This research aims to design and present a comprehensive policy-making model for the process of AI-driven production development. Adopting a mixed-methods approach, the study incorporates documentary research, content analysis, and semi-structured interviews with experts across the industrial, technological, and educational sectors. Initially, the theoretical and empirical foundations of AI in production, policy-making models, and the significance of education in technological transitions are examined; subsequently, by identifying existing challenges and opportunities, a conceptual framework is formulated, and its operational components are detailed with a focus on the role of training. The findings are expected to demonstrate that the successful implementation of AI in manufacturing industries necessitates an integrated policy approach that prioritizes education for a wide range of personnel, from managers to production-line workers, to foster skills in smart systems, data analysis, critical thinking, and problem-solving. Furthermore, the study indicates that investment in education not only enhances productivity and quality but also facilitates innovation and the creation of a sustainable competitive advantage. By highlighting the pivotal role of education throughout all stages of AI-based production development policy-making, the proposed model offers a comprehensive and flexible framework for practitioners and scholars alike.