Transforming human resource management with artificial intelligence: A systematic review and ethical framework for future HR practices
Keywords:
Workforce Transformation, Human Resource Management (HRM), Artificial Intelligence (AI), Recruitment, Employee Engagement, Predictive Analytics, Systematic Literature Review (SLR), PRISMA Framework, Ethical AIAbstract
Purpose: This study systematically explores the role of Artificial Intelligence (AI) in transforming Human Resource Management (HRM), with a focus on recruitment, employee engagement, and decision-making. It aims to consolidate fragmented research, address ethical issues, and highlight sector-specific AI applications in HR practices. Design/methodology/approach: A systematic literature review (SLR) was conducted. following the PRISMA framework to ensure methodological rigor and transparency. Seventy-five peer-reviewed studies published between January 2020 and May 2025 were examined using databases such as Scopus, Web of Science, IEEE Xplore, SpringerLink, and Google Scholar. Data were summarized through narrative and tabular mapping to compare traditional HR approaches with AI-powered methods. Findings: The review reveals that AI significantly enhances recruitment efficiency by reducing the time-to-hire by nearly 50%, increases employee engagement through real-time sentiment analysis, and improves HR decision-making with predictive analytics that achieve a turnover prediction accuracy of over 75%. However, issues such as data bias, privacy concerns, and employee resistance remain significant. Industry-specific uses reveal that AI adoption varies across technology, healthcare, retail, finance, and education, emphasizing the need for tailored implementation. Research limitations and implications: The study is restricted to peer-reviewed publications in English from 2020 to 2025.
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1. Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS). (2024). In Annals of Computer Science and Information Systems (Vol. 39). Belgrade, Serbia: Polish Information Processing Society. dblpProceedings
2. Alam, M. A. (2024). HR analytics for strategic decisions: Resolving conflicts from a psychological perspective. Research Square. https://doi.org/10.21203/rs.3.rs-5344306/v1
3. Albassam, W. A. (2023). The power of artificial intelligence in recruitment: An analytical review of current AI-based recruitment strategies. International Journal of Professional Business Review, 8(6). https://doi.org/10.26668/businessreview/2023.v8i6.2089
4. Álvarez‐Gutiérrez, F. J., Stone, D. L., Castaño, A. M., & García‐Izquierdo, A. L. (2022). Human resources analytics: A systematic review from a sustainable management approach. Journal of Work and Organizational Psychology, 38(3), 129. https://doi.org/10.5093/jwop2022a18
5. Anjum, A., Hrairi, M., Aabid, A., Yatim, N. H. M., & Ali, M. (2024). Civil structural health monitoring and machine learning: A comprehensive review. Frattura Ed Integrità Strutturale, 18(69), 43. https://doi.org/10.3221/igf-esis.69.04
6. Ardichvili, A., Dirani, K., Jabarkhail, S., El Mansour, W., & Aboulhosn, S. (2024). Using generative AI in human resource development: An applied research study. Human Resource Development International, 27(3), 388–409. https://doi.org/10.1080/13678868.2024.2337964
7. Asif, A. (2024). Integrating AI in recruitment: A review of perceptions, acceptance, adoption and ethical considerations of AI usage. Frontiers in Business Economics and Management, 15(3), 108. https://doi.org/10.54097/c759fx45
8. Ayanponle, L., Okatta, C. G., & Ajiga, D. (2022). AI-powered HR analytics: Transforming workforce optimization and decision-making. International Journal of Science and Research Archive, 5(2), 338. https://doi.org/10.30574/ijsra.2022.5.2.0057
9. Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications.
10. Bartosiak, M., & Modliński, A. (2022). Fired by an algorithm? Exploration of conformism with biased intelligent decision support systems in the context of workplace discipline. Career Development International, 27, 601. https://doi.org/10.1108/cdi-06-2022-0170
11. Batool, A., Zowghi, D., & Bano, M. (2025). AI governance: A systematic literature review. AI and Ethics. https://doi.org/10.1007/s43681-024-00653-w
12. Bodnari, A., & Travis, J. (2025). Scaling enterprise AI in healthcare: The role of governance in risk mitigation frameworks. Npj Digital Medicine, 8(1). https://doi.org/10.1038/s41746-025-01700-4
13. Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02079-x
14. Chowdhury, S., Kumar, Y., Shrivastava, S., Patel, S. K., & Jitendra, S. (2023). A review on the recent scientific and commercial progress on the direct air capture technology to manage atmospheric CO₂ concentrations and future perspectives. Carbon Dioxide Removal.
