In current times, there’s hardly any industry left that has not been revolutionized by artificial intelligence and machine learning. Artificial intelligence in the telecommunications sector has delivered a significant number of benefits to the business, such as excellent customer experience, improved network reliability, and much more. Today, telecommunications are not only limited to faded audio calls, basic internet, and text messages; instead, it has made significant technological advancements with the help of IoTs and fast broadband services. AI in telecommunication has enlarged the global telecom IoT market with an amazing CAGR of 45 percent.
AI opportunities in the telecommunication industry are massive as the amount of data produced in the telecom sector is huge. To manage all that data, the industry leans towards AI implementations. If you are a telecom business or want to start a telecom business with AI integration, it might be the right time for you. Let’s find out about the implementation, various opportunities, and challenges of AI in telecommunication.
AI in Telecommunication: Implementation
AI has a million uses in every sector, and the telecom sector is no different. With AI, the telecom sector has been completely changed for good in terms of revenue generation, better customer service, better network quality, and an uninterrupted supply of services due to proper maintenance. There are numerous ways AI implementation can be done in the telecom sector; here are some of the most popular AI implementations.
Customer Service Automation By Virtual Assistants
Every customer is delighted by the rapid delivery of services or answers to their queries. AI-based chatbots help you delivers the solution of a query to the customers while ensuring utmost customer satisfaction. Chatbots with NLP capabilities can interpret the real meaning behind the customer’s choice of words. The best part is, these chatbots are trainable and can be trained as per the ethics of your business.
A trained chatbot can easily detect a customer’s tone of voice or word choice and deliver the solutions accordingly. Using machine learning algorithms and NLP, modern chat robots can analyse historical information, server ticket data and network logs, and real-time customer inputs to provide a pleasant customer experience and solve customer problems. Chat robots also play an important role in improving on-site maintenance, reducing technician visits, and achieving significant cost savings for the company. Various telecom companies also use virtual assistants that help the customers with their queries, recommendations, troubleshooting, etc.
Any customer will be grateful to a company if they prevent the fault before occurring with prior maintenance. Artificial intelligence in the telecommunication sector plays an important role in maintaining the delivery of uninterrupted service to the customers and ensures a high level of customer loyalty. Machine learning and artificial intelligence support the company’s oversight of equipment that can be proactive and prevent various failures. All these processes can be made possible with predictive analytics software. Customers remain happy due to preventive maintenance, which allows you to focus on other forms of customer service. With the use of sophisticated algorithms, collective data is processed and compared with historical information to predict future outcomes and implement preventive maintenance. These techniques can be applied to various forms of telecommunications equipment, i.e., data centres, mobile towers.
Optimization & Automation of Network
Various communication networks available in the market are complex and difficult to manage, which makes it difficult to deploy generic technologies more difficult. Artificial intelligence and ML technologies enable network operators to use advanced automation in network operations to help optimize network architecture and improve management and administration.
However, you can use network and device data to predict and proactively identify potential network-related problems and implement various improvements to optimize reliability. Further, quantitative and qualitative data related to various customer interactions, requests, complaints, service logs, and cross-channel portals can be analysed through AI, ML, NLP, and in-depth learning to reveal trends and performance issues across demographics and devices time zone, and locations. Above all, customers are loyal to the companies that deliver them the value for their money. With better services, your company can extend its user base.
AI in Telecommunication: Opportunities
When we talk about the telecommunications sector, there are a number of opportunities available. With an effective AI implementation, you can seize these opportunities and deliver effective services to the customers. The Telecom sector handles more than 6 billion calls per day, and these numbers keep on increasing. Managing various parameters of ideal service delivery can be hard manually. AI can definitely help in improving the current standards. Here are some of the opportunities in the telecom sector.
Enhanced Quality of Service
With machine learning and artificial intelligence systems, the telecommunications sector can instantly screen through tens of millions of CDRs, identify patterns that may indicate problems, create scalable data visualizations, and use predictive maintenance techniques to reduce dropped calls, poor sound quality and more. These techniques can be used to overcome other similar problems that may cause a dent in the customer satisfaction rate.
