https://globalmainstreamjournal.com/index.php/IJMISDS <p><strong>International Journal of Management Information Systems and Data Science (</strong>ISSN:<strong> <a href="https://portal.issn.org/resource/ISSN-L/2997-9560">2997-9560</a>)</strong> is a top-tier forum for presenting research that advances the understanding and practice of organizational information systems. The journal serves researchers investigating new modes of information technology deployment, the changing landscape of information policy making, and practitioners and executives managing information resources.The IJMISDS is intended for a diverse audience, including academic researchers, industry practitioners, consultants, policymakers, and students. Our readers are individuals and organizations committed to leveraging information systems and data science to achieve strategic objectives, improve operational efficiencies, and drive innovation.</p> GMJ en-US International Journal of Management Information Systems and Data Science 2997-9560 https://globalmainstreamjournal.com/index.php/IJMISDS/article/view/252 <p style="text-align: justify;">Artificial intelligence is transforming industrial engineering in the United States by altering how organisations oversee operations, enhance quality, schedule tasks, maintain equipment, and manage process performance; however, adoption is inconsistent, and numerous firms continue to face challenges in converting experimentation into sustainable value. This study tackles the topic by investigating strategic adoption and solving a knowledge gap regarding the interaction of technical tools, managerial decisions, and organisational systems in practice. The study seeks to elucidate the manner in which organisations incorporate artificial intelligence into industrial engineering and the subsequent impact of that integration on operational decision-making and competence development. It aims to achieve two objectives: to identify the socio-technical and managerial conditions that facilitate reliable, ethical, and effective utilisation in quality control, predictive maintenance, process enhancement, and scheduling, and to investigate how organisations develop the strategic capabilities required to transition from pilot projects to scalable impact. The study is based on the qualitative assertion that successful adoption relies on governance, workflow redesign, and organisational capabilities, rather than solely on technology. Utilising the Resource-Based View, Dynamic Capabilities Theory, and a socio-technical systems perspective, the study employs a qualitative design grounded in document analysis and interpretive synthesis of secondary sources, including policy documents, standards, peer-reviewed research, and applied industrial cases published from 2020 to 2026. The results indicate that acceptance in primary production environments is limited; scaling is influenced more by data discipline, skills, governance, and workflow reconfiguration than by model performance alone, and initial installation may cause temporary disruption prior to the realisation of long-term benefits. The research associate’s strategic adoption with organisational reconfiguration, indicating that enterprises and policymakers ought to regard artificial intelligence as a system redesign, underpinned by human control, risk management, and workforce development. Given that the study emphasises secondary data from the United States, subsequent research should validate these findings by primary fieldwork across all industries and company sizes, establishing this study as a robust foundation for further exploration into the future of industrial engineering.<br /><br /><br /><br /></p> Most Mahbuba Pervin Tanni Evana Tanji Khyrunnahar Mehely Md Saifur Rahman Md Mehedi Hasan Apu Md. Mokshud Ali Copyright (c) 2026 2026-05-15 2026-05-15 3 01 01 12 10.62304/ijmisds.v3i01.252