The evolution of Generative Artificial Intelligence (Gen AI) and Digital Twins (DT) is reshaping processes across various industries. The speed and impact of this technology are unprecedented in industrial automation. Traditionally, DTs were applied to physical assets, integrating real-time data to enable monitoring and predictive optimization. However, inspired by advancements in Gen AI and DT research, this study explores the possibility of extending these concepts to autonomous entities in industrial maintenance. The core research question investigates how to construct an Autonomous Digital Twin for asset and maintenance management. This study builds upon previous research on intelligent dashboards powered by generative AI for CMMS software. The methodology involves a literature review followed by a conceptual modeling approach to integrate these technologies. The proposed framework demonstrates that combining predictive analytics with generative cognitive processing enhances decision-making, providing more precise and context-aware insights aligned with Industry 5.0 principles. The findings suggest a paradigm shift in which Digital Twins evolve beyond passive models into autonomous agents, driving higher operational intelligence and redefining human–machine interaction in maintenance management.