Integration of artificial intelligence (AI) into omnichannel operations has revamped operations, enhanced efficiency, improved sustainability and improved the customer experience. However, AI adoption is subject to various complexities and risks that can dilute operational performance and sustainable aspects. Therefore, this study aims to explore the multiple risks that hinder the adoption of AI in a sustainable, resilient omnichannel supply chain.
Based on the technology–organization–environment–societal theoretical background, the identified risks were categorized. The research methodology is carried out in three phases: first, the Delphi technique was used to verify the risks identified through a literature review. Second, grey relational analysis was done to prioritize the risks. Third, this research establishes a dyadic relationship among anticipated risks using the grey DEMATEL technique.
This research results indicate that inaccurate multichannel demand forecasting and operational cost overruns are the top causal risks. Misaligned performance metrics and loss of human oversight are the most influential risks.
To the best of the authors’ knowledge, this research is the first of its kind, and the results are highly useful for managers and practitioners to successfully integrate AI into the omnichannel supply chain, thereby enhancing sustainability and achieving net-zero goals.
