Barriers to Artificial Intelligence Adoption in Manufacturing and Industrial Sectors: A Multi-Industry Synthesis and Prioritization Approach

Artificial Intelligence (AI) is reshaping Industry ۴.۰, offering major opportunities to enhance productivity, improve product quality, and reduce costs in manufacturing. Yet its adoption is hindered by technological, organizational, economic, and social/ethical barriers. This study applies a systematic literature review (۲۰۲۰-۲۰۲۵), the Technology-Organization-Environment (TOE) framework, and the Analytic Hierarchy Process (AHP) to identify, classify, and prioritize these obstacles. Extending TOE, the environmental dimension is divided into economic and social/ethical categories, yielding four groups. AHP-derived weights rank technological barriers highest (۰.۴۶۶), followed by organizational (۰.۲۷۷), economic (۰.۱۶۱), and social/ethical (۰.۰۹۶). The review screened ۵۲ records (۴۱ English and ۱۱ Persian), with ۲۴ studies meeting the inclusion criteria. From these, ۱۶ sub-barriers were analyzed across the four categories. A TOE-based conceptual model connects each barrier group to targeted interventions, and is reinforced by a practical roadmap with measurable KPIs such as ≥ ۹۵% data completeness, ≥ ۱۲ training hours per employee, ROI payback ۲۴ months, and ۱۰۰% privacy compliance. The findings provide managers and policymakers with actionable guidance to focus resources on the most critical impediments and accelerate AI readiness in manufacturing industries.

Keywords:

Artificial intelligence adoption , manufacturing , TOE framework , AHP , barrier prioritization , digital transformation