From Challenges to Success: Navigating AI Adoption in Multinational Settings A Qualitative Multiple Case Study on the AI Adoption Process within MNEs

Abstract

While the strategic relevance of artificial intelligence (AI) is widely acknowledged, limited insight exists into how adoption is coordinated across organizational layers in multinational enterprises (MNEs). Although technology adoption is a well-established field, few frameworks account for AI's disruptive nature and the complexity of MNE structures. This study addresses this gap by exploring how MNEs adopt AI, focusing on key phases, challenges, and strategic considerations. A qualitative multiple-case study design was applied, drawing on 18 semi-structured interviews with AI experts, managers, and specialists across eleven MNEs. The analysis was guided by a conceptual framework combining AI readiness, adoption phases, and relational and institutional perspectives. Findings show that AI adoption is best understood as a cyclical process, comprising Initiation, Adoption Decision, and Adoption, shaped by organizational maturity, and internal and external dynamics. Trust, leadership, governance, and cross-functional collaboration emerge as key enablers, while cultural caution presents hidden barriers. The study illustrates how AI readiness builds over time, influencing the speed and scope of future adoption. Overall, successful AI adoption depends not only on strategy and resources but also on the ability to navigate institutional conditions and organizational tensions, offering insights into managing AI as a dynamic organizational capability.