Modelling of critical success factors for procurement of AI systems: a study in the purview of the Indian public sector

Purpose This study aims to determine and prioritize the critical success factors (CSFs) that drive the procurement process of artificial intelligence (AI) systems in the public sector in the Indian context. It addresses the peculiar challenges and exigencies of procurement processes of AI systems in public sector organizations and develops a hierarchical model for guiding policymakers. Design/methodology/approach This study uses a research methodology that includes: identifying potential CSFs through literature review and expert consultations, developing a structural self-interaction matrix and performing cross-impact matrix multiplication applied to classification analysis for classifying CSFs based on driving and dependence powers. The study also uses interpretive structural modelling to establish relationships among these factors. Findings Fourteen CSFs are found in this study, which shape public procurement of AI. These influential factors cut across technical, organizational, regulatory as well as socio-economic aspects. This model is arranged hierarchically to indicate how these identified CSFs fit together thereby providing a roadmap towards successful procurement of AI systems. All other CSFs depend on or are influenced by stakeholders’ coordination and collaboration, which means that it is the most important one. Practical implications The results present a hierarchical model for navigating the intricacies of public sector AI procurement. Consequently, this model may support public organizations to enhance service delivery, optimize resource allocation and improve decision-making processes. It also addresses the need for an organized approach to the procurement of AI systems in developing countries such as India where extant guidelines are either insufficient or non-existent. Implementing these CSFs can result in more efficient, transparent and accountable procurement practices of AI systems, ultimately impacting positively on public service outcomes. Social implications The hierarchical model and classification of CSFs provide an alternative view regarding what entails successful AI systems’ acquisition stressing out particular requirements and obstacles associated with it within developing countries’ public sector agencies. It is in line with achieving sustainable development goals (SDGs) 9 which is industry, innovation and infrastructure and SDG 12 which is responsible consumption and production. Originality/value This study provides a new perspective by developing a comprehensive structured framework for the procurement of AI systems in the public sector. It fills gaps existing in research by offering a structural model on how to increase organization efficiency through the procurement of AI systems. The hierarchical model and classification of CSFs provide an alternative view regarding what entails successful AI acquisition, stressing particular requirements and obstacles associated with it within developing countries’ public sector agencies.