The Integration of AI Into Quality Management in Industries Like Automotive, Healthcare, and Defence

Abstract

This study investigates the integration of artificial intelligence (AI) into quality management (QM) across the automotive, healthcare, and defence industries. As organisations increasingly seek data-driven solutions to enhance accuracy, efficiency, and regulatory compliance, AI offers transformative potential. The research analyses how AI technologies—such as machine learning, computer vision, and natural language processing—are reshaping quality assurance by enabling predictive and proactive strategies over traditional reactive methods. A mixed-methods approach was adopted, combining quantitative survey data from 100 industry professionals with qualitative insights from six managerial interviews. The survey included ten closed-ended questions, while the interviews were guided by six open-ended prompts exploring the practical impact of AI integration in QM operations. The findings reveal that AI significantly enhances real-time monitoring, defect detection, and predictive maintenance, contributing to improved product quality and operational resilience. Despite its benefits, AI integration poses challenges, including data quality issues, ethical considerations, and infrastructure readiness. These barriers highlight the need for sector-specific frameworks and strategic planning. The study also emphasises the importance of aligning AI systems with existing processes to ensure successful adoption. Overall, this research offers valuable theoretical insights and practical guidance for implementing AI in quality management. It underscores the role of AI in advancing compliance, reducing human error, and fostering continuous improvement in complex, highly regulated industries.