Optimizing Data Interoperability Across HRIS Platforms Using AI and Microservices

Abstract

HRIS systems lie at the core of workforce management processes such as payroll, benefit administration, talent acquisition, and employee performance tracking. Interoperability issues are also common as a result of varying data formats, proprietary architectures, and the existence of legacy systems alongside newer HR systems. This paper uses a journalistic approach to examine how AI and microservices architecture can be harnessed to solve interoperability problems between HRIS platforms. Using AI for mapping, transforming, and predicting data, along with microservices to create a decentralized and scalable architecture for seamless data sharing in real time, can help achieve these goals. Our proposed framework abstracts compensation data harmonization through AI and exposes it through API-driven microservices middleware to allow communication between HRIS platforms. Reports of implementation and performance show increased data accuracy, operational efficiency, and compliance with industry standards.