The integration of artificial intelligence in manufacturing processes is gaining momentum, driven by the prospect of improved efficiency, precision, and adaptability. This paper presents the application of two AI-based tools developed within the AIDEAS project and piloted at D2Tech, a manufacturer of stone cutting machines. The first tool, Quality Assurance, employs unsupervised learning and machine vision techniques to detect defects in marble slabs by identifying anomalies from normal patterns. The second tool, Procurement Optimizer, focuses on optimizing the procurement of parts and components by cross-referencing supplier data against production schedules. Both tools are currently in the pilot testing phase, and while formal KPI evaluation is pending full deployment, this paper discusses the implementation approach, technical challenges, and expected benefits for each solution. The results contribute to the growing body of research on AI adoption in traditional manufacturing environments.