Enhancing leadership effectiveness through artificial intelligence adoption: A literature review and exploratory research in the Egyptian manufacturing sector

Abstract

Artificial Intelligence (AI) is increasingly shaping leadership effectiveness in manufacturing by enabling data-driven decision-making and enhancing collaboration with AI systems. In emerging economies such as Egypt, however, AI adoption remains uneven across organizational functions, limiting its potential to strengthen leadership practices. Empirical research on AI adoption in developing manufacturing contexts is scarce. This study addresses this gap through a literature review and exploratory research involving 60 senior leaders from 15 manufacturing firms across eight industries, including automotive, electronics, home appliances, glass and crystal, steel, chemicals, textiles, and food processing. Findings show that AI adoption is highest in strategic planning (21–25%) and customer analytics (16%), moderate in operational areas such as production, quality, and supply chain management (10–18%), and lowest in workforce analytics (3%) and innovation/R&D (2–5%), revealing a fragmented adoption landscape that limits leadership integration. Key barriers include legacy systems, limited data infrastructure, fragmented governance, and organizational resistance. To address these challenges, a structured brainstorming process engaged executives, managers, and AI experts to generate, refine, and prioritize initiatives, resulting in a phased, KPI-driven framework for AI-enabled leadership that integrates digital capabilities, organizational alignment, ethical practices, and strategic governance. The study demonstrates that cross-functional collaboration, ethical oversight, and iterative implementation can transform isolated AI initiatives into sustainable strategic enablers, enhancing leadership effectiveness, operational efficiency, workforce engagement, and long-term competitiveness. These findings provide actionable guidance for advancing AI adoption in manufacturing and highlight directions for future research.