Artificial Intelligence Governance in International Corporate Management: A Systematic Literature Review of Strategic, Ethical, and Performance Implications
DOI:
https://doi.org/10.53905/Veritas.v1i02.8Keywords:
artificial intelligence governance, international corporate management, multinational enterprises, ESG, corporate performance, AI ethics, strategic managementAbstract
Purpose of the study: The governance of artificial intelligence (AI) in international corporate management has emerged as a critical strategic, ethical, and competitive priority for multinational enterprises (MNEs). This study systematically reviews and synthesizes the global scholarly literature on AI governance in corporate management, examining how it is conceptualized, operationalized, and linked to strategic, ethical, and performance outcomes in international firms.
Methodology: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, a comprehensive search of the Scopus and Web of Science (WOS) databases was conducted in April 2026, spanning publications from 2016 to 2026. The search employed a structured Boolean query targeting AI governance, corporate management, and multinational enterprise contexts. After deduplication and two-stage screening of 712 records, 50 peer-reviewed studies were included for systematic synthesis. Thematic synthesis methodology was applied across five research questions (RQs) guiding the review.
Results: Five thematic clusters emerged in correspondence with the five research questions. AI governance in international corporate management is conceptualized as a multi-dimensional function encompassing strategic oversight, operational controls, regulatory compliance, ethical accountability, and stakeholder transparency. MNEs deploy five categories of governance mechanisms — structural, process, technical, regulatory, and cultural — with adaptive multi-jurisdictional architectures emerging as best practice. AI governance demonstrably shapes corporate strategy and global competitiveness through enhanced decision quality, risk reduction, and stakeholder trust. Significant ethical, ESG, and accountability challenges arise, including algorithmic bias, attribution gaps, and regulatory fragmentation. Evidence indicates a positive and context-dependent association between AI governance quality and corporate performance across operational, reputational, and financial dimensions.
Conclusions: AI governance constitutes a strategic organizational capability that is indispensable for international corporations navigating an increasingly AI-intensive competitive landscape. This review provides a comprehensive conceptual synthesis, a typology of governance mechanisms for MNEs, and a future research agenda addressing the intersection of AI governance, international business strategy, ESG accountability, and corporate performance.
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