AI-Empowered Human Resource Strategic Management: Opportunities, Challenges, and Strategic Analysis
1. School of Economics and Management, Harbin Normal University, Harbin 150025, China
2. School of Business Administration, South China University of Technology, Guangzhou 510640, China
| Abstract: | The rapid development of Artificial Intelligence (AI) is reshaping organizational management practices, bringing new opportunities and challenges to corporate human resource management. This article delves into the potential opportunities AI presents in the areas of recruitment, training, and motivation within human resource management. These are specifically manifested in improving recruitment efficiency and accuracy, optimizing employee training and development, and personalized employee performance management, leading to a fairer and more efficient employee assessment system. However, in the process of integrating AI with human resources, organizations also face a series of transformative challenges. These mainly involve hard challenges related to shifts in models and technological foundations, such as the disruption of traditional management concepts and models, skill gaps, and the need for retraining. There are also soft challenges related to compliance considerations. Based on these findings, we propose three strategies for AI-empowered human resource management aimed at helping organizations better adapt to the changes brought by AI and fully leverage its potential. These strategies are: innovating management concepts to develop human resource strategic planning, strengthening data privacy protection and algorithm transparency, and enhancing employee training to improve AI literacy among employees. The implementation of these strategies is expected to enhance employee understanding, trust, and adoption of AI technology, which in turn will optimize human resource management processes, increase organizational efficiency, and confer a competitive advantage to organizations in the AI era. |
| Keywords: | Artificial Intelligence Technology; Human Resource Strategic Management; Opportunities; Challenges; Strategic |
| DOI: | 10.57237/j.wjmst.2024.03.002 |
View:
Downloads:
Received: 26 November 2024
Accepted: 24 December 2024
Published Online: 9 January 2025
References
| [1] | Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192-210. |
| [2] | Paluch, S., Tuzovic, S., Holz, H. F., Kies, A., & Jörling, M. (2022). “My colleague is a robot”–exploring frontline employees' willingness to work with collaborative service robots. Journal of Service Management, 33(2), 363-388. |
| [3] | Luo, W., Zhao, W., Zhao, Y., & Wang, Z. (2022). Artificial Intelligence-Driven Organization and Human Resource Management: Practical Insights and Research Directions. Human Resources Development of China, 39(01), 4-16. |
| [4] | Cappelli, P., & Rogovsky, N. G. (2023). Artificial intelligence in human resource management: A challenge for the human-centred agenda? (No. 95). ILO Working Paper. |
| [5] | Sajjadiani, S., Sojourner, A. J., Kammeyer-Mueller, J. D., & Mykerezi, E. (2019). Using machine learning to translate applicant work history into predictors of performance and turnover. Journal of Applied Psychology, 104(10), 1207. |
| [6] | Malik, A., Budhwar, P., Patel, C., & Srikanth, N. R. (2023). May the bots be with you! Delivering HR cost-effectiveness and individualised employee experiences in an MNE. In Artificial Intelligence and International HRM (pp. 83-113). Routledge. |
| [7] | Huang, X., Wang, S., Liu, C., Pan, W., & Liu Y. (2024). Research on the Influential Mechanism of the Usage of Intelligent Machines on Professional Ability Development. Chinese Journal of Management, 21(06), 853-864. |
| [8] | Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159-182. |
| [9] | Chowdhury, S., Budhwar, P., Dey, P. K., Joel-Edgar, S., & Abadie, A. (2022). AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework. Journal of Business Research, 144, 31-49. |
| [10] | Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California management review, 61(4), 66-83. |
| [11] | Zhu, X., Wang, S., & He, Q. (2021). Impact of Skill Requirements on Employees' Thriving at Work: From the Perspective of Artificial Intelligence Embedding. Foreign Economics & Management, 43(11), 15-25. |
| [12] | Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R.,... & Varma, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606-659. |
| [13] | Bankins, S., & Formosa, P. (2023). The ethical implications of artificial intelligence (AI) for meaningful work. Journal of Business Ethics, 185(4), 725-740. |
| [14] | Martin, K., & Waldman, A. (2023). Are algorithmic decisions legitimate? The effect of process and outcomes on perceptions of legitimacy of AI decisions. Journal of Business Ethics, 183(3), 653-670. |
| [15] | Bhave, D. P., Teo, L. H., & Dalal, R. S. (2020). Privacy at work: A review and a research agenda for a contested terrain. Journal of Management, 46(1), 127-164. |
| [16] | He, W., Wang, L., & Wu, X. (2024). Human Resource Management Research Review in the Age of Artificial Intelligence. Bulletin of National Natural Science Foundation, 38(05), 831-840. |
| [17] | Babamiri, M., Heidarimoghadam, R., Ghasemi, F., Tapak, L., & Mortezapour, A. (2022). Insights into the relationship between usability and willingness to use a robot in the future workplaces: Studying the mediating role of trust and the moderating roles of age and STARA. PloS one, 17(6), e0268942. |
| [18] | Jussupow, E., Spohrer, K., Heinzl, A., & Gawlitza, J. (2021). Augmenting medical diagnosis decisions? An investigation into physicians’ decision-making process with artificial intelligence. Information Systems Research, 32(3), 713-735. |
| [19] | Schiavo, G., Businaro, S., & Zancanaro, M. (2024). Comprehension, apprehension, and acceptance: Understanding the influence of literacy and anxiety on acceptance of artificial Intelligence. Technology in Society, 77, 102537. |
| [20] | Johnston, C. S. (2018). A systematic review of the career adaptability literature and future outlook. Journal of Career Assessment, 26(1), 3-30. |
Join the Editorial Board
We invite active, qualified and high profile scientists and researchers to join as Editorial Board Members.
Join UsBecome a Reviewer
Scholars with a strong interest in reviewing are invited to join the reviewer panel to ensure the quality of the research to be published.
Join Us