Shipbuilders and In-Service Support (ISS) contract providers worldwide are grappling with formidable challenges in the maintenance, refit, repair, and training demands of both new and aging ships. These challenges stem from a multitude of factors, including skill shortages, logistical complexities, evolving technologies, and the intricate nature of ship maintenance. In the maritime sector, compounded by a shortage of skilled service technicians. 

To address these challenges and embark on a journey of digital transformation in maintenance and repair processes, we explore the pivotal role of data interconnectivity and artificial intelligence (AI). By leveraging machine learning models for searching technical information, visualizing work locations, and streamlining maintenance tasks; we demonstrate a new vision for automation and collaboration in maritime work. 

Our paper delves into the complex landscape of technical datasets and tasks, emphasizing the necessity of AI for intelligent data retrieval and visualization. It elucidates the fragmented and intricate nature of tasks and documentation, underscoring the critical role of AI systems in empowering users to access information related to ocean assets and tasks while also providing an insightful visualization of task locations and data segmentation. 

Through this technical paper, we aim to shed light on how the interconnectivity of data and AI-driven solutions can revolutionize shipbuilding and ISS. By bridging the gap between training, maintenance, and real-time operations, we explore the potential for enhanced efficiency, reduced costs, and improved customer service. The paper underscores the urgency of embracing data-driven digitization to overcome industry challenges and offers solutions for a more streamlined, interconnected, and efficient maritime sector.