This podcast discusses the limitations of existing Computerized Maintenance Management Systems (CMMS) in handling unstructured data and their difficulties with information retrieval and predictive maintenance in a material production. To overcome these challenges, the text proposes a novel approach: integrating ontologies and Large Language Models (LLMs) into CMMS. Ontologies would structurally organize equipment and maintenance data, defining relationships and enabling semantic search. LLMs would then provide a natural language interface for querying the system, offer contextualized maintenance recommendations, and even facilitate real-time alerts based on IoT sensor data. This intelligent CMMS aims to improve maintenance efficiency, predict failures, reduce costs, and enhance overall operational productivity, marking a digital transformation in equipment upkeep. The document outlines the architecture, functionalities, and anticipated benefits of such a system, including reduced downtime and increased work efficiency.