Navigating the Adoption of AI Technologies in Manufacturing Industries: Insights, Impacts, and Strategic Imperatives

Artificial Intelligence (AI) is changing the manufacturing process speedily; however, there is little empirical data on the adoption rates, technology emphasis, and performance results achieved, especially in developing economies. The study of AI adoption trends in developing economies is critical because these countries have special infrastructural and ability limitations that determine technology adoption and performance. This study fills this gap by surveying 209 manufacturing companies in India and examines the relationships between AI technologies, their areas of application, and multidimensional performance outcomes using descriptive, inferential, and multivariate analysis. Results indicate that robotics, IoT, computer vision, and machine learning are the most commonly utilized technologies, with the majority of their applications being in production, predictive maintenance, supply chain, and design activities. Firms with large size are more likely to adopt, but the size of a firm is not the decisive factor that determines performance improvement. Instead, market dynamics in an industry are more influential, particularly time-to-market, innovation, and responsiveness to customers. The results are relevant because they provide empirical associations between adoption tendency and technology orientation with quantifiable performance gains, which support the objective of the study to advise AI-context-dependent strategies in manufacturing. The novelty of the paper is not only an integrated empirical framework of the tools-applications–multidimensional outcomes but also a change of debate on adoption to an industry-centered view of adoption as opposed to size-centered view. The paper contains delicate organizational guidelines and precise actions concerning the spread of inclusive AI in emerging economies.