Zero Defect Manufacturing (ZDM), driven by Artificial Intelligence (AI), has the potential to transform industry in the digital age, utilizing data from various production stages to achieve zero defects and minimize waste, resulting in superior customer value. However, extensive empirical studies are required to validate its effectiveness across various production settings and enhance its implementation by improving technological components, seamlessly integrating with existing processes, and clearly defining human roles. The purpose of this paper is to investigate how to facilitate the adoption of AI-driven ZDM in production systems. This research highlights challenges and recommendations associated when implementing AI-driven ZDM within a production system and suggests a framework to facilitate this process.Using a longitudinal case study in the heavy-duty automotive industry, four AI-driven ZDM applications were examined to determine adoption dynamics. Leveraging the Technology-Organization-Environment (TOE) theory, we identified ten application challenges and made recommendations for how to address them. The analysis highlights the importance of adhering to agile principles with continuous adaptation during AI implementation, stressing the need for feedback collection from stakeholders to assess adoption factors. This study underscores the significance of explainable AI in empowering operators and emphasizes the necessity of human-centered design principles and feedback loops to ensure AI systems truly serve their users. Building on these findings, a framework is proposed aiming for incremental value creation, continuous learning, and adaptation while considering human feedback through various channels, similar to the Plan-Do-Check-Act cycle in lean management. This iterative approach, encompassing continuous improvement loops, aims to ensure successful adoption and maximize the potential of AI-driven ZDM in order to achieve operational excellence.This study contributes to the existing literature on AI adoption in production systems by offering a comprehensive understanding of the challenges and recommendations associated with implementing AI-driven ZDM. Additionally, the proposed framework provides a practical roadmap for organizations seeking to leverage the power of AI to achieve zero-defect manufacturing and enhance their competitive advantage.