This study aims to assess the adoption of AI-driven smart inspection systems at H automotive manufacturing industry in Selangor. Furthermore, this research will also focus on those factors that influence the decision-making process regarding the integration of these AI technologies. As Malaysia moving towards to Industry 4.0, AI-driven inspection system has the potential to help the manufacturing company to improve the quality control, reduce defect rates, and improve the operational efficiency in automotive manufacturing. However, Malaysian company still faces several challenges, which including high implementation costs, of AI-driven smart inspection systems, organizational readiness, and lack of experienced workers. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, this research investigates the key variables such as effort expectancy, performance expectancy, social influence, facilitating conditions, organizational readiness, cost, and how these variables affect the adoption of AI-driven inspection systems. Data will be collected by distributing questionnaires to professionals at H automotive manufacturing company located in Selangor, which includes quality control managers, production engineers, and technology officers. This study will use quantitative research technique to investigate the relationships between the independent factors, the dependent variable and the adoption of AI-driven smart inspection devices. The findings of this study will contribute knowledge on AI adoption in the Malaysian automotive sector, providing important information for manufacturers, policymakers, and industry leaders. Moreover, by identifying challenges to AI adoption, this research aims to support Malaysia in advancing its automotive manufacturing industry toward full integration with Industry 4.0 technologies.
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