Organizational performance optimization: An AI-enabled framework for employee compensation and benefits management

An organization’s long-term success is primarily tied to employee performance, creating a mutually beneficial relationship. High-performance employees drive organizational excellence, whereas effective organizations foster employee growth and development. Strategic compensation and benefit management enable employee performance by fostering trust, well-being, and engagement. This productivity gain translates to a sustainable competitive advantage, as high-performance and engaged human resources consistently deliver superior outcomes and innovations. Therefore, this study proposes an innovative Big Data Analytics (BDA)-enabled Organizational Performance Optimization (OPO) framework for compensation and benefit management. Integrating two technological models–Big Data Enabled Decision Support Architecture (BD-DSA) for secure beneficiary verification, and Contextual Recurrent-Value Learning Unit Gated Recurring Unit (CR-VLU-GRU) for accurate pay-grade classification–this framework addresses critical gaps in Human Resource (HR) decision-making. To balance operational efficiency and employee trust, this study incorporates data privacy-preserving techniques and efficient structuring into the model. The findings of the study show significant improvements in the proposed model when compared with the existing models, with – 50 % faster data retrieval for employee queries, 70 % reduction in employee digital signature creation and verification time, and 99 % accuracy and fairness in compensation allocation based on pay-grade classifications. The study highlights how data-driven compensation and benefit management strategies directly contribute to employee performance, reducing employee turnover, and eventually boosting the productivity and performance of the organization. Limitations and future direction discussions indicate that the integration of corporate social responsibility dimensions and cross-sectoral validations would strengthen the proposed framework to broaden generalizability.