15. Dima, J., Gilbert, M., Dextras-Gauthier, J., & Giraud, L. (2024). The effects of artificial intelligence on human resource activities and the roles of the human resource triad: Opportunities and challenges. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1360401
16. Fallucchi, F., Coladangelo, M., Giuliano, R., & Luca, E. W. D. (2020). Predicting employee attrition using machine learning techniques. Computers, 9(4), 86. https://doi.org/10.3390/computers9040086
17. Faqihi, A., & Miah, S. J. (2023a). Designing an AI-driven talent intelligence solution: Exploring big data to extend the TOE framework. In Lecture Notes in Computer Science (p. 69). Springer. https://doi.org/10.1007/978-981-99-2233-8_5
18. Faqihi, A., & Miah, S. J. (2023b). Artificial intelligence-driven talent management system: Exploring the risks and options for constructing a theoretical foundation. Journal of Risk and Financial Management, 16(1), 31. https://doi.org/10.3390/jrfm16010031
19. Fenwick, A., Molnár, G., & Frangos, P. (2024). Revisiting the role of HR in the age of AI: Bringing humans and machines closer together in the workplace. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1272823
20. França, T. J. F., Mamede, H. S., Barroso, J., & Santos, V. (2023). Artificial intelligence applied to potential assessment and talent identification in an organisational context. Heliyon, 9(4). https://doi.org/10.1016/j.heliyon.2023.e14694
21. George, G., & Thomas, D. M. R. (2019). Integration of artificial intelligence in human resource. International Journal of Innovative Technology and Exploring Engineering, 9(2), 5069. https://doi.org/10.35940/ijitee.l3364.129219
22. Gölgeci̇, İ., Ritala, P., Arslan, A., McKenna, B., & Ali, I. (2024). Confronting and alleviating AI resistance in the workplace: An integrative review and a process framework. Human Resource Management Review, 35(2), 101075. https://doi.org/10.1016/j.hrmr.2024.101075
23. Gusti, M. A., Satrianto, A., Candrianto, C., Juniardi, E., & Fitra, H. (2024). Artificial intelligence for employee engagement and productivity. Problems and Perspectives in Management, 22(3), 174. https://doi.org/10.21511/ppm.22(3).2024.14
24. Hunkenschroer, A. L., & Kriebitz, A. (2022). Is AI recruiting (un)ethical? A human rights perspective on the use of AI for hiring. AI and Ethics, 3(1), 199. https://doi.org/10.1007/s43681-022-00166-4
25. Hunkenschroer, A. L., & Luetge, C. (2022). Ethics of AI-enabled recruiting and selection: A review and research agenda. Journal of Business Ethics, 178(4), 977. https://doi.org/10.1007/s10551-022-05049-6
26. Jia, X., & Hou, Y. (2024). Architecting the future: Exploring the synergy of AI-driven sustainable HRM, conscientiousness, and employee engagement. Discover Sustainability, 5(1). https://doi.org/10.1007/s43621-024-00214-5
27. SAINI, I. (2020). Artificial intelligence and its role in human resource management. International Journal for Modern Trends in Science and Technology, 8, 310. https://doi.org/10.46501/ijmtst060850
28. Koman, G., Boršoš, P., & Kubina, M. (2024). The possibilities of using artificial intelligence as a key technology in the current employee recruitment process. Administrative Sciences, 14(7), 157. https://doi.org/10.3390/admsci14070157
29. Li, X., Huang, M., Liu, J., Fan, Y., & Cui, M. (2025). The impact of AI negative feedback vs. leader negative feedback on employee withdrawal behavior: A dual-path study of emotion and cognition. Behavioral Sciences, 15(2), 152. https://doi.org/10.3390/bs15020152
30. Luo, H., Shen, L., He, H., Wang, Y., Liu, S., Li, W., Tan, N., Cao, X., & Tao, D. (2025). O1-Pruner: Length-harmonizing fine-tuning for O1-like reasoning pruning. arXiv preprint. https://arxiv.org/abs/2501.12570
31. Luo, W., Zhang, Y., & Mu, M. (2025). Why might AI-enabled interviews reduce candidates’ job application intention? The role of procedural justice and organizational attractiveness. Humanities and Social Sciences Communications, 12(1). https://doi.org/10.1057/s41599-025-05607-z
32. Madanchian, M. (2024). From recruitment to retention: AI tools for human resource decision-making. Applied Sciences, 14(24), 11750. https://doi.org/10.3390/app142411750
33. Madanchian, M., & Taherdoost, H. (2025). Barriers and enablers of AI adoption in human resource management: A critical analysis of organizational and technological factors. Information, 16(1), 51. https://doi.org/10.3390/info16010051
34. Malik, A., Budhwar, P., Patel, C., & Srikanth, N. R. (2020). May the bots be with you! Delivering HR cost-effectiveness and individualised employee experiences in an MNE. The International Journal of Human Resource Management, 33(6), 1148. https://doi.org/10.1080/09585192.2020.1859582
35. Manthena, J., Korrapati, V., & Zhang, C. (2021). SAS PROC MIXED: A statistical programmer's best friend in QoL analyses. In PharmaSUG 2021 Conference Proceedings, Paper SA-062.
