AI-DRIVEN TRANSFORMATION OF MAINFRAME ENVIRONMENTS: A COMPREHENSIVE FRAMEWORK FOR OPERATIONAL RESILIENCE - Jagadish Raju (INTERNATIONAL JOURNAL OF ENGINEERING AND TECHNOLOGY RESEARCH (IJETR), 2024)
This article explores the transformative potential of integrating artificial intelligence (AI), machine learning (ML), and generative AI technologies to enhance operational resilience in mainframe environments. As mainframes play a critical role in enterprise computing, ensuring their robustness, scalability, and adaptability becomes paramount. We present a comprehensive framework that leverages AI for process automation, intelligent resource allocation, and predictive maintenance; ML for anomaly detection, capacity planning, and data-driven decision-making; and generative AI for advanced content creation, incident response, and documentation. By synthesizing these technologies, we demonstrate how mainframe operations can achieve unprecedented efficiency, security, and adaptability levels. Our article includes case studies of successful implementations, addresses potential challenges such as legacy system integration and data privacy concerns, and discusses the evolving role of human operators in AI-enhanced environments. This article provides valuable insights for mainframe operators, IT strategists, and organizations seeking to future-proof their critical infrastructure in an increasingly dynamic digital landscape.