AI-Augmented Data Transformation: Improving ELT Efficiency Using Generative AI Techniques

Abstract

The rapid growth of enterprise data and cloud-based infrastructures has increased the complexity of Extract, Load, and Transform (ELT) processes in modern data engineering systems. Traditional ELT frameworks often require extensive manual effort for schema mapping, transformation logic creation, data cleansing, and workflow optimization, leading to increased operational cost and reduced efficiency. The present study examines the role of Generative Artificial Intelligence in improving ELT efficiency through AI-augmented data transformation techniques. The research adopts an experimental and quantitative approach to compare traditional ELT systems with AI-enabled ELT frameworks based on execution speed, transformation accuracy, automation efficiency, scalability, and resource utilization. Experimental findings revealed that Generative AI significantly improved workflow automation, reduced processing time, minimized transformation errors, and enhanced scalability in cloud-based environments. AI-generated transformation logic and automated schema alignment contributed to higher productivity and operational consistency across enterprise datasets. The study further highlights the potential of intelligent automation in modernizing data engineering practices and optimizing large-scale data transformation workflows. Despite challenges related to computational cost and infrastructure dependency, the overall findings suggest that Generative AI can play a transformative role in enhancing the efficiency and reliability of ELT systems in enterprise data ecosystems.