The integration of Artificial Intelligence (AI) and Big Data has become a transformative force in modern enterprise systems, enabling organizations to extract actionable insights, automate decision-making, and enhance operational efficiency. This study explores how enterprises leverage large-scale data generated from diverse sources—such as transactional systems, IoT devices, social media, and customer interactions—by applying advanced AI techniques including machine learning, deep learning, and natural language processing. The paper examines the architectural frameworks that support AI and Big Data integration, including distributed storage systems, data lakes, and cloud-based analytics platforms. It also highlights the role of real-time data processing and predictive analytics in driving business intelligence and strategic planning. Furthermore, the study addresses key challenges such as data quality, scalability, data privacy, and integration complexity, along with solutions like data governance frameworks, automated pipelines, and AI-driven analytics platforms. The findings demonstrate that the synergy between AI and Big Data empowers enterprises to achieve data-driven innovation, improve customer experiences, and gain a competitive advantage in a rapidly evolving digital landscape.