Frameworks for modernizing legacy systems into multi-cloud AI-enabled platforms

Many businesses are based on legacy systems, which face challenges in agility, scalability, and integration with modern AI technologies because of the monolithic and tightly coupled nature of legacy systems. This paper presents a multi-layered model of changing legacy systems to a multi-cloud AI-enabled architecture, including the description of the components, services, integration, governance, and feedback layers. The experiment-based evaluation, i.e., the performance and the modularity metrics comparison, demonstrates that the modernization will result in modularity, extensibility, maintainability, and resilience across clouds. The findings confirm the empirical research of the trade-offs of legacy system modernization. The study suggests a dependable path to gradual modernization and provides an opportunity as well as a prospect of smart, cloud-native changes of essential legacy infrastructures.