Application of AI in Talent Management
A Systematic Review of Benefits, Challenges, and Prospects
DOI:
https://doi.org/10.48112/aessr.v4i4.941Abstract
Abstract Views: 2053
This systematic literature review explores the current state, advantages, disadvantages, and potential future of using artificial intelligence in talent management. A total of 29 articles published in 2015- 2024 suggest that 33% of the organizations have implemented AI/ML tools, and only 16% are optimally using HR technologies for organizational results. The review identifies four key domains of AI implementation: recruitment and selection, performance analysis, development and training of employees, and strategic implementation. Benefits include increased operational effectiveness, improved decision-making, organizational talent management, and workforce planning processes. That said, technical difficulties, ethical issues on the use of artificial intelligence, privacy, and some organizational individuals' reluctance towards using artificial intelligence remain major hurdles. The results show an increase in the number of publications in recent years, focusing on 2023 and occupying the majority share of journals (83%). More future directions focus on developing a strong theoretical foundation, implementation proposals, and improved ethical standards. These observations indicate that AI holds the potential to revolutionize TM practices but also that organizations need to be cautious of the challenges pertaining to technology integration keeping in view the ethical and human aspects of organizational functioning to effectuate successful implementation.
Keywords:
Artificial intelligence, Employee development, Human resource management, Systematic review, Talent managementReferences
Abdurakhmanov, K., Zikriyoev, A., Shadibekova, D., Khojamkulov, D., & Raimjanova, M. (2023). Limits and challenges of human resource technological talents in AI age. Paper presented at the Proceedings of the 6th International Conference on Future Networks & Distributed Systems, Tashkent, TAS, Uzbekistan. https://doi.org/10.1145/3584202.3584232
Acikgoz, Y., Davison, K. H., Compagnone, M., & Laske, M. (2020). Justice perceptions of artificial intelligence in selection. International Journal of Selection and Assessment, 28(4), 399-416. https://doi.org/https://doi.org/10.1111/ijsa.12306
Agnihotri, A., Pavitra, K. H., Balusamy, B., Maurya, A., & Bibhakar, P. (2023). Artificial intelligence shaping talent intelligence and talent acquisition for smart employee management. EAI Endorsed Transactions on Internet of Things, 10. https://doi.org/https://doi.org/10.4108/eetiot.4642
Albert, E. T. (2019). AI in talent acquisition: a review of AI-applications used in recruitment and selection. Strategic HR Review, 18(5), 215-221. https://doi.org/10.1108/SHR-04-2019-0024
Black, J. S., & van Esch, P. (2021). AI-enabled recruiting in the war for talent. Business Horizons, 64(4), 513-524. https://doi.org/https://doi.org/10.1016/j.bushor.2021.02.015
Chalutz-Ben Gal, H., & Tursunbayeva, A. (2023). Artificial intelligence, human talent, and trust: A management tool for assessing and supporting AI adoption in HRM. In European Academy of Management 2023 Conference proceedings.
Chamorro-Premuzic, T., Akhtar, R., Winsborough, D., & Sherman, R. A. (2017). The datafication of talent: How technology is advancing the science of human potential at work. Current Opinion in Behavioral Sciences, 18, 13-16. https://doi.org/10.1016/j.cobeha.2017.04.007
Chen, C.-C., Wei, C.-C., Chen, S.-H., Sun, L.-M., & Lin, H.-H. (2022). AI predicted competency model to maximize job performance. Cybernetics and Systems, 53(3), 298-317. https://doi.org/https://doi.org/10.1080/01969722.2021.1983701
Chitrao, P., Bhoyar, P. K., Divekar, R., & Bhatt, P. (2022, February). Study on use of artificial intelligence in talent acquisition. In 2022 Interdisciplinary Research in Technology and Management (IRTM) (pp. 1-8). IEEE. https://doi.org/10.1109/IRTM54583.2022.9791659
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), 100899. https://doi.org/https://doi.org/10.1016/j.hrmr.2022.100899
Faqihi, A., & Miah, S. J. (2023). 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/https://doi.org/10.3390/jrfm16010031
França, T. J. F., São Mamede, H., Barroso, J. M. P., & Dos Santos, V. M. P. D. (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
Gonzalez, M. F., Capman, J. F., Oswald, F. L., Theys, E. R., & Tomczak, D. L. (2019). “Where’s the IO?” Artificial intelligence and machine learning in talent management systems. Personnel Assessment and Decisions, 5(3), 5. https://doi.org/https://doi.org/10.25035/pad.2019.03.005
Hmoud, B., & Várallyai, L. (2021). Artificial intelligence in talent acquisition, do we trust it? Agrárinformatika/Journal of Agricultural Informatics, 12(1), 41-51. https://doi.org/https://doi.org/10.17700/jai.2021.12.1.594
Jha, S., & Janardhan, M. (2024). Transforming Talent Acquisition: Leveraging AI for Enhanced Recruitment Strategies in HRM and Employee Engagement. Library Progress International, 44(3), 8857-8867.
