In recent years, the telecommunications industry has been at the forefront of adopting cutting-edge technologies to enhance operational efficiency, improve customer service, and innovate in service offerings. Now, the industry is undergoing a profound transformation.
One of the most transformative technologies driving this evolution is Generative Artificial Intelligence (Gen AI). Gen AI represents a significant leap from traditional AI systems, as it aims to replicate human-like cognitive abilities, including reasoning, learning, and problem-solving.
Gen AI thus is a powerful subfield of AI that is totally revolutionizing how telecom operators manage networks, personalize customer experiences, and optimize operations. The adoption of Gen AI in the telecom industry is still in its early stages, but the potential benefits are undeniable. As Gen AI technology continues to evolve and mature, we can expect to see even more innovative use cases emerge.
In this 2-part series of articles, I explore the profound impact of Gen AI on the telecom industry, examining its applications, benefits, and future prospects.
Overview of AI in Telecom
Gen AI is a category of AI technologies that seeks to emulate human cognitive abilities across a broad range of tasks. Unlike narrow AI, which is designed for specific tasks like speech recognition or image classification, Gen AI aims to generalize and adapt to various scenarios without explicit programming for each task.
As data consumption skyrockets and competition intensifies, telecom operators are constantly seeking new ways to optimize networks, enhance customer experiences, and drive business growth. Gen AI, a technology capable of creating entirely new data or content, is emerging as a game-changer in this evolving landscape.
In the telecom sector, Gen AI holds immense potential to revolutionize operations, customer interactions, network management, and more. Telecom operators are leveraging Gen AI technologies to streamline processes, personalize customer experiences, predict network failures, optimize resource allocation, and innovate new services.
Gen AI use cases already deployed by global telcos
Gen AI learns the underlying patterns and relationships within datasets and utilizes this knowledge to create novel content. This opens up a plethora of opportunities for the telecom industry. A few od these use cases (with deployment examples) are discussed below:
- Customer Service and Chatbots
- Network Optimization and Predictive Maintenance
- Personalized Marketing and Recommendation
- Fraud Detection and Security
- IoT and Smart Connectivity
Customer service and chatbots
One of the most visible applications of Gen AI in telecom is through advanced customer service chatbots. These bots use natural language processing (NLP) and machine learning to understand and respond to customer queries in real-time, 24/7.
Gen AI powers chatbots that understand natural language, allowing them to answer questions, offer solutions, and even generate personalized recommendations. This streamlines customer interactions, reduces wait times, and personalizes the experience for improved satisfaction. Currently AI assistants can handle basic inquiries and proactively suggest solutions, freeing up human agents for complex issues.
- Example: Verizon (USA)
Verizon is integrating Gen AI into its customer service strategy. During customer service, Verizon prioritizes human interaction, however, Gen AI plays a supporting role. AI-powered chatbots can handle basic inquiries, answer frequently asked questions, and troubleshoot common issues. This frees up human agents for more complex situations and personalized assistance. Verizon’s focus is on using Gen AI to streamline the customer experience while maintaining a human touch. Verizon has been developing – and deploying – industry-leading, human-assisted Gen AI applications to simplify experiences and help make every interaction a positive one.
Furthermore, using Gen AI, the company was able to stop about 100,000 customers from leaving its service (in 2023) by predicting why a customer is calling, connecting them with a suitable agent and reducing store visit time. The company receives around 170 million calls every year and with Gen AI it can now determine 80% of the time why a customer is calling.
Using a tool called Personal Shopper/Problem Solver that works alongside employees for customers, Verizon is using AI to instantly analyze a customer’s profile and help employees get a head start on who the customer is and why they may be calling. This allows them to provide answers, offers, experiences and products that speak to the customers’ needs with ease, accuracy, and efficiency. With the personal shopper and problem solver working in the background, Verizon has already halved customer transaction time to four minutes.
AI is helping Verizon treat each customer in a highly personalized manner by providing unique offers and products that are tailored exclusively for them – a tool it calls “segment of me.” From new plans, product offers, service upgrades and more, AI is accurately and proactively identifying what a customer may be looking for and enabling agile, consistent experiences no matter where they shop. With this type of proactive work, Verizon has increased the engagement with its customers and lowered churn.
Network optimization and predictive maintenance
Network Optimization: Gen AI analyzes massive amounts of real-time traffic data. By identifying usage patterns and potential bottlenecks, it can dynamically allocate resources. This ensures efficient bandwidth distribution, preventing slowdowns and buffering for a seamless user experience.
Self-Optimizing Networks: A sub-set of this that Gen AI enables is to help create networks that autonomously adjust configurations based on real-time data, without human intervention. This significantly enhances network efficiency and adaptability.
Hyper-Targeted Resource Allocation: Another sub-set of network optimization that Gen AI enables is to create detailed user profiles, allowing for personalized bandwidth allocation based on individual usage patterns. This would optimize network resources and ensure a smooth experience for everyone.
Predictive Maintenance: Gen AI analyzes network equipment data, identifying subtle anomalies that might indicate future failures. This allows for proactive maintenance, replacing or repairing equipment before it disrupts service. This proactive approach minimizes downtime, ensuring network reliability and avoiding revenue losses from frustrated customers.
- Example: China
China Mobile has developed in in-house Gen AI model called Jiutian. This tool stands out for its ability to optimize network performance and predict potential issues. Jiutian has been trained on over 2 trillion tokens and incorporates expertise in eight critical industries, including telecommunications. This industry-specific knowledge positions Jiutian as a powerful tool specifically suited for China Mobile’s needs.
This tool is part of China Mobile’s Mid-End Platform with an AaaS (Ability as a Service) system. This platform has supported the operator’s digital-intelligent transformation and the telco has built co-creation and win-win eco-system surrounding AaaS on its smart Mid-End Platform.
Towards this, the telcos combined premium digital applications such as its Yunshang Yidong (mobile cloud), Wutong Yinfeng (big data platform) and Jiutian Lanyue (AI platform).
By the end of 2021, its smart Mid-End Platform ability service system offered a catalogue of 325 common capabilities, processing over 8.1 billion requests per month on average.
The Jiutian model has been working well with respect to network optimization, with a focus on the following:
Predictive Maintenance: Jiutian analyzes vast amounts of network data, identifying subtle anomalies that might indicate future equipment failures. This allows China Mobile to schedule maintenance proactively, minimizing downtime and ensuring uninterrupted services for customers.
Dynamic Resource Allocation: By analyzing real-time traffic patterns, Jiutian can dynamically allocate bandwidth across different regions. For example, if there is surge in traffic during a major sporting event, it can identify this and ensure that bandwidth is efficiently distributed, preventing congestion and buffering.
Taken from: https://blog.huawei.com/en/post/2024/7/9/generative-ai-reshaping-telecoms-landscape