ARTIFICIAL INTELLIGENCE IN DYNAMIC DATA TRANSFORMATION: A FRAMEWORK FOR ENTERPRISE INTEGRATION AND OPTIMIZATION

The exponential growth in data volume and complexity has created an urgent need for more sophisticated approaches to data transformation in enterprise environments. This article presents a comprehensive framework for implementing artificial intelligence (AI) in dynamic data transformation processes, addressing key challenges in data quality, schema evolution, and real-time processing. Through multiple case studies across different industries, we examine the implementation of machine learning algorithms, natural language processing, and predictive analytics in automating and optimizing data transformation workflows. The article demonstrates how AI-driven approaches significantly improve operational efficiency, reduce manual intervention, and enhance data quality while maintaining system scalability. The findings indicate that organizations implementing AI-based transformation strategies achieve substantial improvements in processing speed, accuracy, and adaptability to changing data patterns. The article also addresses critical integration considerations, including architecture design, security implications, and change management strategies. This article contributes to both theoretical understanding and practical implementation of AI in data transformation, providing a structured approach for organizations seeking to modernize their data processing capabilities. The article concludes with recommendations for practitioners and identifies emerging trends that will shape the future of AI-driven data transformation.