https://globalmainstreamjournal.com/index.php/IJSE/issue/feed 2026-03-29T10:01:22+00:00 Principal editor@globalmainstreamjournal.com Open Journal Systems <p><strong>International Journal of Science and Engineering</strong> (ISSN: <strong><a href="https://portal.issn.org/resource/ISSN-L/2998-4874">2998-4874</a></strong>) is an open-access, peer-reviewed, multidisciplinary, and online journal. GMJ aims to contribute to the constant scientific research and training, so as to promote research in different fields of basic and applied sciences. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence in all the fields of basic and applied sciences.</p> https://globalmainstreamjournal.com/index.php/IJSE/article/view/248 2026-03-29T10:01:22+00:00 Most Mahbuba Pervin Tanni mostmahbubapervint@mail.adelphi.edu Khyrunnahar Mehely khyrunnaharmehely@mail.adelphi.edu Evana Tanji tanji.e004@gmail.com Md Saifur Rahman mrahman62026@ucmberlands.edu Md Mehedi Hasan Apu mehediapu696@gmail.com Md. Mokshud Ali md.mokshudali@gmail.com

<p>The fast expansion of artificial intelligence (AI) capabilities has spurred a fundamental discussion over how established industries in the United&nbsp;States might integrate this general‑purpose technology into basic management practices. Despite significant investments, U.S. manufacturers and service providers have struggled to transform AI pilots into scale deployments, and the larger management implications of AI remain opaque. This qualitative study analyses the strategic integration of AI within American industrial management, using exclusively secondary data sources taken from published case studies, industry reports, academic papers and government documents. The study sets forth one overall aim—to clarify how organizational transformation happens when AI goes from pilot projects to full production. Two specific objectives guide the inquiry: (i) to identify the managerial and socio‑technical aspects that influence effective AI adoption, and (ii) to characterise the organizational capabilities needed to maintain AI‑enabled transformation. Findings suggest that AI integration is less a plug‑and‑play activity than a holistic process needing cultural alignment, continual reskilling and smart governance. While the secondary sources highlight productivity improvements and new data‑driven insights, they also indicate transitional dips, higher work‑in‑progress inventory and the need to restructure workflows and reward systems. The study enriches the literature on digital transformation by presenting an empirically based model of AI integration that emphasizes strategic alignment, participatory change management and the balancing of efficiency and labour well‑being. Implications for practitioners and policymakers are highlighted.</p>

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