One of the main aspects fuelling this market is an increased investment in 5G infrastructure deployment due to a shift in customer preference for next-generation technologies and smartphone devices.
Increased need for value-added managed services, a growing number of mobile users, and surging demand for high-speed data connectivity are all major market drivers. Over the last few decades, the global communication network has clearly been one of the most important areas for continuing technical advancement.
The time it takes to transport large volumes of data has been decreased from days to hours, and now to a few seconds, thanks to data networking.
AI & the DNA — Devices — Networks — Applications — for Telecom
Artificial Intelligence is helping the telecom companies improve performance that has become a vital part of their digital transformation. Providers of communications services are under increasing pressure to deliver higher-quality services and a better customer experience. To exploit these emerging opportunities, they’re relying on the vast amounts of data they’ve gathered over the years from their massive consumer base.
To deal with increasing network complexities, expanding networks, ever-changing communication technologies, and the massive amount of data created, several telecom businesses have begun to use AI solutions.
The biggest challenge in telecom is voluntary turnover, which can reach 67%. Customer losses have a direct impact on telecom company metrics.
Use Case 1 — ChatBots — some companies like moviestar have successfully cut their customer support costs by 30% and increased their customer retention rate to over 80% by using AI Chatbots. They are assisting telecom firms in lowering attrition rates, successfully handling recurring customer requests, reducing wait times, redirecting customer questions to the appropriate department or automated personalized responses, sending automated reminders to customers, and collecting customer feedback.
Most of the customers use self-service to pay bills or change accounts. As a result, chatbots are no longer a “nice to have.” It’s now more important than ever to provide excellent client experiences. Chatbots with NLP capabilities can interpret client sentiment a step ahead. These bots can react accordingly based on the customer’s tone or choice of words by using sentiment analytics.
Use Case 2 — Fraud Detection and Prevention — AI is playing a significant role is fraud prevention; according to the Communications Fraud Control Association (CFCA), fraud costs the industry $18.2 billion per year. Because of the close ties between operators, fraudsters have a strong chance of embedding themselves in the business chain and committing fraud. To validate calls in real time, both the originating and terminating networks use AI.
Use Case 3 — Contact Centers: Although AI may never be able to completely replace human involvement in contact centres, it is assisting telcos in identifying issues earlier, resolving them more quickly, and preventing cases from repeating.
In the telecom industry, AI is often linked with the development of virtual assistants, or chatbots, but it is increasingly being utilised to help operators manage their networks more efficiently, save OPEX, and provide better service to customers.
In the telecommunications industry, artificial intelligence applications use complicated algorithms to search for patterns in data, allowing telcos to discover and anticipate network anomalies, as well as proactively address issues before they affect users.
Use Case 4 — Network Quality & Optimization: For a massively greater number of office workers, video calling moved swiftly from being a novelty to an everyday expectation. Now the telecom companies need to maintain the quality of telecommunication services while under pressure which is manually impossible. Hence with ai companies automatically crowdsources when there is a speed and data issue that signals the AI chatbot that either resolves the issue or sends the right message to customer.
Use Case 5 — Network Fault Detection: Communication Service providers (CSP) are showing keen interest in implementing AI-based network fault detection systems. CSPs are increasingly integrating AI-based network problem detection systems as AI advances. To distinguish false or symptomatic alarms from crises, AI/ML systems classify alarms/alerts based on their previous behaviour. It’s nearly hard for network managers to sort things out on their own, especially when there are multiple networks to deal with. So, with AI, systems learn to predict faults intelligently and can pinpoint the source of a network outage.
Use Case 6 — Network Investments: Rather than focusing on locations with the highest population and traffic levels, Orange is utilising big data and machine learning to target its CAPEX investments to cell sites that produce the highest margins and reduce churn.
Use Case 7 — Network Fault Prediction: Companies are deploying AI-powered video cameras for routine on-site inspections. They alert the operators in real time in the event of risks or other disasters such as fire or smoke. The IoT sensors on mobile towers keep a close eye on the entire asset infrastructure, and various machine learning algorithms analyse the enormous data.
Use Case 8 — Effective Campaign Management: Machine learning predicts the budget of customers with the data collected and offers the best service to customers with advanced customer service. While machine learning does all of these, it actually provides companies with great convenience, contributes to companies saving time and money, and increases the sales with effective campaigns for ROI.
Telecom providers are using AI and machine learning to gain insights into client behaviour and identify their preferences. This allows them to send out offers that are suited to the needs of their subscribers at the proper moment.
They learn more about the percentage of clients who like or detest offers that are presented to them, as well as which customers are satisfied with their network’s products. This information, when further processed, is turned into campaigns that aid in efficiently targeting clients.
Use Case 9 — Robotic Process Automation (RPA) — Machine learning RPA is being adopted by the telecom industry to cut costs, improve customer service, increase operational efficiency, and improve data quality. Many business processes, such as service fulfilment, service assurance, billing, revenue management, and more, are largely manual, repetitive, and rule based. By automating these operations, RPA can save people hours.
