THE IMPACT OF AI AND BIG DATA INTEGRATION ON INDUSTRY 4.0

Abstract

The integration of Artificial Intelligence (AI) and Big Data has become one of the foundational pillars of Industry 4.0, changing industrial practices and driving digitalization faster. This study investigates the effects of AI-Big data convergence on industrial performance, innovation and sustainability. The research design used was based on a mixed-methods approach it involves a systematic review of the recent academic literature and case study analysis of applications in manufacturing, supply chain management, and smart factory ecosystems. The results demonstrate that AI-based predictive models that run with Big Data analytics are highly beneficial in improving real-time monitoring, predictive maintenance, adaptive scheduling, and automated decision-making. Not only do these capabilities lower operating costs, downtime, but also make production more flexible, and more customer-oriented. Moreover, the integration enhances the creation of autonomous systems, digital twins and intelligent supply chains, which cumulatively contribute to the vision of fully interconnected and data-driven industries. Although these advantages exist, there are a number of obstacles to the widespread adoption, such as data security concerns, cross-platform interoperability, ethical concerns, and skills shortages in the workforce. These barriers are further worsened by high implementation cost and resistance to change in traditional industries. It is concluded that AI and Big Data synergy is enablers of Industry 4.0 nevertheless, it cannot achieve its full potential without sound data governance models, strategic investments, and ongoing workforce reskilling. Establishing solutions to such problems will enable industries to succeed in growing sustainably, competitively, and resiliently in the digital age.