<![CDATA[Purpose-This article explains how organizations in emerging economies can adopt artificial intelligence (AI) in quality management without reproducing infrastructure, capability, and governance models designed for resource-rich settings. Design/methodology/approach-An integrative literature review synthesizes research on Quality 4.0, AI-enabled quality control, technology adoption, responsible AI, dynamic capabilities, and development constraints, supplemented by evidence from the World Bank and UN Trade and Development. Findings-AI creates value through augmented inspection, predictive prevention, process optimization, customer-feedback intelligence, traceability, and organizational learning. These benefits are constrained by fragmented data, limited connectivity and compute, skills shortages, vendor dependence, explainability and bias risks, cybersecurity, and weak accountability. The proposed Quality-AI Readiness and Responsible Adoption (QAR-RA) framework sequences adoption through six gates: strategic quality problem, foundational readiness, bounded pilot, human-governed validation, scaled integration, and continuous assurance. Four cross-cutting enablers-leadership, workforce capability, ecosystem collaboration, and proportionate governance-support every gate. Originality/value-The article shifts the debate from technology acquisition to quality-value realization under constraint. It integrates quality management discipline with responsible AI and development readiness, and provides a staged framework suitable for SMEs and other organizations in emerging economies.]]>
