A development framework for human work integrated AI systems in manufacturing - Yamamoto, Yuji; Aranda-Muñoz, Álvaro; Sandström, Kristian (Frontiers in Manufacturing Technology, 2025)
Integrating AI-driven applications in manufacturing processes holds immense potential for enhancing production performance. However, adopting AI technology in manufacturing presents significant challenges, including the frequent need for human and AI work harmonisation and the knowledge gap among diverse stakeholders. This paper presents a development framework for human-AI systems that effectively integrates human work in manufacturing settings. The framework is specially designed for development project leaders from the manufacturing domain who play a critical role in integrating the diverse expertise required to realise such systems. The framework was derived based on a literature review, and its practical utility was examined through empirical applications. The resulting framework contains 147 task recommendations associated with six conceptual development progression stages. The empirical studies, including external reviews and preliminary empirical validation of the framework through pilot applications to two real-world projects, indicate the framework’s practical utility, such as facilitation of deep multi-domain dialogues for early phase project planning and mid-term reflective planning, as well as remarks, such as its use in iterative development environments. The present study extends the existing manufacturing research by offering deeper insights into and advancing a structured approach to developing human-integrated AI systems in industrial environments, which the study found to be immature.