AI-Assisted Software Architecture Design for Multi-Cloud Enterprise Environments

Abstract

The rapid adoption of multi-cloud strategies by enterprises has introduced significant challenges in software architecture design, including increased system complexity, heterogeneous cloud services, interoperability issues, security risks, and performance optimization across distributed environments. Traditional architecture design approaches are often manual, time-consuming, and insufficient to dynamically adapt to evolving business and technological requirements. To address these challenges, this study proposes an AI-assisted software architecture design framework tailored for multi-cloud enterprise environments. The proposed approach leverages artificial intelligence techniques such as machine learning, knowledge-based systems, and architectural pattern recognition to support automated decision-making in cloud service selection, workload distribution, scalability planning, fault tolerance, and security enforcement. By analyzing historical system data, architectural constraints, and real-time operational metrics, the framework provides intelligent recommendations for optimal architectural configurations while ensuring compliance, resilience, and cost efficiency. The AI-assisted model enables architects to evaluate multiple design alternatives, predict performance bottlenecks, and proactively mitigate risks before deployment. Experimental evaluation and scenario-based analysis demonstrate that the proposed framework significantly improves design accuracy, reduces architectural complexity, and enhances system performance compared to conventional design methods. This research highlights the potential of AI-driven architectural intelligence to support adaptive, scalable, and robust software systems in complex multi-cloud enterprise ecosystems.