This study aims to develop plausible scenarios for an artificial-intelligence-based human resource management model using a futures research methodology. A mixed-method research design was applied. In the qualitative phase, thematic analysis was conducted through in-depth interviews with university experts and banking industry specialists, followed by open, axial, and selective coding. The Delphi technique was used to validate the extracted components and indicators. In the quantitative phase, structural-scenario analysis was performed using the MICMAC interaction matrix and Scenario Wizard to identify key variables, examine interdependencies, and extract consistent scenarios. Data analysis was conducted using MaxQDA, MICMAC, and Scenario Wizard software. Five core dimensions were identified, including AI-based performance evaluation and e-learning enhancement, prediction of employee motivation and behavior, algorithm-driven optimization of HR processes, organizational justice and communication optimization, and strategic foresight for learning and innovation. MICMAC analysis produced three consistent scenarios: (1) Balanced Integration (optimal), (2) Ethical Acceleration (likely), and (3) Justice Delay (high-risk). The first scenario indicates improved alignment between ethical AI and moderate technological advancement, leading to a potential 30–50% increase in HR efficiency. The second scenario suggests strong synergy between rapid AI development and high ethical standards, while the third scenario warns of reduced organizational trust and systemic bias due to weak ethical governance. The findings demonstrate that AI-based HRM models achieve their highest effectiveness when technological, human, and structural domains are balanced. Futures research enables organizations to anticipate AI-driven transformations, manage ethical and behavioral risks, and design adaptive long-term strategies for human resource development.