Generative AI in Telecom: 6 Promising Use Cases

10 min read
Generative AI in Telecom Use Cases
Generative AI in Telecom: Use Cases

Generative AI, with its remarkable capabilities, is the new star on the AI horizon. The anticipation of its growth is nothing short of substantial, and it's setting the telecom field abuzz. A recent survey of the industry’s leaders has revealed that the use of generative AI in telecom is expected to surge from 19% to a mind-blowing 48% within the next two years.

Now, what's all the buzz about? Is generative AI truly a revolutionary force within the telecom domain, or just another hyped-up topic that came from other sectors and is more suited to the glitzy world of marketing than the complex, infrastructure-dependent realm of telecom?

At Flyaps, we’ve been working closely with telcos for more than a decade. We helped such industry giants as Yaana Technology to improve their offerings with our cutting-edge solutions and dedicated assistance. We also have pretty impressive AI cases for various domains. Therefore, we're more than just enthusiasts; we're fervently committed to uncovering the real potential of generative AI in the telecom sector.

In this article, we will unravel the enigma of generative AI in telecom. We'll delve into its applications, use cases, and the transformative potential it holds. Are we on the brink of a telecom revolution, or is this buzz just another passing fad? Let's find out.

How generative AI in telecom works

Let's begin with the definition of generative AI. In a nutshell, this technology relies on deep learning algorithms to analyze extensive datasets and create fresh content by recognizing patterns in the data. Think of it as learning from a bunch of examples and then coming up with your own unique ideas.

To illustrate, suppose you have a large collection of photos. Generative AI can study these photos, figure out what they have in common, and then use this intel to generate new pictures that resemble the originals. But generative AI isn't limited to just images; it has a wide range of applications. It can craft stories, compose music, and even hold conversations like a human.

On the technical side, generative AI uses a system called artificial neural networks, which can be thought of as a vast network of interconnected nodes. Each node has a special weight that affects how the network operates. During the learning process, it adjusts these weights to improve its ability to recognize patterns.

How generative AI works
How generative AI works

Once it has learned enough, you can give it a starting point, often referred to as a random seed value. The AI then uses this seed to create a sequence of outputs based on the patterns it has learned. These outputs can be further refined to appear more natural and realistic, often through methods like smoothing and filtering.

What sets generative AI apart from other AI categories is its position within the broader field of machine learning. Generative AI doesn't exist as a separate entity but rather operates as a subset of AI, utilizing artificial neural networks to generate content.

Generative AI use cases in telecom

While this type of AI is still relatively new, industry giants like Vodafone, Verizon, AT&T and others have already embraced it and achieved remarkable results. Let's delve into the paths big hitters forged and the diverse range of areas they've successfully enhanced through the adoption of generative AI.

Generative AI use cases in telecom
Generative AI use cases in telecom

Behavior simulation

Generative AI in telecom can mimic user behavior, enabling telcos to forecast how consumers might react to new services, pricing models, or network modifications. This predictive capability results in informed decision-making and service optimization.

Ability to understand context and intent interpretation is at the core of behavior simulation. Ericsson uses these features in two key ways, one of which is the Intelligent assistant. This application features a chat-based conversational interface combined with a generative language model backend, initially using the GPT model developed by OpenAI. The purpose of this intelligent assistant is to provide valuable support across various departments, aiding employees in their day-to-day tasks.

Ericsson's generative AI functional architecture
Ericsson's generative AI functional architecture

The Intelligent assistant has a chat-based conversational interface, and depending on its design and capabilities, it could potentially incorporate behavior simulation. For example, it might simulate the behavior of a helpful human assistant by responding in a manner consistent with how a human would behave in various situations.

Content personalization

By analyzing trends, user preferences, and relevant data, generative AI for telecom can aid in crafting compelling and personalized content, allowing you to achieve more effective and engaging communication with customers, and increasing user satisfaction.

Vodafone is one of telcos harnessing generative AI for analyzing anonymized transcripts of customer calls to create more accurate and insightful summaries of these interactions, a task traditionally performed by contact center agents. This is achieved using what Vodafone's CEOr, Scott Petty, refers to as a "summarization engine," a software-driven solution that replaces the manual process of selecting predefined boxes to categorize customer issues.

