Architectural Patterns for AI Integration with Legacy Systems

Abstract

Legacy enterprise platforms continue to support critical operations across domains such as healthcare, finance, manufacturing, and public services. At the same time, artificial intelligence capabilities have matured to a point where predictive analytics, adaptive automation, and data driven reasoning can provide substantial operational value. Integrating these capabilities into existing systems introduces architectural challenges related to coupling, data access, trust, and long term maintainability. This paper investigates architectural patterns that enable the integration of artificial intelligence into legacy systems while preserving operational stability. The study synthesizes established architectural strategies and evaluates their effectiveness through structured analysis and empirical comparison. The results highlight practical tradeoffs among performance, scalability, transparency, and governance, offering guidance for organizations seeking incremental and sustainable modernization.