AI-Powered Decision Making in HRM: Designing Dynamic Tariff Plans for Workforce Compensation

Abstract

The adoption of Artificial Intelligence (AI) in the Human Resource Management (HRM) is fundamentally changing the way things were done, and the compensation and benefits are one of the most affected spheres. The paper discusses the paradigm shift of a one-size-fits-all model of compensation to a dynamic AI-based tariff plan. The old models, which are usually grounded on historical standards and annual evaluations are no longer suitable to the new workforce, which is dynamic, flexible, and worldly. They do not consider the market variations in real-time, the nuances of performance of each individual, the values of the skills of the employees and their own preferences. The article assumes that AI-inspired systems, which can be based on machine learning, natural language processing, and predictive analytics, have the potential to create and operate dynamic tariff plans that are fair, competitive, and extremely personalized. We explore the architectural aspects of such systems such as the data aggregation, predictive modelling to market rates and flight risk, ontology mapping of skills, and the optimization of personalized benefits. The methodology is based on the conceptual analysis of AI applications with the support of case vignettes and the overview of the current technological platforms. It is demonstrated in the analysis that AI can help advance pay equity by reducing human bias, improving retention with predictive analytics, and streamlining compensation budgets. Nonetheless, the major issues are mentioned, such as bias in algorithms, the question of data privacy, the black box problem of AI decision-making, and the possibility of dehumanization of HR practices. The conclusion states that to be successful in the talent war, the use of AI in compensation planning is not just a choice but a strategic necessity to organizations. The key is a symbiotic strategy that involves AI managing the data-intensive computing and pattern recognition, whereas HR specialists can ensure strategic management, governance, and understanding of their staff.