Published on November 25, 2024
AI, one of the most significant innovations of recent years, is becoming ubiquitous and GenAI is already having a major influence on many companies, including telecom operators. With the exponential increase in digital uses and connectivity, AI is proving to be a powerful asset and marks an opportunity for Orang to take a leading role in this technological transformation. Laurent Leboucher, Technical Director of the Orange Group, helps us take stock.
As the need for connectivity and value-based services increases, our telecommunications networks become even more important to support them. This is also where we make the biggest gains in performance and innovation, with AI playing a fundamental role in our ability to maintain excellent and scalable service.
We also use AI in our network planning and investment choices, automating operational tasks and making the way we work more efficiently. This is all part of a virtuous cycle in which AI boosts networks and networks enhance AI.
Laurent Leboucher,
Technical Director of the Orange Group
How AI improves quality of service and operational efficiency
Our systems include real-time data processing technologies and machine learning solutions.
We have been analyzing large amounts of data from network equipment for many years, which has allowed us to deploy innovative use cases, such as immediately identifying voice call quality issues. Traditional machine learning-based AI can automatically process large amounts of data and alerts to quickly detect the root causes of problems, reducing our reaction time and improving the call experience.
Machine learning techniques allow us to locate issues within minutes, improving operational efficiency and the quality of service provided to customers.
Beyond this example, AI used in our network operations can help us optimize our investment choices and improve efficiency and resilience.. Specific applications include automating network monitoring, real-time monitoring, predictive maintenance, anomaly detection and reducing energy consumption.
Traditional AI also improves the efficiency of our field technicians. They use image recognition to check the quality of network maintenance, taking photos before and after any work to enable real-time verification and immediate correction if necessary.
The latest generation of GenAI offers unprecedented capabilities for summarizing trouble tickets, saving our technicians time, and initial tests are already showing promising results.
We can already see how GenAI leads to greater operational efficiency. We also test use cases to generate relevant queries to analyze the state of the network in real time and suggest operational improvements to our teams.
GenAI offers incredibly powerful new possibilities
Before fully integrating GenAI into our operations, we are working to control risks and eliminate possible hallucinations that it could theoretically cause. We also study the added value of use cases, as well as their financial and energy impacts, before deploying them on a large scale.
The use of AI in our networks results in a better customer experience, including service reliability, reduced downtime, optimized performance and more efficient customer support. Our enterprise customers benefit from more robust and secure networks tailored to their specific needs, including their own AI tools.
How AI optimizes energy efficiency
AI also plays a key role in making mobile network infrastructure more energy efficient. by adapting the consumption of equipment and antennas according to actual uses. Power consumption can be reduced during periods of low traffic and certain frequencies can be turned off when not needed. The goal is to find the best balance between reducing energy consumption and maintaining a high-quality user experience.
We work with our industry partners to develop sophisticated mechanisms that strike the right balance between reducing energy consumption and maintaining a high-quality user experience.