By applying this technology, Vodafone aims to improve content personalization by better understanding customer concerns and interactions. The AI-driven summarization engine analyzes conversation data to provide more accurate insights, allowing Vodafone to craft content and responses tailored to individual customer needs. This not only leads to more effective communication but also enhances user satisfaction.

Additionally, Vodafone has embraced generative AI for software engineering, referred to as a "virtual assistant" for writing code. During trials with approximately 250 developers, this AI tool demonstrated a productivity gain of between 30% and 45%. By using this technology in this context, Vodafone aims to streamline coding tasks, improve code quality, and reduce the workload on software developers.

It's important to note that, in Vodafone's case, the use of generative AI for telecom is primarily internal and not directly exposed to customers. The focus is on improving internal operations and services rather than replacing customer service agents. The goal is to enhance the quality of service and potentially enable Vodafone to charge more for its services by offering a better-quality customer experience. Vodafone has procured generative AI tools from hyperscalers, specifically Google's Vertex AI and Microsoft-backed OpenAI, to support these internal initiatives.

Natural voice generation

Producing lifelike voices suitable for Interactive Voice Response (IVR) systems, virtual assistants, and voice-based services is becoming increasingly common among telcos. This enhances the quality of customer interactions, making them more natural and user-friendly.

Telefónica used generative AI in telecom for creating their AI assistant called Aura. This solution is designed to enhance customer service and help customers manage their digital lives.

Aura AI assistant
Aura AI assistant

Here's how it works:

  • Voice interaction: Aura allows customers to interact with the network in real time through multiple channels. Customers can engage in conversations with Aura to inquire about various aspects of their telecom services, such as their bills, contracted services, data usage, and other content.
  • Realistic voice: The use of generative AI in voice generation ensures that Aura's responses are lifelike and natural, creating a more engaging and interactive experience for customers.
  • Multichannel integration: Aura is available not only through a mobile application but also via third-party channels, including Facebook, Google, and Microsoft. This multichannel approach ensures that customers can access Aura conveniently through their preferred platforms.
  • Data storage and AI integration: Telefónica has invested significantly in developing smart platforms that securely store data and integrate it with artificial intelligence. This integration allows Aura to access and analyze customer data effectively, offering personalized experiences and responses.

Telefónica acknowledges that Aura is an evolving platform, and they have plans to expand its capabilities further.

Network optimization

Telecom companies can employ generative AI to optimize network performance, predict potential issues, and enhance resource allocation.

Network optimization is just one of the many business purposes for which AT&T has introduced Ask AT&T. This tool has democratized AI, making it accessible and applicable across the organization. It's designed as an intuitive, conversational platform that employees can interact with using natural language.  

Ask AT&T autonomously analyzes data, identifying fields, merging tables, and crafting the necessary code to extract valuable insights from the extensive data flows within their network.

This transformative functionality underscores the notion that human language is becoming the new SQL or Python, offering fresh opportunities to reimagine and enhance AT&T' business operations.

Predictive maintenance

Imagine a telecom giant that operates a vast network of cell towers. They utilize generative AI to predict maintenance needs. By analyzing historical data related to each tower's performance, the AI creates predictive models. These models take into account factors such as weather conditions, equipment age, and usage patterns. The AI can forecast when a specific tower's components, like antennas or power systems, are likely to experience issues or require maintenance. This proactive approach allows the telecom company to schedule maintenance during off-peak hours, minimizing disruptions to network services and ensuring consistent, high-quality connectivity.

Fraud detection

Consider a telecommunications service provider that uses generative AI to protect against fraudulent activities. The AI system analyzes vast amounts of data 24/7, including call records and online transactions. It learns normal customer behavior patterns and can detect anomalies or unusual activities. For example, if a customer's account shows a sudden surge in international calls or data usage outside their typical usage patterns, the AI raises a red flag. It can also identify patterns of SIM card swapping or suspicious login locations. By identifying these anomalies, the AI helps the telecom company swiftly detect and mitigate fraudulent activities, protecting both the company and its customers from financial losses and security breaches.

