Artificial Intelligence-assisted legacy modernization is essential in changing the stalwart mainframe systems of the past into flexible, scalable, and smart architecture. While mainframes are generally dependable, they can be difficult to maintain due to their high maintenance costs, the shortage of skills, and the problems in integrating them with cloud-based systems. By adopting AI-driven modernization strategies such as automated code refactoring, migration of data using smart tools, and predictive maintenance, companies can easily move to microservices, containerized environments, and hybrid cloud platforms. Machine learning models have the capability to go through legacy codebases, figure out efficiency opportunities, and carry out automated testing and deployment. Besides that, AI improves the organization's operational efficiency by generating the insights that can be used to level the workload and detect the anomalies. The coupling of the two is not only about saving the core business logic but also about enabling quicker innovation, less downtime, and enhanced system resilience. Therefore, the use of AI in mainframe modernization is a catalyst for digital transformation and enterprise growth that is sustainable over time.

翻译：人工智能辅助的遗留系统现代化对于将过去稳固的大型机系统转变为灵活、可扩展且智能的架构至关重要。尽管大型机通常具有高可靠性，但由于其高昂的维护成本、技能短缺以及与云原生系统集成的困难，其维护往往面临挑战。通过采用AI驱动的现代化策略，例如自动化代码重构、智能工具辅助的数据迁移以及预测性维护，企业能够顺利过渡至微服务架构、容器化环境及混合云平台。机器学习模型能够深入分析遗留代码库，识别性能优化机会，并执行自动化测试与部署。此外，人工智能通过生成可用于平衡工作负载和检测异常的洞察，显著提升组织的运营效率。两者的结合不仅有助于保留核心业务逻辑，还能推动更快的创新、减少停机时间并增强系统韧性。因此，在大型机现代化中应用人工智能是推动数字化转型与企业可持续发展的关键催化剂。