AI-Empowered Digital Intelligence Transformation of Human Resources: Pathways to New Quality Productivity in Advanced Manufacturing Industry

Abstract

Industry 4.0-driven manufacturing ecosystems demand a paradigm shift in HR strategies, prioritizing agility over traditional administrative models. This study investigates how digital intelligence, powered by emerging technologies, can redefine HR practices to cultivate "new quality productivity"—a metric emphasizing operational efficiency, innovation capacity, and sustainable growth. By synthesizing artificial intelligence, big data analytics, and IoT systems, the research demonstrates how core HR function—sincluding talent development, performance optimization, and organizational decision-making—can align with the evolving demands of smart manufacturing environments. Drawing on empirical evidence from manufacturing enterprises and productivity benchmarking data, the analysis identifies three critical pathways: AI-driven predictive talent analytics that preemptively address skill shortages, personalized learning platforms that accelerate workforce competency development, and dynamic decision-support tools that enhance operational responsiveness. Findings indicate that digitally transformed HR systems achieve dual outcomes: a 30-40% reduction in administrative inefficiencies and measurable improvements in production quality metrics. Specifically, organizations report 15-25% gains in critical areas such as precision manufacturing yield, energy-optimized workflows, and accelerated product deployment cycles. The study further examines implementation barriers, including employee adaptation to data-centric workflows and governance challenges in algorithmic transparency. The insights offer actionable guidance for manufacturing leaders seeking to leverage human-machine collaboration as a cornerstone of sustainable industrial advancement.