Artificial Intelligence for Strategic Adaptation in International Business

Abstract

International businesses increasingly face deglobalization pressures that challenge their ability to coordinate activities, interpret uncertainty, and adapt strategic decisions across markets. At the same time, artificial intelligence is becoming a relevant support for managerial decision-making, although its effects remain ambiguous. This study investigates how AI integration can enhance or hinder the dynamic capabilities required by multinational firms under deglobalization pressures. We adopt a qualitative multi-case study design focused on three customer-facing multinational firms: Unilever, Amazon, and Starbucks. The unit of analysis is the AI integration, resulting in six embedded cases across supply-chain forecasting, logistics coordination, recruitment, personalization, and inventory management. Data collection combines semi-structured interviews with secondary sources, including company reports, public declarations, media documentation, and indirect executive interviews. Data analysis follows an inductive three-stage coding approach. Findings show that AI can improve strategic decision-making when it supports cross-border detection, uncertainty processing, and operational responsiveness. However, AI can also create problems when automated judgment becomes opaque, biased, inaccurate, or disconnected from contextual interpretation. The study identifies enhancing, hindering, and mixed effects of AI integration. It contributes by connecting AI-enabled decision support with dynamic capabilities, value co-creation, and value co-destruction in international business. For managers, the study suggests that firms should evaluate AI through decision quality, contextual fit, and governance rather than efficiency alone.