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American Journal of Applied Science and Technology

Peer Reviewed | Open Access | E-ISSN: 2771-2745
Published Article

Integrating AI-Augmented Refactoring And Adaptive Systems For Optimizing Enterprise Software Architectures

Integrating AI-Augmented Refactoring And Adaptive Systems For Optimizing Enterprise Software Architectures

Authors

  • Nathan Meyer
    University of Cape Town, South Africa

Keywords:

AI-Augmented Refactoring Enterprise Systems Monolithic Architecture Predictive Optimization

Abstract

Enterprise software systems have historically relied on monolithic architectures, which, while initially offering simplicity and centralization, increasingly struggle under the pressures of scalability, maintainability, and rapid digital transformation. The advent of artificial intelligence (AI) and automated analytical tools offers new avenues to enhance these monolithic frameworks through refactoring, predictive optimization, and adaptive restructuring. This study provides a comprehensive analysis of AI-augmented approaches to enterprise software refactoring, emphasizing frameworks capable of supporting modularization, resource allocation, and automated fault detection. Drawing from a diverse range of interdisciplinary studies, including robotics, database optimization, cybersecurity, and distributed systems, the article constructs a theoretical and empirical scaffold for understanding the transformative potential of AI in complex software ecosystems. We explore the implications of integrating AI-driven refactoring within enterprise contexts, addressing both technical challenges and organizational considerations. The study highlights how simulation-to-real transfer learning can inform system adaptation (Chukwurah et al., 2024), how predictive modeling enhances resource management (Adepoju et al., 2024), and how robust frameworks mitigate security vulnerabilities in autonomous systems (Ajayi et al., 2024). Furthermore, it critiques prevailing methodologies, outlines limitations in current AI frameworks, and proposes avenues for future research, particularly in developing transparent, explainable AI systems that adhere to ethical and operational standards (Hebbar, 2023). By synthesizing these insights, this research positions AI-augmented refactoring not merely as a technical improvement but as a strategic paradigm shift for enterprise software evolution. The outcomes presented extend beyond isolated technological interventions, advocating for an integrated approach that balances computational efficiency, system reliability, and organizational adaptability. This study contributes to a growing body of knowledge that emphasizes the convergence of AI, software engineering, and enterprise architecture, offering both theoretical and practical insights for academics, practitioners, and policy-makers aiming to future-proof large-scale enterprise systems.

 

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