AI and Big Data Practices in Developing Country Context: A Socio-Technical Perspective

Advanced analytics, involving AI and big data, has radically transformed how firms capture value through enhanced business operations and product offerings. Nevertheless, firms often find initiating AI and big data projects much more manageable than maintaining them at scale during the post-adoption stage. This situation could be much more pronounced for developing nations due to their less sophisticated computing infrastructure, poor data quality, and constrained budgets. This study employs a qualitative approach to gain an in-depth understanding of the factors hindering post-adoption usage and scaling of AI and big data artifacts in a developing country context. We specifically present insights from ten experienced managerial and technical executives working across multiple industries. Based on the well-established socio-technical framework, our findings reveal sixteen challenges across four dimensions: structure, people, technology, and task. These factors highlight complexities in AI and big data practices in developing countries. Our research offers significant theoretical and practical implications.