36. Manthena, S. R. L. (2021). Impact of artificial intelligence on recruitment and its benefits. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 9(5), 1. https://doi.org/10.37082/ijirmps.2021.v09si05.013
37. Mwita, K., & Kitole, F. A. (2025). Potential benefits and challenges of artificial intelligence in human resource management in public institutions. Discover Global Society, 3(1). https://doi.org/10.1007/s44282-025-00175-8
38. Nawaz, N., Arunachalam, H., Pathi, B. K., & Gajenderan, V. (2024). The adoption of artificial intelligence in human resources management practices. International Journal of Information Management Data Insights, 4(1), 100208. https://doi.org/10.1016/j.jjimei.2023.100208
39. Nyathani, R. (2022). AI-powered recruitment: The future of HR digital transformation. Journal of Artificial Intelligence & Cloud Computing, 1. https://doi.org/10.47363/jaicc/2022(1)133
40. Oman, Z. U., Siddiqua, A., & Noorain, R. (2024). Artificial intelligence and its ability to reduce recruitment bias. World Journal of Advanced Research and Reviews, 24(1), 551. https://doi.org/10.30574/wjarr.2024.24.1.3054
41. Ooi, et al. (2025). Unable to locate with available metadata.
42. Opada, F. M. M., Ibrahim, M. B. H., Irawan, A., Akbar, M. A., & Rasyid, A. (2024). Talent acquisition strategies: A comprehensive examination of recruitment policies for organizational success. Advances in Human Resource Management Research, 2(2). https://doi.org/10.60079/ahrmr.v2i2.185
43. Ouakili, O. E. (2025). The impact of artificial intelligence (AI) on recruitment process. Open Journal of Business and Management, 13(2), 749. https://doi.org/10.4236/ojbm.2025.132039
44. Palos‐Sánchez, P. R., Baena‐Luna, P., Badicu, A., & Infante-Moro, J. C. (2022). Artificial intelligence and human resources management: A bibliometric analysis. Applied Artificial Intelligence, 36(1). https://doi.org/10.1080/08839514.2022.2145631
45. Pei, Q. M., et al. (2024). Exploring fluorite veins using multi-source remote sensing ASTER data: A case study from the Shuitou fluorite deposit in Inner Mongolia, China. Northwestern Geology, 57, 121–134.
46. Pooja, J., & Krishnan, L. R. K. (2024). AI structuring work practices and fuelling employee outcomes Manufacturing industry. International Journal of Research and Innovation in Social Science, 2927. https://doi.org/10.47772/ijriss.2024.807227
47. Prasad, B. V. (2024). The impact of technology on human resource management: Trends and challenges. Kuey, 30(5), 9746. https://doi.org/10.53555/kuey.v30i5.4635
48. Prikshat, V., Malik, A., & Budhwar, P. (2021). AI-augmented HRM: Antecedents, assimilation and multilevel consequences. Human Resource Management Review, 33(1), 100860. https://doi.org/10.1016/j.hrmr.2021.100860
49. Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS). (2024). Annals of Computer Science and Information Systems, 39. https://doi.org/10.15439/978-83-969601-6-0
50. Qayoumi, A. S., Shalizi, H., & Dhankot, M. A. (2025). Review on artificial intelligence application in structural earthquake engineering. International Journal of Science and Research Archive, 14(1), 83. https://doi.org/10.30574/ijsra.2025.14.1.2659
51. Qayoumi, M., Mushtaq, A., Jansen, M., O’Reilly, S., & Jamaluddin, M. (2025, January 10). Adaptive treatment strategy: Adjuvant chemoradiotherapy for a complex SMARCB1 (INI-1)-deficient sinonasal carcinoma with intracranial extension. Cureus, 17(1), e77217. https://doi.org/10.7759/cureus.77217
52. Rahman, M. M., Islam, M. S., Karim, A., Singh, M. K. M., & Hu, G. (2025). Ideologies of teachers and students towards meso-level English-medium instruction policy and translanguaging in the STEM classroom at a Malaysian university. Applied Linguistic Review, 16(2), 825–848.
