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AI Regulatory Fragmentation and MNE Strategy: Evidence from Microsoft, Salesforce, and Oracle
Sullivan, Grace
Sullivan, Grace
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Abstract
This thesis examines how fragmentation in artificial intelligence (AI) regulation affects international business strategy in Software as a Service (SaaS) firms. It compares Microsoft, Salesforce, and Oracle to show how regulatory divergence between the European Union and the United States shapes choices to standardize or adapt data architectures, AI products, and governance. Using a qualitative multiple case design, the study analyzes company reports, technical documentation, and selective secondary sources from 2023–2025. This analysis is done through the theoretical framework of standardization versus adaptation theory and contingency theory. The findings show that all three firms localize data architecture over product features or governance. Microsoft and Salesforce follow hybrid strategies as they keep a global AI product and governance core, while adding EU specific data residency, routing, and controls. Oracle instead follows a fully adapted strategy through an isolated EU sovereign cloud.
Across cases, data sensitivity and compliance risk are the main factors that push firms toward hybrid or fully adapted responses, while strong infrastructure control reduces the cost of deeper localization.
Overall, the study shows that AI regulatory fragmentation reshapes the standardization vs adaptation trade-off and that hybrid, data centric adaptation is an effective strategy for global SaaS firms under emerging fragmented AI regimes.
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Date
2025
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Keywords
AI regulatory fragmentation, Multinational enterprise strategy, Standardization vs Adaptation, Artificial intelligence governance
Citation
Sullivan, Grace. "AI Regulatory Fragmentation and MNE Strategy: Evidence from Microsoft, Salesforce, and Oracle". BA in International Business Capstone, John Cabot University, Rome, Italy. 2025.