Leveraging data for AI implementation requires complex managerial approaches and sophisticated technology arrangements that ideally bridge the gaps between the needs of different stakeholders and an organization’s strategic objectives. Although organizations believe that they are in the “perfect” position to do so, in practice this is not good enough. The tandem between the managerial approaches and the technology catalyzes changes in the way an organization operates and distributes its services, keeping digital innovation at the center of the operation. However, the understanding of how different groups of stakeholders actually promote, support and participate in such innovation opens up a new path for further investigation. This thesis contributes to a better understanding of how organizations leverage data and implement AI within specific industry domains, promoting and maintaining digital innovation. It sheds light on how organizations leverage data and support AI implementation from a theoretical perspective on business analytics and AI, as well as an empirical perspective with two case studies of Lufthansa Industry Solutions and ARUP, and pulls together both the systematic literature review method and qualitative analysis to problematize the current scholars’ conversation on these topics. This thesis adds important insights to the current academic conversation on leveraging data for AI implementation in today’s business landscape. The insights do not emphasize failed attempts, but rather highlight how specific organizations face unique challenges compared to well-accepted and continuously discussed practices while working with data and AI. In doing so, the thesis also provides multiple future research directions, finally highlighting important theoretical and practical implications for organizations and practitioners.