Abstract: International Journal of Blockchain Applications and Financial Technology

Abstract:
The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) technologies is reshaping the land scape of global manufacturing by enabling real-time data-driven decision-making, predictive analytics, and autonomous operations. This research investigates the practical applications, challenges, and strategic impli cations of integrating AI and IoT in smart manufacturing through an in-depth case study of Emirates Global Aluminium (EGA), one of the world’s leading aluminium producers based in the United Arab Emirates. By conducting semi-structured interviews with 15 domain experts, including plant managers, IT engineers, auto mation specialists, and AI consultants, the study captures rich qualitative insights into the technological trans formation taking place within EGA’s production ecosystem. The findings reveal that AI and IoT have signifi cantly enhanced operational efficiency at EGA through predictive maintenance, energy optimization, process automation, and real-time quality control. Experts emphasized the critical role of machine learning algorithms in forecasting equipment failures and the use of IoT-enabled sensors in tracking environmental and perfor mance metrics. Furthermore, the adoption of AIoT technologies has improved data interoperability across departments, contributing to more agile and responsive manufacturing workflows. This study contributes to the growing body of Industry 4.0 literature by offering a grounded case of AI-IoT synergy within a regional industrial leader, thereby bridging the gap between conceptual frameworks and operational realities. It also proposes a customized smart manufacturing framework aligned with EGA’s strategic goals and technological maturity. The implications of the study are both practical and theoretical. Practically, it provides a roadmap for other industrial players in the Middle East seeking to integrate AIoT solutions for enhanced productivity and sustainability. Theoretically, it enriches our understanding of digital transformation in resource-intensive sec tors, offering empirical evidence for models like the Technology-Organization-Environment (TOE) framework and the Dynamic Capabilities Theory. The study also underscores the need for addressing challenges such as cybersecurity vulnerabilities, data governance, talent gaps, and integration costs, which must be strategically managed to ensure long-term competitiveness. This research demonstrates how a forward-thinking industrial enterprise like EGA leverages AI and IoT to remain at the forefront of smart manufacturing, offering valuable lessons for policymakers, practitioners, and academic scholars engaged in digital industrial transformation.