Implementing challenges of artificial intelligence: Evidence from public manufacturing sector of an emerging economy

The growing Artificial Intelligence (AI) age has been flooded with several innovations in algorithmic machine learning that may bring significant impacts to industries such as healthcare, agriculture, education, manufacturing, retail etc. But challenges such as data quality, privacy and lack of a skilled workforce limit the scope of AI implementation in emerging economies, particularly in the Public Manufacturing Sector (PMS). Therefore, to enhance the body of relevant literature, this study examines the existing challenges of AI implementation in PMS of India and explores the inter-relationships among them. The study has utilized the DEMATEL method for identification of the cause-and-effect group factors. The findings reveal that poor data quality, managers' lack of understanding of cognitive technologies, data privacy, problems in integrating cognitive projects and expensive technologies are the main challenges for AI implementation in PMS of India. Moreover, a model is proposed for industrial decision-makers and managers to take appropriate decisions to develop intelligent AI enabled systems for manufacturing organizations in emerging economies.