AI-Driven Leadership in Manufacturing Supply Chains: A Systematic Literature Review, Research Gaps Analysis, and Exploratory Study of Egyptian Manufacturing Firms

Integrating AI into circular supply chain decision-making: a systematic review and conceptual meta-framework

The study concludes that AI should be positioned as a governed decision-support layer rather than an autonomous technical add-on, and that effective circular supply chain decision-making depends on data quality, interoperability, organisational capability, ecosystem coordination, and sustainability guardrails.

Human-Centered Supply Chain Agility in AI-Enabled Customized Apparel Production: Evidence from Qualitative Interviews in the Central American Apparel Industry

Customer customization and high-mix, low-volume production have increased operational complexity and sustainability pressures in global apparel supply chains. Although artificial intelligence (AI) improves visibility, forecasting, and decision support, limited research explains how AI-generated intelligence is translated into coordinated organizational action in labor-intensive manufacturing. This study examines how strategic flexibility, manufacturing flexibility, AI-enabled operational intelligence, and human expertise interact in the enactment of Human-Centered Supply Chain Agility (HCSA) in customized apparel manufacturing. An exploratory qualitative design was employed using semi-structured interviews with 13 senior managers and executives from apparel manufacturing firms operating in Guatemala, Honduras, Nicaragua, and El Salvador. Reflexive thematic analysis identified seven interrelated themes: customer customization and operational uncertainty, strategic flexibility, manufacturing flexibility, AI-enabled operational intelligence, human expertise, AI–human complementarity, and coordinated organizational response. Participants reported varying levels of engagement with AI-assisted and broader digital systems. Where AI-supported tools were used, they primarily provided enhanced visibility and analytical support, while experienced managers remained responsible for contextual interpretation, priority setting, exception handling, and implementation decisions. Participants associated these digitally supported and human-centered processes with more efficient resource use, reduced operational waste, organizational resilience, and more responsible managerial decision-making. Rather than proposing a new theory, this study offers an empirically grounded, process-oriented interpretation of how established agility capabilities are enacted in AI-enabled customized apparel manufacturing.

NEW TRENDS IN CONTEMPORARY ECONOMICS, BUSINESS AND MANAGEMENT

Human–AI Synergy in Workforce 5.0: A Comprehensive Review of Reskilling Strategies and Organizational Agility Frameworks

Background: The rapid advancement of Artificial Intelligence (AI), intelligent automation, and digital technologies is reshaping workforce dynamics across industries. Organizations are increasingly adopting AI-enabled systems to improve operational efficiency, innovation, and decision-making capabilities. This transformation has led to the emergence of Workforce 5.0, a human-centric paradigm that emphasizes collaboration between human intelligence and artificial intelligence to create agile, resilient, and future-ready organizations. Objective: This review aims to examine existing research on Human–AI synergy in Workforce 5.0, with a particular focus on reskilling strategies, workforce transformation, and organizational agility frameworks that support intelligent enterprise development. Review Methodology: A comprehensive literature review was conducted using peer-reviewed journal articles, conference papers, industry reports, and scholarly publications published between 2018 and 2026. Relevant studies were identified through academic databases including Google Scholar, Scopus, IEEE Xplore, SpringerLink, and ScienceDirect. The selected literature was analyzed using thematic synthesis to identify recurring trends, challenges, and emerging frameworks related to Human–AI collaboration. Key Findings: The review reveals that Human–AI collaboration enhances productivity, innovation, decision-making quality, and workforce adaptability. AI-driven learning platforms, personalized reskilling programs, and intelligent workforce analytics emerged as critical enablers of organizational agility. The findings further indicate that organizations adopting collaborative intelligence models achieve greater resilience and competitive advantage compared to those relying solely on automation-focused approaches. Conclusion: Workforce 5.0 represents a significant shift toward human-centered digital transformation, where AI augments rather than replaces human capabilities. Successful implementation requires continuous reskilling, adaptive organizational strategies, and integrated Human–AI collaboration frameworks. The review highlights the growing importance of balancing technological advancement with workforce development to achieve sustainable organizational performance in intelligent enterprises.

A Conceptual Framework for Ai-Driven Industrial Management: Integrating Dynamic Capabilities and Organizational Readiness

A framework in which the microfoundations of dynamic capabilities operate through organizational readiness to shape AI-driven industrial management capability and, in turn, operational and managerial performance outcomes is developed.

Does AI strengthen business leadership decision making: literature review

AI does not replace the role of executive leaders; instead, it serves as a cognitive aid that frees up a leader's capacity from routine operational tasks, yet it still requires the contextual intuition and ethical governance of human leaders.