Bridging the Gap by Analyzing AI Deployment Challenges and Solutions in Manufacturing

Paper in proceeding, 2025

AI/ML technologies has transformative promise for manufacturing, however their adoption rates remain limited. This paper identifies key challenges that hinder AI/ML adoption in manufacturing environments through expert interviews. We used the Gioia method, which is a qualitative methodological framework, to develop a theoretical process model showing how these challenges sequentially and cyclically interact to influence AI/ML implementation effectiveness. As a result, we identified four key challenges such as infrastructure limitations, scalability issues, work-force skill gaps, and lack of AI/ML solution maintenance strategies, and proposed a set of solutions including practical approaches and strategies to address these challenges and support more effective deployment of AI/ML technologies in manufacturing. The contribution of this study lies in identifying a mismatch between traditional manufacturing practices characterized by long equipment lifecycles and isolated solutions and the dynamic requirements of AI implementation, emphasizing the necessity for continuous monitoring and the evolution of AI systems.