The telecommunications business in the Asia-Pacific area has one of the greatest adoption rates of RPA technology, with a fantastic growth rate of 60%.
Use Case 10 — Reduce Churn: Customers leave telecom companies for a variety of reasons. Everything from new rivals to poor service can cause churn, and it’s tough to tell out why on your own. Artificial intelligence and machine learning are sifting through massive volumes of data to determine the precise reasons behind each customer’s departure. This data is being used in predictive analytics to anticipate when and why consumers will quit. As a result, telecom firms are already contacting customers to resolve concerns.
Customers have a lot of options because new features, plans, and rivals are continuously being introduced. Customers’ opinions on telecom services are important to companies. Conversational history is analyzed by AI to see how clients react to certain offerings. Telecom Chatbots are now capable of reading clients’ emotions and determining their interest in a new offering.
The Future of AI in Telecom Industry
Artificial Intelligence has huge potential in the telecommunications industry. Most of the world’s top telecommunications companies have already begun to use AI and Machine Learning in a variety of ways. Only those telcos that fully utilise AI tools will be successful.
Over the next few months, telcos across the globe are going to face these demands.
Demand 1 — Telcos must reimagine the customer experience from the ground up by offering extreme personalisation, immersive experiences, and superior product packages for both consumers and businesses. It will be critical to do so, as demand patterns are currently mixed.
AI can help telcos reimagine customer connections by understanding tailored needs and engaging with customers through hyper-personalized one-to-one interactions. It can assist in the configuration of VPN, teleconferencing, and productivity app bundles for fixed and mobile networks. These packages will be especially appealing to commercial consumers, whose telecommunications usage has changed from offices to residences, and from field demand to fixed-line demand. AI also enables operators to more correctly forecast demand, anticipate network load, and automatically adjust capacity and throughput.
Demand 2 — Telcos will need to reconfigure their supply chains based on data, accelerate the rollout of 5G cellular systems, and deploy smart supply chain and employ smart manufacturing.
The first stage in building a bionic supply platform should be to initiate a data-based reconfiguration; supply-side risks for telcos will remain high for the foreseeable future. Equipment and devices have grown more difficult to procure as a result of global supply chain disruption.
Telecom companies can increase the speed of new system deployments and improve network resilience by adopting onshoring and nearshoring, which are optimised by the global supply chain. Self-enabling cognitive supply chain modelling and decentralised, on-demand 3D-printing nodes can be used to optimise network performance and replace parts in real time. Telcos should leverage blockchain-based protocols and AI to protect their IP as they transform their supply chain into a bionic supply platform.
Here are three takeaways for the AI leaders in the telecommunications industry.
Integrate AI into the CORE of your business
Early telecom sector leaders have remade themselves by incorporating AI not only into their goods, but also their core operations. Companies in the telecom industry must first determine their priorities by determining where AI will add the most value. Then, in order to streamline business operations and give greater value to customers, companies should roll out use cases in those priority areas from beginning to end. Example: Using AI, the system should be able to suggest to the operator what plans should be deployed to maximise revenue.
Create AI-Based Business Models
Build new business models and ecosystems that can lead using data and analytics. Telcos may use AI to promote growth in areas like health, media, and entertainment, as well as third-party analytics and other digital services. Companies can gain market share by leveraging their best-in-class data assets, data scientists on staff, and the platform they’ve built. While there are obvious advantages to doing so, telcos must be cautious to prevent dangers to consumer privacy that could jeopardise years of customer confidence.
Make the C-suite Own AI
While implementing AI at scale necessitates a shift in employee mindset and culture, it’s critical to prepare your workforce for change. Getting the C-suite to own the AI deployment process is crucial to doing this. Making the company’s executive committee members accountable for delivering outcomes and value throughout the AI journey is one strategy to ensure AI ownership at the top. Companies can help executives by establishing a shared resource, such as an AI centre of excellence, that can be used by all functions.
Future-Proof your Data and Systems
Companies in the telecommunications industry should guarantee that their data and technological assets are AI-ready. This necessitates two steps: developing a data governance framework to ensure that the data is useful, consistent, and valuable, and overhauling the IT infrastructure to make it more flexible and resilient. This will allow AI-driven telecoms to scale their AI applications by breaking down the siloed complexity of old IT systems.
Without a question, AI has the ability to assist global telecom firms in achieving their plans, goals, and overcoming problems. As a result, they must immediately expedite their AI reforms!
Would love to hear your thoughts on this article and/or the innovations that you are doing with AI.
This post was written by Asokan Ashok, the CEO of UnfoldLabs. Ashok is an expert in driving customer insights into thriving businesses and commercializing products for scale. As a leading strategist in the technology industry, he is great at recommending strategies to address technology & market trends. Highly analytical and an industry visionary, Ashok is a sought after global high-tech industry thought leader and trusted strategic advisor by companies.
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