More Knowledge, Less Difference: Generative AI and the Distinctiveness of Subsidiary Knowledge

Jiawei Song ()
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Jiawei Song: Graduate School of Economics, The University of Osaka.

No 26-13, Discussion Papers in Economics and Business from Osaka University, Graduate School of Economics

Abstract: Multinational enterprises (MNEs) are increasingly providing the same generative AI tools to employees across their subsidiaries. Research on AI in international business has emphasized how AI transforms the way MNEs learn and decide, while research on AI and homogenization has shown that AI-assisted outputs tend to become more alike. Neither has examined how individual employees’ use of AI in subsidiaries affects the knowledge that reaches headquarters. I argue that generative AI creates a paradox for MNEs: by lowering writing and language barriers, it increases the amount of knowledge subsidiaries send to headquarters, while potentially reducing how distinctive that knowledge is. I identify four points at which AI enters the production of subsidiary knowledge (gathering, interpreting, writing up, and receiving) and develop propositions on how AI use at these points affects distinctiveness, which employees are most affected, and how subsidiaries’ role in the MNE may change over time. I also propose a competing outcome: AI can increase distinctiveness when it enables employees previously limited by the corporate language to share their own observations. The decisive question is whether AI replaces the first-hand work through which local knowledge is produced or carries knowledge employees already hold. The paper contributes to research on reverse knowledge transfer, AI in international business, and AIdriven homogenization.

Keywords: generative AI; reverse knowledge transfer; MNE subsidiaries; homogenization; knowledge distinctiveness (search for similar items in EconPapers)
JEL-codes: D83 F23 M16 O33 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2026-10
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