INTELLIGENT ENTERPRISE INTEGRATION: AN AI FRAMEWORK FOR DYNAMIC DATA TRANSFORMATION AND PROCESS OPTIMIZATION

Abstract

The exponential increase in data volume and complexity has made more advanced methods of data translation in business environments desperately necessary. Emphasizing organizational integration and optimization, this paper offers a thorough methodology for integrating artificial intelligence (AI) in dynamic data transformation systems. To maximize data translation efficiency while preserving data integrity and security, the framework combines machine learning methods, adaptive pattern recognition, and automated rule development. Leveraging deep learning approaches and natural language processing capabilities, the system shows notable gains in managing unstructured data, lowering operator involvement, and speeding processing pipelines. While offering strong error handling and recovery systems, the architecture of the framework stresses flawless interoperability with current corporate infrastructure. The automotive manufacturing environments confirm the efficacy of the framework in practical situations, therefore proving its ability to transform business data transformation methods. This article provides a basis for the next developments in intelligent data processing systems, therefore contributing to both theoretical knowledge and practical implementation of AI-driven data transformation in large-scale industrial environments.