ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. AI in Business
Volume 8 - 2025 | doi: 10.3389/frai.2025.1645172
This article is part of the Research TopicAI-Human Co-Evolution: Feedback Loop Design, Organizational Innovation, Ethical Considerations, and Workforce DynamicsView all articles
AI-Enabled Workforce Integration: Blended Human Resource Contribution Rate in Chinese Companies
Provisionally accepted
- Hefei University of Technology, Hefei, China
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With the development of AI technology, the employment mode of companies is undergoing unprecedented changes. The paper defines the composition of blended human resources of a company as three types of formal employees, flexible workers and intelligent machine workers, constructs a blended human resource contribution rate calculation method based on BP-MIV, and analyzes the data of automobile manufacturing companies in 2022. The results show that the contribution rate of blended human resources to company performance is 73.81%. Among them, the contribution rate of formal employees is 19.55%, while flexible workers and intelligent machine workers, despite their significantly smaller proportion in number compared to formal employees, have contribution rates of 20.26% and 34.00%, respectively. In further discussions, the calculation results of the blended human resource contribution rate based on the production function method were compared with those based on the BP-MIV method. The findings indicate that the BP-MIV-based calculation method exhibits certain advantages in capturing nonlinear relationships, such as the synergistic effects of various types of blended human resources on company performance. This study attempts to propose a preliminary theoretical framework and methodological approach for blended human resource management research in the AI era.
Keywords: Human Resources, Formal employees, Flexible workers, intelligent machine workers, Contribution rate, Backward (BP) neural network, Mean impact value (MIV) algorithm, Nonlinear relationships
Received: 11 Jun 2025; Accepted: 30 Jun 2025.
Copyright: © 2025 Zhang, Wu, Ge and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Kexin Zhang, Hefei University of Technology, Hefei, China
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