Scenario Planning for the Risks of Expanding Artificial Intelligence in Human Capital Management Processes

Objective: This study aimed to develop scenarios addressing the risks posed by the broad application of artificial intelligence (AI) in human capital management processes. Method: This developmental-applied research utilized a descriptive-causal method. The statistical population included managers and professors from a military organization with sufficient expertise in intelligent human capital management. A purposeful sample of 30 individuals was selected for data collection via a questionnaire. The questionnaire's validity was confirmed through content validation, and its reliability was verified using the test-retest method, yielding a correlation coefficient of 0.76. Data analysis was conducted using system dynamics and Vensim software. Findings: Four scenarios were developed and simulated to incorporate all relevant factors. The results indicated that increased AI-related risks in human capital management processes—such as recruitment, development, and performance management—significantly reduce individual productivity. This decline leads to higher dissatisfaction and employee turnover, particularly evident in the fourth scenario where AI risks permeate all management processes. Conclusion: To mitigate the pessimistic scenarios identified in this study, several actions are recommended. These include empowering human capital through soft skills development, enhancing interpersonal interactions, addressing ethical, legal, and security concerns in AI applications, and leveraging AI as an intelligent assistant in human capital management processes. These revisions aim to improve clarity by refining sentence structure and ensuring consistent capitalization while maintaining an academic tone. The restructured sentences enhance readability, and additional details provide a clearer understanding of the methodology and findings.