This dissertation examines the workforce development practices of multinational corporations (MNCs) in the U.S. manufacturing sector, focusing on companies with an existing or emerging presence in the United States, exploring how these organizations adapt their labor strategies to address shifting economic, social, and geopolitical landscapes post COVID-19. The study investigates labor localization strategies employed by MNCs as they navigate challenges including skill gaps, regulatory compliance, and cultural integration within host communities. Additionally, the research briefly explores the impact of technological advancements—specifically the advent of artificial intelligence (AI)—and the broader geoeconomic paradigm shift, highlighting how these factors shape labor demand and influence skill requirements. The role of unions within local contexts is also examined, offering insights into their influence on workforce dynamics and labor relations as MNCs strive to localize effectively. Drawing from qualitative interviews with industry professionals and survey data, the research offers insights into the effectiveness of MNCs' approaches to workforce development, skill transfer, and community engagement. Findings reveal that successful localization requires a blend of adaptive labor practices, collaboration with local institutions, and community-focused initiatives, which together help address the heightened need for skilled labor amidst a resurgence in U.S. manufacturing. The study underscores the importance of public-private partnerships, targeted workforce training programs, technology integration and hiring local talents as critical factors that enable MNCs to bridge skill gaps while supporting local labor markets and achieving strategic objectives.