Implementing challenges of artificial intelligence: Evidence from public manufacturing sector of an emerging economy |
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| Institution: | 1. Guildhall School of Business and Law, London Metropolitan University, London, UK;2. Ch. Ranbir Singh State Institute of Engineering & Technology (CRSSIET), Jhajjar 124103, Haryana, India;3. Operations and Supply Chain Management Lab, School of Management, Doon University, INDIA;4. Supply Chain and Business Analytics, Guildhall School of Business and Law, London Metropolitan University, UK;5. Faculty of Engineering and Information Technology, University of Technology Sydney, AUSTRALIA;1. Associate Professor and Director, School of Planning, Public Policy, and Management, University of Oregon, 263 Hendricks Hall, 1209 University of Oregon, Eugene, OR 97403, United States of America;2. Associate Professor, School of Urban Affairs, Cleveland State University, Cleveland, OH 44115, United States of America;1. Department of Organization, University of Zagreb, Faculty of Organization and Informatics Vara?din, Pavlinska 2, 42 000 Vara?din, Croatia;2. College of Arts and Sciences, Carlow University, 3333 Fifth Avenue, Pittsburgh, PA 15213, United States;1. Departamento de Computação, Universidade Federal Rural de Pernambuco, Recife, Brazil;2. Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil;1. Mid Sweden University, Faculty of Science, Technology and Media, Department of Information systems and Technology, Forum for Digitalization, Holmgatan 10, Sundsvall 851 70, Sweden.;2. University of South Africa, Department of Information Science, Preller Street, Muckleneuk Ridge, Pretoria.;1. Party School of the Chengdu Committee of the Chinese Communist Party, Chengdu 610110, China;2. School of Management, Fudan University, Shanghai 200433, China;3. Business School, University of Nottingham Ningbo, Ningbo 315100, China |
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| Abstract: | 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. |
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