Artificial intelligence adoption for pharmaceutical salesforce performance : a case study in Kuwait

Purpose - The research aims to investigate the factors influencing the adoption of artificial intelligence (AI) in pharmaceutical industry companies, specifically focusing on its impact on salesforce performance. Design/methodology/approach - We collected research data from pharmaceutical industry companies, specifically the salesforce teams, using a valid and reliable questionnaire. Descriptive data analysis was used to summarize the participants' demographic characteristics, and inferential data analysis employed partial least squares structural equation modeling (PLS-SEM) to assess the reliability and validity of the measurement model and the relationships between variables. Findings - About 176 questionnaires were completed, and only 169 were deemed complete and used for data analysis. This study reveals a significant relationship between pharmaceutical salesforce performance and AI adoption (a path coefficient of 0.647, a p-value of 0.000 and an R2 of 0.584). Supervisory support and career development emerged as the essential variables of pharmaceutical salesforce performance, with the strongest relationship to AI adoption, as indicated by a path coefficient of 0.434 and a p-value of 0.000. Practical implications - Based on the results of this study, supervisory support drives AI adoption, underscoring the importance of leaders during technological transformations. Supervisors help minimize employees' opposition to change by providing concise direction, training and persuasion. By providing clear guidance, training and persuasion, supervisors decrease employee resistance to change. This supports community socioeconomic growth by enhancing patient care and access to healthcare, especially in underserved areas. Originality/value - This paper is the first to link salesforce performance and AI adoption in pharmaceutical companies.