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As manufacturing transitions from Industry 4.0 to Industry 5.0, a critical challenge emerges in integrating Generative Artificial Intelligence (GAI) into adaptive social manufacturing to achieve sustainability goals. This transition reflects a paradigmatic shift from a technology-centric model focused on automation and efficiency toward a more holistic framework that embeds human-centricity and environmental responsibility into industrial systems. Whereas Industry 4.0 emphasizes digital innovation and productivity, Industry 5.0 seeks to align technological advancement with broader ecological and societal objectives. Despite advancements in automation and digitalization, existing frameworks lack a structured approach to leveraging GAI for environmental, social, and economic sustainability. This study explores the transformative role of GAI in adaptive social manufacturing, addressing the gap in the existing frameworks. Employing a multi-method research design, including content analysis, expert-driven validation, and system dynamics modeling, the study identifies nine key sustainability dimensions of Industry 5.0 and maps them to 17 GAI functions. The findings reveal that GAI significantly enhances adaptive social manufacturing by optimizing resource efficiency, promoting inclusivity, and supporting ethical governance. System dynamics analysis highlights the complex interdependencies between GAI-driven functions and sustainability outcomes, underscoring the need to balance technological innovation with human values. The research provides a novel framework for industries seeking to implement GAI in sustainable production systems, bridging theoretical insights with practical applications. Additionally, it offers actionable strategies to address challenges such as workforce adaptation, ethical AI governance, and adoption barriers, ultimately facilitating the transition toward Industry 5.0’s sustainability goals.
Amirkhizi et al. (Sat,) studied this question.