Artificial Intelligence in Quality Assurance: A New Paradigm for Total Quality Management

Quality Assurance is a fundamental process where organisations ensure that their products and services are met with established standards and customer needs. Integrating artificial intelligence (AI) and machine learning (ML) into quality assurance (QA) processes can significantly enhance efficiency and accuracy. In this paper, the researcher perform a bibliometric analysis (2013–2024) of the role of AI in quality assurance, evaluate its effect on Total Quality Management (TQM), and recognize important research developments. A bibliometric analysis was accomplished based on explorations in the Scopus database to identify and collect peer-reviewed research articles related to AI in QA. Bibliometric indicators were employed to assess the trends in publications, distribution of disciplines, geographic contributions, institutional collaborations, funding organizations, and journal metrics [9]. Through bibliometric analysis, utilizing data obtained from the Scopus database, this study systematically identified and assessed peer-reviewed research regarding the use of artificial intelligence (AI) in quality assurance (QA). The findings showed a significant increase in the research output concerning AI-driven QA systems in the past decade, as well as the countries, universities, and funding agencies driving specific research trends. AI-driven QA approaches such as defect detection using ML, predictive maintenance frameworks, and automated quality control systems were key themes dominating the literature. Moreover, the analysis revealed ongoing challenges related to AI integration in quality assurance (QA), covering topics such as algorithm bias, ethical concerns, and data governance issues. By facilitating more accurate, speedy and flexible quality management systems—artificial intelligence (AI) is changing the way that quality assurance (QA) is done. Yet harnessing its power requires a well-thought-out deployment strategy, sound governance frameworks, and integration between AI and human skills. By providing valuable insights, this study ultimately serves as a guide for researchers, industry professionals, and events alike seeking ethical, transparent, sustainable integration of AI-driven solutions into QA processes.