Generative AI in telecom: 5 use cases for operational performance and innovation

The telecom industry is at a pivotal moment, grappling with increasing demands for seamless connectivity, enhanced customer experiences, rising infrastructure and maintenance costs, cybersecurity threats, regulatory hurdles, and the pressing need for operational efficiency. 

In this challenging environment, generative AI is emerging as a key driver of innovation and performance improvement.  

According to a McKinsey study, many telecom leaders are already exploring generative AI, with initiatives ranging from pilot programs to full-scale deployments. Companies like AT&T, SK Telecom, and Vodafone have launched trials and committed to AI integration, with promising results. One European operator, for instance, boosted its marketing campaign conversion rates by 40% while reducing costs through AI-driven personalized content. 

In this article, we explore five of the most impactful generative AI use cases in the telecom sector today, highlighting additional examples from leading companies like Bouygues Telecom, Orange, and Proximus. 

1. The go-to case : customer service automation with AI-driven chatbots, virtual assistants and personalized support 

Generative AI is used to enhance customer service through AI-driven chatbots, virtual assistants, and personalized support. These systems can handle routine queries, troubleshoot issues, and provide instant responses, significantly reducing wait times and operational costs, and allowing human agents to focus on more complex tasks.  

Use Case: Proximus, a leading telecom operator in Belgium, is reported to have improved its customer satisfaction rates and a reduction in the workload of call centers by adopting AI-powered chatbots to handle thousands of customer interactions daily.

2. High-stakes use-case: network planning and maintenance with AI 

While many telecom operators have already seen success with customer service automation and marketing personalization, more transformative applications like network planning and optimization offer high-stake potential, such as improving network performance, reducing downtime, and enhancing user experience on a large scale. 

AI’s ability to analyze vast amounts of data in real time allows telecom companies to predict network failures and optimize performance. This capability enables operators to: 

• Forecast network traffic patterns 

• Allocate resources more efficiently 

• Identify and resolve bottlenecks before they impact service 

• Improve overall network quality and reliability 

Used properly, generative AI is therefore bringing a better service quality, less dropped calls, and improved overall user satisfaction. 

Remember strong data governance is a prerequisite to ensure data is accurate, secure, and compliant with regulatory standards.  

By implementing robust data management practices, Communication Service Providers (CSPs) can confidently use their data for transformative AI use cases, unlocking the full potential of AI while maintaining privacy and security standards. This will be critical as telcos continue to push the boundaries.  

3. Predictive maintenance: anticipating and addressing equipment issues 

Generative AI can be used to predict equipment failures and outages, leading to timely maintenance actions, minimizing downtime, reducing maintenance costs and enhancing service reliability, ensuring a seamless experience for their customers. 

Example: In France, Bouygues Telecom utilizes AI-driven predictive maintenance tools that monitor network conditions and signal anomalies. 

4. AI-Driven fraud detection: real-time protection at scale 

AI models can detect suspicious patterns and transactions in real-time, alerting operators to potential security breaches (for instance: SIM swapping, account takeovers), which helps protect both the company and its customers. 

Use case: Orange introduced an AI model that analyzes on average 100 million calls per day, but can peak at 160 million calls. “We can identify fraud patterns very rapidly. It not only increased the amount of fraud we were detecting” – up by 350%, according to the operator – “but has reduced fraud cost by €37 million”. 

5. Personalized marketing campaigns: boosting engagement with data-driven insights 

Generative AI analyzes customer data to generate tailored content and product recommendations with personalized marketing messages, social media posts, and targeted offers, improving engagement and conversion rates. 

Use Case : Proximus announced using AI to analyze customer behavior and preferences, creating targeted campaigns that speak directly to individual needs.  

Conclusion: The future of telecom is wired by AI – plan your next steps 

Generative AI is reshaping the telecom industry, offering solutions that address current challenges but also open new opportunities for growth and innovation.  

From customer service automation and predictive maintenance to network optimization and fraud detection, AI is driving improvements in operational performance, reducing costs, and enhancing customer experiences.  

Telcos like Orange, Bouygues Telecom, and Proximus have already demonstrated the transformative impact of AI, setting a clear path for others to follow. 

However, the journey toward fully leveraging AI’s potential requires a strategic approach, including :  

• robust data governance 

• a clear AI roadmap 

• the right set of expertise 

This is where Anais Digital and JEMS Group come in. With our deep experience in data, AI and digital transformation, we help telecom operators shortcut the complexities of AI adoption, ensuring they are well-equipped to thrive in a fast-evolving landscape. 

As AI continues to advance, the question isn’t whether to adopt it, but how to harness it effectively. By embracing AI now, telecom companies can not only stay competitive but lead the way in shaping the future of telecommunications.