Artificial Intelligence in Manufacturing: A Systematic Literature Review of Applications, Adoption Challenges, and Performance Outcomes

Abstract

Artificial intelligence (AI) is rapidly transforming manufacturing by enhancing process intelligence, operational efficiency, product quality, and supply chain responsiveness. Despite growing scholarly attention, the existing review literature remains fragmented, with most studies focusing either on AI applications or performance outcomes, while giving limited attention to the adoption challenges that shape implementation success. This gap is critical because manufacturing firms need a more integrated understanding of what AI can do, what barriers may arise during adoption, and how these factors influence actual performance outcomes. Addressing this need, the present study conducts a systematic literature review of 34 peer-reviewed articles published between 2019 and 2024 to examine AI applications, adoption challenges, and performance outcomes in manufacturing. Using a combined qualitative thematic synthesis and descriptive statistical analysis, the review identifies predictive maintenance, automated quality control, inventory optimization, process bottleneck detection, and predictive logistics as the most prominent AI applications. The findings show that AI generally improves efficiency, productivity, quality, and responsiveness; however, these benefits are uneven across sectors, firm sizes, and implementation contexts. Major adoption challenges include high investment costs, data quality issues, system integration difficulties, skills shortages, and organizational resistance, with SMEs facing particularly acute constraints. The review highlights that the value of AI in manufacturing depends not only on technological capability, but also on organizational readiness, governance, and strategic alignment. By integrating applications, adoption challenges, and performance outcomes within a single review, this study offers a more comprehensive evidence base for researchers and provides practical guidance for firms seeking to make informed AI adoption decisions.