LEVERAGING AI-ENABLED INTEGRATION IN MODERN MIDDLEWARE PLATFORMS A STRATEGIC FRAMEWORK FOR ENTERPRISE IT

Abstract

The advent of digitalization in enterprises has led to complex and distributed IT systems which require integration at various levels, continuous data flow in real-time and scale-out model. Existing middleware mainly for message processing, transaction process, and service composition are insufficient to handle the exponential increase in volume, velocity, and variety of data. This paper explores the game changing paradigm of Artificial Intelligence (AI) in turning middleware from a passively functional tunnel to an intelligent context aware system that learns, adjusts and makes decisions. Using a mix-methods approach surveys among IT pros and case studies among finance, healthcare, logistics, and e-commerce the study documents significant improvements in integration efficiency, operational flexibility, compliance and scalability. The findings indicate the decrease in integration time, predict system optimization, and improve decision support. But issues like data privacy, explainability of AI models, and workforce shortages in the necessary skills are still large barriers. The recommended strategic framework provides tangible, incremental next steps for companies to follow -- from identifying needs, to evaluating technology solutions, to piloting deployments, to training a workforce - to continuous monitoring to ensure agile, resilient, and digitally competitive IT ecosystems.