BARRIERS TO ARTIFICIAL INTELLIGENCE ADOPTION IN RETAIL: AN INTEGRATED TISM APPROACH

Abstract

The retail sector is getting transformed through, artificial intelligence through data driven decision making, enhanced operational efficiency and customer experience. Despite huge potential, AI adoption in retail is uneven due to various interrelated barriers. This research aims to examine these barriers, and structure using Total Interpretive Structural Modelling. The barriers to AI adoption were first identified from literature followed by validation from the panel of experts. TISM was implemented, to establish hierarchical relationships among the barriers, and provide interpretive insights for these relationships. The results suggest that lack of top management support, and absence of a clear AI strategy are the most critical driving barriers, influencing technological, data-related, and organizational constraints, which lead to resistance to change, and misalignment of business goals. The findings offer a structured framework, contributing towards understanding AI adoption barriers and insights for practioners.