Artificial Intelligence-Driven Transformation and Digital Upgrade of Traditional Manufacturing: Critical Influencing Factors and Policy Implications

Abstract

The traditional manufacturing sector faces unprecedented competitive pressures and sustainability challenges requiring fundamental transformation through artificial intelligence (AI) integration. This study investigates the critical factors influencing AI-driven digital transformation and intelligent upgrading of traditional manufacturing industries. A mixed-methods approach combining quantitative surveys (n=400) with qualitative interviews (n=45) and documentary analysis captures diverse stakeholder perspectives across manufacturing sectors in China and emerging economies. Results reveal that new-generation information technology, national policies, talent construction, and technological innovation are the strongest predictors of successful transformation (R²=0.378, p<0.001). Digital enterprise transformation and integrated interconnectivity act as critical mediators between foundational factors and manufacturing intelligence upgrade outcomes. Regression analysis demonstrates that government policies contribute 22.4% to transformation success, while technological innovation accounts for 31.2%, and talent construction adds 18.9%. The study identifies persistent barriers including skills gaps (68% of firms), capital constraints (54% report insufficient investment), and organizational resistance to change (61%). Policy recommendations emphasize coordinated investment in R&D infrastructure, workforce development programs, and regulatory frameworks enabling responsible AI adoption. The findings provide actionable guidance for policymakers, manufacturers, and technology providers seeking to navigate Industry 4.0 transitions while maintaining competitiveness and sustainability.