Enhanced Customer Satisfaction
Apart from effective services and 24×7 availability, maintaining an optimum customer satisfaction rate is difficult for various telecom companies all around the world. There are various AI opportunities in the telecom sector that can help in maintaining an optimum customer satisfaction rate and ultimately increase the profit generation rate. The main added value of telecommunication networks is the reduction of service calls, which means high costs for operators and diverts technicians from their work. By analysing huge amounts of data using massive learning methods, teams can identify unwarranted service calls and evaluate technician performance data to further improve customer service.
Conventional security technologies rely on rules and signatures to detect threats, but this information may soon become obsolete. Opponent tactics are evolving rapidly, and the number of advanced and unknown threats to communication service provider networks continues to grow. Artificial intelligence / ML algorithms could be taught to adapt to a changing threat landscape by making independent decisions about whether the anomaly is malicious or by providing a context to assist human experts. Artificial intelligence techniques, like neural networks and machine learning, have been used for years to improve the detection of malicious code and other threats in telecommunications. Artificial intelligence has the potential to go further, such as taking corrective action automatically or providing the human security analyst with the right type of information to act accordingly.
You may have received an anonymous phone call or an email that involved fraud. However, these fraud practices don’t pose a direct threat to telecommunications companies, but they can reduce long-term customer satisfaction. By implementing AI in telecommunication, these companies can easily identify suspicious speech or customer patterns and prevent potential fraud. The faster they will be able to fight fraud. Artificial intelligence strategies and techniques, such as advanced anomaly detection, make it easier for telecommunications companies to detect real party fraud. It would be beneficial for telecom companies to know the way of distinguishing legal defaults from fraud, as they will be able to focus their efforts on services that will enhance their user base and, ultimately, revenue.
AI in Telecommunication: Challenges
Implementing AI solutions to solve various problems is nothing new; applications such as chatbots and virtual assistants are common. The real challenge lies in implementing Artificial intelligence into networking. However, there are various challenges in AI implementation in the telecommunications sector. Such as privacy issues, network issues, and much more. Let’s have a look at the most generic AI faces in the telecom sector.
Lack of Resources
Network engineers in the telecom industry do not have the background to include the mathematical training and experience that is essential in ML. Network engineers usually do not have the background to include the mathematical training and experience that is essential in an ML space, such as data modelling and evaluation, software engineering and systems design, ML algorithms, and libraries. The result is a serious skills shortage. Open-source frameworks, such as Acumos, alleviate this problem. However, the widespread implementation of ML still requires a thorough understanding of mathematics, which is a scarce resource among CSPs today. However, the introduction of artificial intelligence should change the skill set of employees in most organizations, not just in telecommunications. In general, people have multiple opinions as they are worried that AI implementation in telecom may take their job, and on the other hand, they are excited to learn something new.
Lack of Tools
Challenged in AI adoption comes in numerous forms. One of the significant challenges in AI implementation is the lack of tools in the telecom sector. It is true that demanding applications are the motivation for new tools. There are two ways to address the lack of tools in the big data ages; we can enhance the functionality of our existing tools to solve new problems. It is natural that we expand existing tools to gain a new ability to meet unprecedented challenges. On the other hand, we can invent some new tools. These new tools can be invented considering the fact we have made significant progress in understanding the fundamentals of AI applications.
Artificial intelligence can be beneficial in improving every step of network operation. Artificial intelligence can learn faster than humans, so that with the huge amount of data produced by these networks, artificial intelligence solutions can identify patterns and potential network problems before they are even interrupted and even find and implement a solution. Managing the complexity of this huge network is overwhelming for network technicians and putting service providers at the back, not reacting proactively to their network. Implementing AI into networks provides an excellent opportunity to address service issues, reduce manual tasks and improve efficiency, which ultimately allows resources to be focused on creating a better and more customized, and soothing customer experience. This is the ultimate goal of an adaptive network-based on artificial intelligence.
Artificial intelligence and machine learning have affected the telecommunication sector in numerous ways. AI in telecommunication allows large data sets to be analysed quickly, problems to be identified, and data to be managed efficiently. AI not only saves you a lot of money and time, but it also delivers a competitive edge to you over your competitors. The future of AI has numerous opportunities for the telecommunication sector.
Matellio can help you in the implementation of AI solutions for telecom with its team of highly skilled and trained engineers. We excel in delivering services such as virtual assistant solutions, robotic process automation (RPA), network monitoring software, and much more. Our team of engineers and experts can develop a custom solution that will fit like a glove to your business needs. For more information, book a 30-min free consultation call!
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