Why AI is the Catalyst in Transforming the Telecom Industry


Numerous advancements are providing telecom operators with the ability to respond to business requirements by creating limitless applications on top of artificial general intelligence.

The explosive growth of data, coupled with advancements in computational power and sophisticated computing architecture, has propelled artificial intelligence (AI) into the forefront of innovation across diverse industries such as retail, finance, healthcare, and transportation. These early adopters have successfully leveraged AI to redefine their respective industries and transform their operational landscapes. However, telecom operators, until recently, have been somewhat slow to embark on their own AI journey.

That is about to change. Telecommunications operators are now recognizing the immense potential of AI and are beginning to embrace its transformative power. The telecom industry, with its vast networks, massive volumes of data, and critical role in connecting people and businesses globally, stands to benefit significantly from AI integration. By harnessing the capabilities of AI, telcos can unlock new opportunities and drive profound changes in their operations. AI can enable them to optimize network performance, enhance service quality, streamline processes, and deliver personalized customer experiences. Furthermore, AI-driven automation can improve efficiency, reduce costs, and empower operators to provide innovative digital services beyond traditional connectivity.

Telecom operators can leverage AI in various areas. For instance, AI-powered network management can enable predictive maintenance, intelligent resource allocation, and dynamic network optimization. AI algorithms can analyze data in real time, making network operations more efficient and responsive. Additionally, AI can revolutionize customer experiences by personalizing services, anticipating customer needs, and enabling proactive issue resolution. Virtual assistants and chatbots powered by AI can offer 24/7 support, enhance self-service options, and provide instant responses to customer queries.

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Moreover, AI can play a pivotal role in transforming business models and revenue streams for telcos. By analyzing vast amounts of data, AI can identify new market opportunities, optimize pricing strategies, and support the development of innovative value-added services. Telcos can explore partnerships with other industries, such as smart cities, healthcare, and entertainment, to deliver comprehensive solutions and capture new sources of revenue. As telecommunications operators embark on their AI journey, they have the potential to redefine their industry and shape the future of connectivity and communication. By embracing AI technologies, they can unlock unprecedented insights from data, drive operational efficiency, and deliver exceptional customer experiences. The telecom industry is poised to benefit from the power of AI, enabling operators to lead the way in the digital transformation landscape.

New technologies are driving telecom to AI

The convergence of 5G networks, the Internet of Things (IoT), and the rising quantity of Big Data are the driving factors that have propelled communications service providers (CSPs) to turn their attention to AI. As explained in an Allied Market Research report – AI in Telecommunication Market 2022: “The global AI in telecommunication market size was valued at $1.2 billion in 2021, and is projected to reach $38.8 billion by 2031, growing at a CAGR of 41.4% from 2022 to 2031”.In an effort to effectively navigate this era of unprecedented connectivity and data volumes, telecom companies (telcos) are turning to AI as a critical facilitator for innovation, operational efficiency, and enhanced customer experiences.

By combining advanced algorithms, machine learning (ML), and deep neural networks (DNN), AI technologies can analyze vast datasets, identify patterns, and make intelligent predictions. With the introduction of 5G, many telecom operators have begun to integrate 5G into this mix.

It’s been said that 5G provides a supercharging force to AI. When telecom and AI are integrated, CSPs gain immense benefits, including:

