AI-enhanced strategic management of foreign direct investment in China

The integration of artificial intelligence into foreign direct investment management represents a paradigm shift in strategic decision-making, particularly as traditional analytical frameworks prove inadequate for navigating complex economic environments characterized by volatility, uncertainty, complexity, and ambiguity. This study develops an AI-enhanced strategic management framework for optimizing foreign direct investment (FDI) allocation decisions while examining the mediating roles of labor productivity and infrastructure development in facilitating economic growth outcomes in China’s digital transformation context. Employing a mixed-methods approach combining panel data analysis from 30 Chinese provinces (2010-2023) with machine learning algorithms including Random Forest, Support Vector Machines, and Neural Networks, the research integrates traditional econometric techniques with AI-powered predictive modeling to capture complex non-linear relationships. AI-enhanced management achieves 21% higher prediction accuracy (R²=0.743) compared to traditional methods (R²=0.614) while reducing processing time by 78%. Labor productivity mediates 41.2% and infrastructure development 21.0% of the total effect on economic growth, with significant synergistic interactions (β=0.087, p<0.01) amplifying individual contributions. The findings establish AI integration as essential for modern FDI management, providing theoretical advancement through the four-layer architectural framework and practical implementation guidelines. The research demonstrates that successful AI-enhanced FDI strategies require simultaneous optimization of technological capabilities, human capital development, and infrastructure readiness.