As one of the most significant innovations of recent years, AI 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 leadership role in this technological transformation. Laurent Leboucher, Orange Group Chief Technology Officer, helps us take stock.
As the need for connectivity and value-based services increases, our telecoms networks become all the more important to support them. It’s also where we’re making the greatest gains in performance and innovation, with AI playing a fundamental role in our ability to maintain an excellent and scalable service.
We also use AI in our network planning and investment choices, automating operational tasks, and making our ways of working more efficient. It’s all part of a virtuous circle where AI drives networks and networks enhance AI.
Laurent Leboucher,
Orange Group Chief Technology Officer
How AI improves quality of service and operational efficiency
Our systems include real-time data processing technologies and machine learning solutions.
We’ve been analyzing large amounts of data from network equipment for many years, which has enabled us to deploy innovative use cases, such as the immediate identification of voice call quality issues. Traditional AI based on Machine Learning 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 enable us to locate problems in minutes, which improves operational efficiency, and the quality of service delivered to customers
Beyond this example, AI used in our network operations can help us optimize investment choices and improve efficiency and resilience. Specific applications include automating network supervision, real-time monitoring, predictive maintenance, anomaly detection, and decreasing energy consumption.
Traditional AI also improves the efficiency of our technicians in the field. They use image recognition to check the quality of network maintenance, taking photos before and after any work to allow real-time verification and immediate correction if necessary.
The latest generation of GenAI offers unprecedented possibilities to summarize trouble tickets, saving our technicians time, and the first tests are already showing promising results.
We can already see how GenAI is leading to greater operational efficiency. We’re also testing use cases to generate relevant queries to analyze the real-time network status and suggest operational improvements to our teams.
GenAI offers new and impressively powerful possibilities
Before fully integrating GenAI into our operations, we’re working on controlling any risks and eliminating any possible hallucinations that it could theoretically cause. We’re also investigating the added value of use cases, as well as their financial and energy impacts, before deploying at scale.
Using AI in our networks translates into a better customer experience, especially in terms of 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. Energy consumption can be reduced during periods of low traffic and certain frequencies can be switched off when not needed. The goal is to find the best balance between reducing energy consumption and maintaining a high-quality user experience.
We are working with our industrial partners to develop sophisticated mechanisms that strike the right balance between reducing energy consumption and maintaining a high-quality user experience
