The rise of AI in telecommunications, from optimizing Radio Access Networks to managing user experience, has sharply increased data volumes and training demands. Telecom data is often noisy, high-dimensional, costly to store, process, and label. Despite Ai's critical role, standard workflows still assume all training samples contribute equally. On the other hand, next generation systems require AI models that are accurate, efficient, and sustainable.The paper questions the assumptions of equal importance by focusing on applying and analyzing the roles of individual samples in telecom training and assessing whether the proposed model optimizes computation and energy use. we perform sample-level gradient analysis across epochs to identify patterns of influence and redundancy in model learning. Based on this, we propose a sample importance framework thats electively prioritizes impactful data and reduces computation without compromising accuracy. Experiments on three real-world telecom datasets show that our method [reserves performance while reducing data needs and computational overhead while advancing the goals of sustainable AI in telecommunications.

The core business of Mobile Network Operators (MNO) has moved from network management and phone services to service providing. In contrast to Information Communication Technology (ICT) service providers, MNOs handle large amounts of their customers’ context data and generated content, which can be used to bring value-added services to customers and therefore, generate solid revenues. Given this scenario, this paper describes how Telecom Italia (a major Italian MNO) has prototyped such type of services after a deep research performed in the context-awareness and context management field and using its user-generated content management facilities in federation with other platforms and systems.

Context Management technology is not novel itself, and ICT companies are already looking at this area and spending effort for a long time trying to find a technically feasible solution, appealing marketing usage and solve all the possible issues with its privacy and security concerns. However, after many years of technology scouting and academic scrutiny within this still innovating area, there is no unique best practice or reference standardization solving all the technological difficulties within this field. The context information available in the real world from many potential sources should be handled in a near real-time way, efficiently processed by many devices and be interoperable among different actors dealing with the context. Therefore not only a comprehensive context management framework shall be in the place but also efficient context representation formalism should be employed in order to represent the context data suitably for an autonomous Machine-to-Machine processing, with all the data maintained within that representation and with all the mechanisms or artifacts needed for a secure and privacy safeguarding sensitive data handling. This all compose a set of requirements to be respected in the context information data representation, which are listed and solved by the solution described within with paper.