges like network operation and infrastructure issues, complex nature of networking systems, improper utilization of resources, traffic congestion and delay, virtual assistance related issues, network and transmission failures, and ever-increasing bandwidth requirements.

Application of artificial intelligence for the telecom sector has helped organizations to boost growth and revenues, while also helping to improve network capabilities and enabling faster processing of a large volume of data. As the use of connected devices continues to skyrocket, more and more CSPs (communication service providers) are jumping on the bandwagon, recognizing the value of artificial intelligence applications in the telecommunications industry.

Machine learning and AI applications in the telecom sector

Telecom giants and innovative niche players are leveraging AI/ML powered solutions to tackle a wide range of tasks. Let’s take a look at applications of AI/ML that can help telecom companies solve some of the most persistent problems faced by the industry.

Chatbots for operational support and automated self-service

It often seems that some telecom companies, in order to reduce the user complaints, make it difficult for the user to access the options for online chat, phone numbers, and contact forms on the website and user portals. And when the customer finally connects with a person over chat or call, they often do not get the information or answers they seek.

According to research from NewVoice Media, “an estimated $62 billion is lost by U.S. businesses each year following bad customer experiences.” Usually, telecom companies receive complaints from the customers regarding the connectivity of the equipment like Internet Protocol Television (IPTV) boxes, modems, and other devices. It is not possible to dispatch the technicians every time and many of the problems could be solved with a single reset, or other known standard solutions to known issues.

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The limited number of people manning the chats and phones, compared to a much larger volume of customer service requests, is the weak link in the process. However, with machine learning based chatbots, companies can have 24/7 chatbots, helping customers quickly access the information they require with the help of a ticketing system.

In addition, chatbots with NLP capabilities have the ability to interpret the meaning behind the customer’s words. Such chatbots can also detect from a customer’s tone of voice, or word choice, if the customer is frustrated or angry. With machine learning algorithms and NLP, modern chatbots can analyze historical information, server ticket data, and networking logs, and the customers’ real-time inputs to deliver a delightful customer experience and to solve the problems faced by the customer.

Apart from offering a better customer experience, OSS (operational support system) chatbots also play an important role in improving on-site maintenance, reducing technician visits, and delivering significant cost savings to the business.

Telecoms are also adopting virtual assistants to help businesses with a huge number of support requests for installation, set up, maintenance, and troubleshooting, which are traditionally handled by customer support centers. These solutions are backed by self-service capabilities – the assistants can offer advice to customers about their devices and offer them step-by-step guidance for troubleshooting.

ABI Research predicts that “the virtual assistants will enable telcos to save US$1.2 billion on customer care management by 2022 with a CAGR of 17% over the next five years.” Big telecom giants have already developed and successfully used AI-enabled voice assistants in their network; for example virtual assistants that suggest solutions for network-related issues have helped organizations reduce customer service costs generated by phone inquiries.

Some other prominent examples include Comcast’s Voice Remote that allows customers to interact with their Comcast system through natural speech. DISH Network and Amazon’s partnership allows customers to search or buy media content by spoken word rather than a remote control or click of a mouse, via Alexa. This solution also integrates visual support within IVR (interactive voice response), which improves the overall efficiency of the process, helping to reduce the average handling times and customer hold times, ultimately driving better customer experience.  

Network Automation and Optimization / Network operation monitoring and management

Most communication networks are complex and difficult to manage, so there is an increase in complexity with the deployment of technologies like SD-WAN and services like SDN and NFV. AI and ML technologies can allow network operators to leverage advanced automation in network operations, which can help to optimize network architecture and improve control and management.

Network and device data can be used to predict and preemptively identify possible network-related issues and apply fixes to optimize reliability. In addition, quantitative and qualitative data related to customer interactions, requests, complaints, service logs, and cross channel portals can be analyzed using AI, ML, NLP, and deep learning to uncover trends and performance issues across demographics, device, time zones, and locations.

Predictive maintenance using AI applications

Predictive analytics, powered by AI, enable telecom companies to leverage data, sophisticated algorithms and advanced machine learning ability to forecast future results by building on historical data. Artificial intelligence algorithms use data-driven techniques to monitor the current condition of equipment and predict equipment failure based on the analysis of previous patterns. This makes it possible to proactively fix issues with equipment like power lines, data center services, cell towers and also the various devices that are placed in the homes of the customers.

There is no doubt that machine learning and artificial intelligence will make the edge more intelligent and pave the way for next-gen telecom solutions. We have machine learning capabilities across cloud, hardware, neural networks, and open source frameworks.

eInfochips provides a wide range of services to the telecommunication industry, including solutions for AI and ML to help organizations build highly-customized solutions running on advanced machine learning algorithms. Our AI and machine learning services help telecom clients in implementing a highly- scalable, reliable, and cost-efficient solution that combines AI, ML and IoT.

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