How Artificial Intelligence Transforms the Multinational Enterprise: Theory-Based Predictions and Research Directions

Artificial Intelligence (AI) is reshaping how multinational enterprises (MNEs) strategize, operate, and coordinate across borders. IB scholarship has yet to systematically explain how these transformations differ from those generated by prior technologies or what is distinctively at stake for MNEs. In this Perspective, we develop an analytical lens grounded in three properties of contemporary AI— self-updating, generativity, and agentic autonomy —to derive theory-based predictions about transformations in MNE organization, governance, and decision-making. We identify three transformations. The first is institutional misalignment , in which self-updating progressively privileges dominant institutional contexts over peripheral ones, widening capability and performance hierarchies across the MNE network. The second is cultural contingency , in which generativity embeds institutional duality in algorithmic design rather than local managerial adaptation, creating legitimacy challenges that cannot be fully resolved after deployment. The third is an accountability gap , in which agentic autonomy separates formal responsibility from actual decision influence, creating forms of distributed and uneven accountability. These transformations are not generic organizational phenomena. They arise from multinationality itself and expose important limitations in existing IB theories of knowledge transfer, institutional duality, organizational learning, and governance. Four research directions synthesize them into broader theoretical challenges for IB scholarship, suggesting that AI shifts the locus of multinational management from managerial adaptation toward the design and governance of organizational and algorithmic architectures.