An Artificial Intelligence-Driven Human Resource Management Framework for Intelligent Organizational Decision-Making: A System Thinking and System Dynamics Approach

Abstract

The rapid advancement of Artificial Intelligence (AI) has fundamentally transformed Human Resource Management (HRM), requiring organizations to adopt intelligent decision-making frameworks capable of addressing dynamic workforce challenges. However, most existing studies examine AI applications in HRM using static analytical approaches, offering limited insight into the complex feedback mechanisms and long-term behavioral dynamics of organizational systems. This study proposes an Artificial Intelligence-Driven Human Resource Management (AI-HRM) Framework by integrating System Thinking and System Dynamics to support intelligent organizational decision-making.
The research employs a mixed-method modeling approach. System Thinking is utilized to identify causal relationships among key HRM variables, while System Dynamics is applied to develop causal loop diagrams, stock-and-flow models, and policy simulation scenarios. The framework incorporates AI-enabled recruitment, employee competency development, performance management, workforce engagement, talent retention, organizational learning, and organizational performance. Several policy scenarios are simulated to evaluate the long-term impact of AI adoption on strategic HR outcomes.
The proposed model demonstrates that AI implementation generates reinforcing feedback loops that enhance employee capability, decision quality, organizational agility, and sustainable organizational performance. Furthermore, balancing mechanisms associated with technology readiness, employee resistance, ethical AI governance, and organizational investment significantly influence the success of AI-driven HRM implementation. The simulation results indicate that organizations adopting integrated AI-HRM policies achieve superior workforce productivity, lower turnover rates, and more adaptive decision-making compared with conventional HRM practices.
This study contributes to the literature by introducing a novel AI-driven HRM framework that combines Artificial Intelligence, System Thinking, and System Dynamics into an integrated decision-support model. The findings provide practical guidance for managers and policymakers in designing sustainable AI-enabled human resource strategies while extending the theoretical understanding of intelligent organizational decision-making in the digital transformation era.