How Gen AI will revolutionize telecom network operations

The launch of ChatGPT in late 2022 propelled Generative AI to prominence, gaining significant visibility and popularity among both consumers and organizations. Telecom operators have been quick to experiment with this technology, exploring applications that would boost individual productivity and optimize industry-specific processes, like client interactions

Most recently we have seen a significant interest in applying Generative AI to Network Operations, which is an area in which CSPs (Communications Service Providers) have long sought efficiency gains to enable the management of an increasingly complex technology landscape. In this article, we explore how operators can leverage Gen AI to augment the employee experience and automate business processes, all in the name of smarter, faster, more resilient networks.

Network knowledge at your fingertips

Generative AI is a gamechanger when it comes to intelligent document querying and information retrieval. The retrieval augmented generation (RAG) pattern allows the interrogation of text indexes via a semantic representation of a query and has quickly become a standard. Based on retrieved sections of selected documents, Generative AI can easily produce summaries compliant with pre-defined templates.

This application is demonstrated with one of our clients, who is now providing network technicians with a tool that enables them to quickly access a summary of past incidents that have affected a specific node on the broadband access network. Thanks to Generative AI, the new tool can retrieve all incidents relevant to the current investigation and produce a formatted summary of past events – reducing the time for this task.

CSPs are also investigating the use of Generative AI for managing contracts, such as interconnect and roaming contracts, or cell tower lease agreements. Through the help of Gen AI-enabled tools, users can quickly search for specific clauses or ask direct questions, such as “What are the security procedures for accessing this site?” or “What is the average price for site rentals in Madrid?” This removes the need for extensive, complex searches for the most up-to-date and relevant contracts and amendments. It also reduces the risk of error, especially when navigating large, complex document repositories that can span several years and many geographies.

The rise of conversational interfaces

For the past 60 years, most traditional applications have been using either command-line or graphical, menu-based interfaces. While regular users have mastered these tools, occasional users struggle. This is why some customers still prefer calling the contact center instead of using a mobile app!

Generative AI gives a new dimension to user interfaces, moving from predefined, rigid dialogs, to more free-flowing and intuitive conversations. This is relevant for some of the interfaces used when operating networks, where the user experience can be much improved.

For instance, we are currently defining a proof-of-concept with one of our CSP clients to support field technicians who perform interventions at customer homes and/or network points of interest. These technicians frequently need specialized help from team leads or colleagues while in the field; if this help cannot be provided on demand, then the service agent may need to schedule a follow up intervention.

Generative AI allows the development of a conversational bot that enables the technician to get the information they need about the specific location/services and technologies that will enable them to successfully troubleshoot or deploy services. A voice bot or a chat bot makes the job of the technician easier and quicker, allowing for faster issue resolution and avoiding repeat and costly truck rolls.

These service tools can also be combined with augmented reality (AR) applications, such as using a mobile phone to scan physical devices and generate relevant information. A variety of new interfaces that enable this type of support is made possible by the emergence of multimodal models such as Google’s Gemini, OpenAI’s GPT-4 and Mistral AI’s Mistral 7B.

An additional application focuses on network configuration. Specific intents, like boosting radio capacity at a stadium ahead of a major event, are set up through dedicated interfaces that require expertise on network management applications. By employing a conversational interface, network engineers can effortlessly grasp available capacity and make configurations for upcoming activities through friendly conversations with an agent. This conversational agent doubles as an advisor, drawing insights from historical activities and the present network status.

Autonomous Networks monitoring with a human touch

Moving to a higher level of network automation is critical for network operators. In fact, this is the key to improving service quality within a more complex technological environment without adding additional staff. AI is key to moving from human-managed networks, supported by insights from data, to AI-managed networks. Generative AI can thus complement other AI models, such as anomaly detection and classification.

While the industry has settled on the term “Autonomous Networks,” the goal is not complete autonomy. Configuration of intent is essential, and ongoing network monitoring for compliance is crucial. Even with a significant level of automation, human oversight remains imperative to ensure safety and maintain the quality of service.

Generative AI can produce a human readable summary of the status and activity in the network, allowing human agents to understand if and how the intent is satisfied. Even when operating networks with a high level of automation, human agents must be able to investigate, ask specific questions and get replies.

In the same way, an Autonomous Network’s reaction to an alarm or an anomaly must be defined in advance by a network engineer. On older generations of solutions, scripts must be developed and tested, which requires strong expertise. With Generative AI, natural language could be used to define responses to alarms, going as far as to extract appropriate remediation procedures from process documents. This approach allows human experts to review and make necessary adjustments.

Leading the way in an AI revolution

Like traditional AI, there are many use cases for Generative AI in the Telecom industry. In addition to individual agent productivity tools, Generative AI can be used to refine and streamline operational processes to better manage networks.

At Capgemini, we are now experimenting with leading CSPs on how to augment or automate existing workflows through AI technologies, helping them create a smarter, faster, more resilient network.

TelcoInsights is a series of posts about the latest trends and opportunities in the telecommunications industry – powered by a community of global industry experts and thought leaders.