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

Attia Hussien Gomaa

doi:10.65773/tscr.2.3.154

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Article Type: Research Article

Issue: Vol. 2 No. 3 (2026): In Progress

Published: 26 August 2026

Pages: 1-40

How to Cite

Gomaa, A. H. (2026). AI-Driven Leadership in Manufacturing Supply Chains: A Systematic Literature Review, Research Gaps Analysis, and Exploratory Study of Egyptian Manufacturing Firms. Transnational Supply Chain Research, 2(3), 1-40. https://doi.org/10.65773/tscr.2.3.154

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Authors

Attia Hussien Gomaa ^*

Mechanical Engineering Department, Faculty of Engineering. Shubra, Benha University, Cairo, Egypt

Abstract

Artificial intelligence (AI) is increasingly transforming manufacturing supply chains by enabling predictive analytics, intelligent automation, real-time visibility, data-driven decision-making, and adaptive coordination. Yet, the realization of sustained value from AI extends beyond technological adoption and depends on leadership capabilities that strategically align AI with organizational objectives, orchestrate technological and human resources, establish governance mechanisms, and reconfigure organizational capabilities. Despite the growing body of research on AI-enabled supply chains, the literature remains fragmented and largely technology-centric, with insufficient attention to the leadership mechanisms through which AI capabilities are translated into organizational and supply-chain outcomes, particularly in emerging-economy manufacturing contexts. This study addresses this gap through a two-stage design that integrates a systematic literature review of research published between 2011 and 30 May 2026 with exploratory empirical evidence from 60 senior managers across 15 Egyptian manufacturing firms. The review synthesizes research across key supply-chain functions to identify dominant themes, theoretical perspectives, methodological patterns, and unresolved research gaps, while the empirical inquiry examines AI maturity, leadership practices, and barriers to implementation and scaling. The findings reveal a persistent technology–leadership asymmetry, with limited scholarly attention to strategic AI leadership, governance, organizational readiness, resource orchestration, and human–AI collaboration. Empirical evidence further indicates heterogeneous levels of AI maturity, while legacy systems, fragmented data infrastructures, weak governance, organizational inertia, skills shortages, and leadership capability gaps constrain effective implementation. Integrating these insights, the study conceptualizes AI-driven leadership as a higher-order dynamic capability through which leaders sense AI-enabled opportunities and threats, seize them through strategic resource orchestration and governance, and reconfigure organizational and supply-chain capabilities to enhance intelligence, resilience, agility, and adaptability. The study advances AI and supply-chain scholarship by positioning leadership as a critical micro-foundation of AI-enabled transformation and extending the dynamic capabilities perspective to the emerging-economy manufacturing context.

Keywords:

AI-driven leadership, manufacturing supply chains, artificial intelligence

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