This research paper examines the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in advancing manufacturing techniques within the context of Industry 5.0 in India. The study aims to identify the adoption patterns of AI and ML technologies across various manufacturing sectors and to explore the factors influencing their successful implementation. Employing a mixed-methods approach, data were collected through surveys targeting middle to senior management across major industrial hubs including Mumbai, Pune, and Bangalore. The primary data analysis utilized clustering analysis to segment firms based on their AI and ML adoption levels and regression analysis to determine the factors affecting the success of these technologies.
Key findings indicate distinct adoption patterns: Early Adopters, Mid-level Adopters, and Late Adopters, with varying degrees of integration and operational impact. Significant predictors of successful AI and ML implementations include budget allocation, technological infrastructure, and training and development. The results also reveal substantial regional disparities in adoption rates, with Bangalore leading in technology integration. Challenges identified include high initial costs, skill shortages, and technological complexity.
The implications of this study are significant for policymakers and business leaders, suggesting that strategic investments in infrastructure, education, and skill development are crucial for maximizing the potential of AI and ML in manufacturing. The findings provide a roadmap for enhancing technological integration that could lead to increased competitiveness and innovation in the Indian manufacturing sector.
