Barriers to AI Adoption in Sales: Challenges and Implications for Sales Professionals Using the Total Interpretive Structural Modelling (TISM) Approach

This study examined the barriers that exist in the adoption of AI among various industries’ sales processes. Using the Total Interpretive Structural Modelling (TISM) approach, eight key barriers to the process of AI sales integration have been identified as data quality problems, inefficiency in the management of data, lack of skill sets, reluctance to adopt AI, loss of human-driven interactions, customer discomfort, unclear benefits of AI, and lack of predictive systems. The study identified three major challenges to AI adoption in sales: insufficient AI and human skill sets, reluctance and fear of AI, and ineffective data management and silos. Major challenges highlighted were inadequacies in training, lack of trust in AI systems, and resistance to organisational change. These challenges are very interdependent, meaning that resolving one challenge could have several positive impacts on others. As such, the sales professional, the developer of the technology, and the policymaker have been given great significance from these results, with a notion that investments should be made regarding training, development of trust in AI, and support of organisational change. The study also emphasizes proactive practices by companies by setting up the required structure for integrating AI into it and fostering an open culture toward technologies of AI.