An AI-Driven Enterprise Architecture Optimization Model for Enhancing Adaptive and Intelligent Software Solution Design
Keywords:
Enterprise Architecture, Artificial Intelligence, Adaptive Software Systems, Machine LearningAbstract
The increasing complexity of enterprise systems and the rapid evolution of artificial intelligence (AI) have created a critical need for adaptive software systems that can dynamically align with business goals and technological environments. Traditional Enterprise Architecture (EA) approaches often struggle to accommodate real-time adaptability, scalability, and intelligent decision-making. This research proposes an AI-driven Enterprise Architecture optimization model designed to enhance adaptive and intelligent software solution design. The model integrates machine learning-based monitoring, architectural optimization mechanisms, and data-responsive adaptation strategies to improve system efficiency and decision alignment.
The study synthesizes prior research on EA modeling, AI-as-a-service frameworks, and intelligent monitoring systems to construct a unified optimization approach. The methodology incorporates a hybrid analytical framework combining data-driven architecture evaluation and AI-enabled decision support mechanisms. The findings suggest that integrating AI into EA significantly improves system responsiveness, architectural consistency, and operational efficiency while reducing human intervention in system optimization tasks.
The proposed model contributes to bridging the gap between static architectural design and dynamic intelligent systems, offering a scalable approach for modern digital enterprises. However, challenges such as computational overhead, integration complexity, and data dependency remain critical limitations. The study concludes by outlining future directions for autonomous enterprise architecture systems driven by advanced AI techniques.
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Copyright (c) 2026 Dr. Kenta Watanabe, Dr. Rina Kobayashi (Author)
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