AI-Driven Enterprise System Integration: Improving Data Interoperability Across Complex Organizations

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in enterprise system integration, enabling organizations to improve data interoperability across increasingly complex digital environments. Modern enterprises often operate heterogeneous systems that generate fragmented and incompatible datasets, creating challenges in communication, coordination, and decision-making processes.
This study examines the role of AI-driven integration technologies in enhancing interoperability among enterprise systems through intelligent automation, machine learning, cloud-based architectures, and real-time data synchronization. The paper explores how AI improves enterprise application integration, optimizes data governance, and facilitates secure information exchange across organizational units. It further analyzes the strategic implications of AI-enabled interoperability for operational efficiency, scalability, and digital transformation. Despite the significant benefits, challenges such as data quality issues, cybersecurity risks, ethical concerns, and legacy system incompatibility remain critical barriers to implementation. The study concludes that AI-driven enterprise integration provides a sustainable pathway for organizations seeking resilient, adaptive, and intelligent information infrastructures in rapidly evolving business ecosystems.