The sequential integration of generative AI in the telecom industry

The telecom industry is undergoing a significant transformation through the gradual integration of generative AI. Dr. Ishwar Parulkar, Chief Technology Officer for the Telco Industry at AWS, outlines this evolution, taking place in three distinct phases:

Three phases of generative AI in telecom integration
Three phases of generative AI in telecom integration

1. Customer experience enhancement

In the first phase, the focus is on enhancing customer experiences. AI-driven chatbots are at the forefront of this effort, offering efficient and personalized customer support. These chatbots handle inquiries, troubleshoot issues, and provide valuable information, ultimately elevating the overall customer experience.

An excellent example here is Orange, which leverages Google Cloud's generative AI solution to analyze call center data. The application transcribes phone conversations, summarizes customer-service interactions, and even suggests follow-up actions for agents based on these conversations. Google Cloud recognizes the transformative potential of this endeavor, leading to a remarkable improvement in both efficiency and overall quality.

IVR systems, powered by generative AI, are also playing a pivotal role in this phase. They intelligently route calls and provide automated assistance, leading to smoother and more efficient communication.

Additionally, real-time call analysis is employed to extract valuable insights, enabling telecom companies to better understand customer needs and sentiments, resulting in enhanced service quality

2. Fine-tuning models for telco purposes

The fine-tuned generative AI models are deployed to optimize network performance, ensuring seamless connectivity and delivering high-quality services to customers. Furthermore, AI models are a life-saver for detecting and preventing revenue leakage within telco operations. They play a crucial role in ensuring that billing and revenue processes function optimally, thereby minimizing financial losses.

For a better understanding, imagine a bustling metropolis where a major telecom provider sought to elevate the quality of their services. By harnessing generative AI models, the company embarked on an ambitious project to optimize network performance. They analyzed vast datasets containing network traffic patterns, identifying congestion points, and even predicting potential issues. The AI-driven solution they made seamlessly redistributed network resources, ensuring that high-density areas received the bandwidth they required, ultimately resulting in faster and more reliable internet connections for their customers. This fine-tuning of network performance became a game-changer, setting the company apart as a telecom leader known for delivering unparalleled connectivity.

3. Industry-specific models

In the final phase, industry-specific generative AI models come into play. Telcos harness these models, which are trained on telecom-specific data, to design and optimize network function software. This optimization has the potential to result in a more efficient and scalable network infrastructure. AI also assists in the design and configuration of telecom networks, streamlining the deployment of network resources and services.

Notably, the models are a key player in expediting the identification and resolution of network failures by analyzing historical data and suggesting corrective actions. This leads to improved network reliability and heightened customer satisfaction.

How Flyaps can assist you with generative AI in telecom implementation

With years of experience behind our backs, we at Flyaps have witnessed the telecom industry's incredible transformations and have helped companies embrace these changes. We understand the nuances, the challenges, and the aspirations of telcos like no other. This experience is our foundation, upon which we build solutions that perfectly align with the industry's needs. Let's delve into how we can leverage our expertise to propel your telecom business into the new, generative era of AI.

Generative AI consulting for telecom: Flyaps’ experts can analyze the specific needs and challenges of your business, guiding you and pinpointing areas where generative AI can make the greatest impact.

Tailored development of generative AI solutions: Given that generative AI in telecom is a cutting-edge technology, it requires a deep understanding of the nuances of AI in various applications. Our extensive experience, built over years of working with a wide range of AI technologies and crafting custom solutions, enables us to create solutions that perfectly align with your company's objectives and existing infrastructure.

Seamless integration with your current systems: Generative AI for telecom is not a standalone solution, and it needs to be carefully integrated into your existing systems and processes. Our team is adept at ensuring seamless integration, enabling generative AI applications to harmoniously operate alongside legacy systems and other software tools utilized by your telecommunications company.

Still unsure about whether generative AI is the right fit for your business? Let's discuss it. Contact us today, and we'll steer you in the right direction.