Personal is focusing its mobile access network automation efforts on two areas: the evolution toward an autonomous network and digital operations, meaning self-configurable operations. Through these two initiatives, the company has implemented around 69 automated solutions that, by May 2026, had enabled 400 RAN Sharing integrations, 4,200 diagnostics, 4,300 managed licenses and other impacts.
As part of Personal’s deployment activities, AI is being applied in five areas: 2G, 3G, 4G and 5G integration; mobile site acceptance; RAN Sharing; refarming and overlay; and Huawei license management.
As presented during the 2026 International Technology Seminar (Seminario Internacional de Tecnología, SIT), the operator created an AI agent specialized in mobile site acceptance that automates the review and validation of cellular network installations, a task traditionally performed by technical specialists. The system analyzes field evidence, verifies compliance with acceptance criteria and detects both physical issues and logical inconsistencies, integrating with the tools and suppliers involved in the process. The future roadmap for this specific development includes a regional and subregional management dashboard for the agent, as well as the incorporation of accuracy and efficiency metrics.
To address operation and maintenance tasks, Personal uses AI for site load balancing; mobile site self-diagnostics; cell and controller management; and structure validation through a combination of drones and multimodal AI.
Regarding the latter, the project is in a pilot phase in Mar del Plata, based on Gemini 2.5 Flash, and aims to automate inspections of poles, towers, monopoles and masts. While the traditional process required technicians to perform visual inspections at height, the new methodology uses drones to capture complete images of the structures, eliminating the need to perform a large portion of field tasks. Based on this material, AI analyzes the photographs, detects patterns and generates detailed reports for specialists.
The system can identify components, detect potential damage, classify anomalies and generate an executive summary of findings. Key benefits include reducing work at height, lowering personnel exposure to risky tasks, improving diagnostic accuracy and generating savings in both hiring and operating costs. The company estimates an analysis cost of US$0.03 per inspected pole -based on approximately 30 photographs- and between US$0.5 and US$3 per analyzed tower, based on around 400 images per structure. As a next step, the project will move forward with the analysis of results from self-supporting towers and monopoles.