  • Reliable, high-speed network infrastructures: AI-powered devices and applications gain the ability to access and process data in real time, resulting in enhanced performance, responsiveness, and scalability.
  • Virtual network management: Although not fully deployed, the rollout of 5G is quickly hitting the mainstream. 5G, in conjunction with the introduction of Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV), has enabled AI to play a critical role in managing virtualized networks. AI algorithms can optimize resource allocation, orchestrate virtual network functions, and automate network provisioning and scaling. This benefits CSPs by enabling more flexible, efficient, and agile network management.
  • Revenue assurance: AI helps in revenue assurance by detecting and preventing revenue leaks and billing errors. Machine learning algorithms can analyze billing data, identify discrepancies, and automate the reconciliation process. This enables operators to deliver accurate billing, minimize revenue losses, and improve financial performance.
  • Fraud detection and security: AI-powered security systems can protect networks from cyber threats, including malware, Distributed Denial of Service (DDoS) attacks, and network intrusions. Additionally, AI plays a crucial role in detecting and preventing telecom fraud. Machine learning algorithms are being leveraged to analyze network traffic patterns and identify suspicious activities, such as SIM card cloning, subscription fraud, or unauthorized access attempts.
  • Predictive analytics: AI and machine learning algorithms enable telecom companies to leverage vast amounts of customer data for predictive analytics. By analyzing historical data, operators can forecast demand, predict customer churn, and identify potential revenue opportunities. This information helps in making intelligent, data-driven decisions for network planning, marketing campaigns, and service offerings.
  • Network optimization: AI algorithms can optimize network resources by dynamically adjusting capacity, routing, and configuration based on real-time demand. This helps in maximizing network efficiency, reducing operational costs, and improving Quality of Service (QoS) for customers. AI-driven optimization techniques also facilitate the deployment and management of emerging technologies like 5G and edge computing.

On a global basis, telcos are still in the process of launching 5G, making now the right time for operators to set their sights on harnessing the power of artificial intelligence. This will enable them to not only deliver value to the customer but also develop innovative solutions and new revenue streams that leverage the big data that is now being produced in terabytes.

Not only can telcos leverage AI for internal benefits, but it also gives them the ability to become an enabler for other domains. By combining AI and 5G, CSPs are able to extend their reach and become a critical partner to organizations within other industries and domains, such as:

  • Smart cities and infrastructure management
  • Healthcare and telemedicine
  • Industrial transformation 4.0
  • Agriculture industry
  • Digital governance
  • AR/VR industry and the gaming industry

Now’s the time to join the AI revolution

Although the AI timeline goes back to the 1940s, it’s only been recently that AI has advanced from narrow (weak) AI to the era of Artificial General Intelligence (AGI) – where the machine has the ability to understand or learn any intellectual task that a human is capable of learning. In recent years, we have seen the AI community develop an assortment of generalized solutions like Large Language Models (LLMs), Generative Adversarial Networks (GANs), etc. These advancements are providing telecom operators with the ability to respond to business requirements by creating limitless applications on top of AGI. Although training these generalized models is an expensive process that includes infrastructure, specialized human resources, and technology, using these models is relatively easy, and they have high adoption rates.

Today, the market offers numerous no-code platforms, making AI transformation easierthan ever before.Telecom operators thatwant to develop custom solutions for their business have AI no-code platforms available that can be used to either customize pre-built models that meet their business requirements or develop new ones through simple configuration. Many times, organizations that offer these platforms or solutions provide an integrated AI suite that allows CSPsto not only to create ML models but also to manage the complete life cycle of AI/ML models.

While there’s a new simplicity to AI, it is an art that needs to be mastered by carefully designing the right success metrics and coupling them with the right data that is saved at each point throughout the decision-making journey. The challenge most telecom operators face during this journey is not having the right process in place to store the data – which will be a key factor in determining the success of their AI transformation. To be successful, the start of the AI journey requires that CSPs carefully design data pipelines that are centered around the problem(s) they are trying to resolve. It is only after this step is complete that the CSP can begin its AI transformation.

Shashank Shekhar

About Shashank Shekhar

Shashank Shekhar is a data science leader with diverse experience in fields like telecom, CPG, retail, hi-tech and e-commerce. He is currently heading the Artificial Intelligence Labs at Subex. Shashank is the creator of augmented analytics product HyperSense AI, an AI orchestration platform to democratize AI. He has also created two open-source Python library used by more than 100 thousand data scientists worldwide. In the past, Shashank has worked at VMware, Amazon, Flipkart, and Target and has been involved in solving various complex business problems using Machine Learning and Deep Learning.

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