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Abstract
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Artificial Intelligence (AI) is reshaping Industry
4.0, 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 (2020–2025), 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 (0.466), followed by organizational (0.277),
economic (0.161), and social/ethical (0.096). The review screened
52 records (41 English and 11 Persian), with 24 studies meeting
the inclusion criteria. From these, 16 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 ≥ 95% data completeness, ≥ 12 training hours
per employee, ROI payback ≤ 24 months, and 100% 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.
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