AI Adoption among Manufacturing SMEs in Malaysia: Interview Insights from A TOE Perspective

Abstract

Artificial Intelligence (AI) has emerged as a critical enabler of Industry 4.0 transformation, offering manufacturing firms opportunities to enhance productivity, operational efficiency, and data-driven decision-making. However, adoption among small and medium enterprises (SMEs), particularly in developing economies, remains uneven. This study examines the challenges influencing AI adoption among Malaysian manufacturing SMEs through the Technology, Organization, and Environment (TOE) framework. A qualitative research design was employed, drawing on in-depth semi-structured interviews with 10 manufacturing SME owners and managers. The findings reveal that technological challenges remain substantial, particularly high implementation costs, legacy machinery incompatibility, limited data infrastructure, and the absence of scalable AI solutions tailored to SME production environments. From an organizational perspective, although leadership awareness toward AI is generally positive, workforce capability gaps, limited digital training structures, vendor dependency, and resistance to constrain implementation readiness. Environmental factors exert both pressure and constraint, as supply chain digitalization requirements and competitive intensity drive adoption urgency, yet institutional support mechanisms remain procedurally complex and underutilized. Collectively, the study demonstrates that AI adoption is not determined by technological availability alone but by the alignment of technological readiness, organizational capability, and ecosystem facilitation. The study contributes empirically grounded insights to SME digital transformation discourse and offers practical implications for policymakers, technology providers, and manufacturing firms seeking to accelerate inclusive AI adoption.