53. Reena, D. R., K, M. M. A., & Jayakrishnan, S. S. (2019). Emerging trends in human resource analytics in upcoming decade. International Journal of Engineering Applied Sciences and Technology, 4(8), 260. https://doi.org/10.33564/ijeast.2019.v04i08.045
54. Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2022). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33(1), 100925. https://doi.org/10.1016/j.hrmr.2022.100925
55. Rukadikar, A., Khandelwal, K., & Warrier, U. (2025). Reimagining recruitment: Traditional methods meet AI interventions A 20-year assessment (2003–2023). Cogent Business & Management, 12(1). https://doi.org/10.1080/23311975.2025.2454319
56. Sadeghi, Z., Alizadehsani, R., Cifci, M. A., Kausar, S., Rehman, R., Mahanta, P., et al. (2024). A review of explainable artificial intelligence in healthcare. Computers & Electrical Engineering, 118, Article 109370. https://doi.org/10.1016/j.compeleceng.2024.109370
57. Sadeghi, S. (2024). Employee well-being in the age of AI: Perceptions, concerns, behaviors, and outcomes. https://doi.org/10.48550/ARXIV.2412.04796
58. Saini, A. (2023). AI: Changing human capital management in retail Revolutionizing human capital management in retail. Journal of Artificial Intelligence & Cloud Computing, 1. https://doi.org/10.47363/jaicc/2023(2)e167
59. Sasi, R., Kumar, V., & Mehta, P. (2024). Artificial intelligence applications in HR analytics: A systematic review. Journal of Human Resource Technology, 12(3), 210–225.
60. Sasi, K. P. (2024). Impact of AI in recruitment and talent acquisition. Human Resource and Leadership Journal, 9(3), 78. https://doi.org/10.47941/hrlj.2117
61. Saxena, A. (2020). The growing role of artificial intelligence in human resource. EPRA International Journal of Multidisciplinary Research (IJMR), 152. https://doi.org/10.36713/epra4924
62. Singh, A., & Shaurya, A. (2021). Impact of artificial intelligence on HR practices in the UAE. Humanities and Social Sciences Communications, 8(1). https://doi.org/10.1057/s41599-021-00995-4
63. Soleimani, M., Intezari, A., Arrowsmith, J., Pauleen, D. J., & Taşkın, N. (2025). Reducing AI bias in recruitment and selection: An integrative grounded approach. The International Journal of Human Resource Management, 1. https://doi.org/10.1080/09585192.2025.2480617
64. Sucipto, H. (2024). The impact of artificial intelligence (AI) on human resource management practices. Deleted Journal, 1(1), 138. https://doi.org/10.62207/xey9mx18
65. Taslim, W. S., Rosnani, T., & Fauzan, R. (2025). Employee involvement in AI-driven HR decision-making: A systematic review. SA Journal of Human Resource Management, 23. https://doi.org/10.4102/sajhrm.v23i0.2856
66. Tinguely, P., Shrestha, Y. R., & von Krogh, G. (2023). Unlocking the potential of AI in strategic HR decision-making. 4th AI and Strategy Consortium
67. Tinguely, P. N., Lee, J., & He, V. F. (2023). Designing human resource management systems in the age of AI. Journal of Organization Design, 12(4), 263. https://doi.org/10.1007/s41469-023-00153-x
68. Venugopal, J. P., Subramanian, A. A. V., Sundaram, G., Rivera, M., & Wheeler, P. (2024). A comprehensive approach to bias mitigation for sentiment analysis of social media data. Applied Sciences, 14, Article No. 11471.
69. Venugopal, M., Madhavan, V., Prasad, R., & Raman, R. (2024). Transformative AI in human resource management: Enhancing workforce planning with topic modeling. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2432550
70. Vivek, R. (2023). Enhancing diversity and reducing bias in recruitment through AI: A review of strategies and challenges. Informatics Economics Management, 2(4), 101. https://doi.org/10.47813/2782-5280-2023-2-4-0101-0118
71. Wijayati, D. T., Rahman, Z., Fahrullah, A., Rahman, M. F. W., Arifah, I. D. C., & Kautsar, A. (2022). A study of artificial intelligence on employee performance and work engagement: The moderating role of change leadership. International Journal of Manpower, 43(2), 486. https://doi.org/10.1108/ijm-07-2021-0423
72. Yuan, J. (2018). HR predictive data analytics in the era of big data. https://doi.org/10.2991/ebmcsr-18.2018.75
73. Zhang, S. (2023). Artificial intelligence and applications in structural and material engineering. Highlights in Science Engineering and Technology, 75, 240. https://doi.org/10.54097/9qknfc57
74. Zhang, X., Waugh, D. W., & Orbe, C. (2023). Dependence of Northern Hemisphere tropospheric transport on the midlatitude jet under abrupt CO₂ increase. Journal of Geophysical Research: Atmospheres, 128(13), e2022JD038454.
75. Zhu, Y., Corbett, J., & Chiu, Y.-T. (2020). Understanding employees’ responses to artificial intelligence. Organizational Dynamics, 50(2), 100786. https://doi.org/10.1016/j.orgdyn.2020.100786
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