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

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 transformativ...

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Main Author: ROBARI, SAUD HASSAN (author)
Published: 2025
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Online Access:https://bspace.buid.ac.ae/handle/1234/3353
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author ROBARI, SAUD HASSAN
author_facet ROBARI, SAUD HASSAN
author_role author
dc.contributor.none.fl_str_mv Dr Maria Papadaki
dc.creator.none.fl_str_mv ROBARI, SAUD HASSAN
dc.date.none.fl_str_mv 2025-11-20T12:19:33Z
2025-06-15
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv https://bspace.buid.ac.ae/handle/1234/3353
dc.language.none.fl_str_mv en
dc.publisher.none.fl_str_mv The British University in Dubai (BUiD)
dc.subject.none.fl_str_mv artificial intelligence
quality management
predictive maintenance
real-time monitoring
dc.title.none.fl_str_mv The Integration of AI Into Quality Management in Industries Like Automotive, Healthcare, and Defence
dc.type.none.fl_str_mv Dissertation
description 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.
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publishDate 2025
publisher.none.fl_str_mv The British University in Dubai (BUiD)
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spelling The Integration of AI Into Quality Management in Industries Like Automotive, Healthcare, and DefenceROBARI, SAUD HASSAN artificial intelligencequality managementpredictive maintenancereal-time monitoringThis 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.The British University in Dubai (BUiD)Dr Maria Papadaki2025-11-20T12:19:33Z2025-06-15Dissertationapplication/pdfhttps://bspace.buid.ac.ae/handle/1234/3353enoai:bspace.buid.ac.ae:1234/33532025-11-20T12:28:29Z
spellingShingle The Integration of AI Into Quality Management in Industries Like Automotive, Healthcare, and Defence
ROBARI, SAUD HASSAN
artificial intelligence
quality management
predictive maintenance
real-time monitoring
title The Integration of AI Into Quality Management in Industries Like Automotive, Healthcare, and Defence
title_full The Integration of AI Into Quality Management in Industries Like Automotive, Healthcare, and Defence
title_fullStr The Integration of AI Into Quality Management in Industries Like Automotive, Healthcare, and Defence
title_full_unstemmed The Integration of AI Into Quality Management in Industries Like Automotive, Healthcare, and Defence
title_short The Integration of AI Into Quality Management in Industries Like Automotive, Healthcare, and Defence
title_sort The Integration of AI Into Quality Management in Industries Like Automotive, Healthcare, and Defence
topic artificial intelligence
quality management
predictive maintenance
real-time monitoring
url https://bspace.buid.ac.ae/handle/1234/3353