Kamaruddin, N., Rahman, A. W. A., & Harris Jr, F. C. (2023). Enhancing talent development using AI-driven curriculum-industry integration. Environment-Behaviour Proceedings Journal, 8(26), 377-382. https://doi.org/10.21834/e-bpj.v8i26.5129
Kar, S., Kar, A. K., & Gupta, M. P. (2021). Modeling drivers and barriers of artificial intelligence adoption: Insights from a strategic management perspective. Intelligent Systems in Accounting, Finance and Management, 28(4), 217-238. https://doi.org/https://doi.org/10.1002/isaf.1503
Kaur, M., AG, R., & Vikas, S. (2021). Adoption of artificial intelligence in human resource management: a conceptual model. Indian Journal of Industrial Relations, 57(2), 331-342.
Liu, S., Li, G., & Xia, H. (2021, February). Analysis of talent management in the artificial intelligence era. In 5th Asia-Pacific Conference on Economic Research and Management Innovation (ERMI 2021) (pp. 38-42). Atlantis Press. https://doi.org/10.2991/aebmr.k.210218.007
Maity, S. (2019). Identifying opportunities for artificial intelligence in the evolution of training and development practices. Journal of Management Development, 38(8), 651-663. https://doi.org/10.1108/JMD-03-2019-0069
Pillai, R., & Sivathanu, B. (2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking: An International Journal, 27(9), 2599-2629. https://doi.org/10.1108/BIJ-04-2020-0186
Popo–Olaniyan, O., Elufioye, O. A., Okonkwo, F. C., Udeh, C. A., Eleogu, T. F., & Olatoye, F. O. (2022). Ai-driven talent analytics for strategic HR decision-making in the United States of America: A Review. International Journal of Management & Entrepreneurship Research, 4(12), 607-622. https://doi.org/https://doi.org/10.51594/ijmer.v4i12.674
Prikshat, V., Islam, M., Patel, P., Malik, A., Budhwar, P., & Gupta, S. (2023). AI-augmented HRM: Literature review and a proposed multilevel framework for future research. Technological Forecasting and Social Change, 193, 122645. https://doi.org/https://doi.org/10.1016/j.techfore.2023.122645
Qin, C., Zhang, L., Cheng, Y., Zha, R., Shen, D., Zhang, Q., Chen, X., Sun, Y., Zhu, C., & Zhu, H. (2023). A comprehensive survey of artificial intelligence techniques for talent analytics. arXiv preprint arXiv:2307.03195. https://doi.org/https://doi.org/10.48550/arXiv.2307.03195
Rajesh, S., Kandaswamy, M. U., & Rakesh, M. A. (2018). The impact of Artificial Intelligence in Talent Acquisition Lifecycle of organizations: A global perspective. International Journal of Engineering Development and Research, 6(2), 709-717. https://doi.org/https://rjwave.org/IJEDR/papers/IJEDR1802131.pdf
Rezzani, A., Caputo, A., & Cortese, C. G. (2020). An analysis of the literature about the application of Artificial Intelligence to the Recruitment and Personnel Selection. Bollettino di Psicologia Applicata, 25-33.
Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33(1), 100925. https://doi.org/https://doi.org/10.1016/j.hrmr.2022.100925
Saxena, P., Sharma, S., & Jora, R. B. (2023, March). Impact of emotional intelligence and artificial intelligence on employee retention: A review of the service industry. In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 819-823). IEEE. https://doi.org/10.1109/ICACCS57279.2023.10113017
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15-42. https://doi.org/10.1177/0008125619867910
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207-222. https://doi.org/10.1111/1467-8551.00375
Tusquellas, N., Palau, R., & Santiago, R. (2024). Analysis of the potential of artificial intelligence for professional development and talent management: A systematic literature review. International Journal of Information Management Data Insights, 4(2), 100288. https://doi.org/https://doi.org/10.1016/j.jjimei.2024.100288
van Esch, P., Stewart Black, J., Franklin, D., & Harder, M. (2021). Al-enabled biometrics in recruiting: Insights from marketers for managers. Australasian Marketing Journal, 29(3), 225-234. https://doi.org/https://doi.org/10.1016/j.ausmj.2020.04.003
Yanamala, K. K. R. (2024a). Artificial Intelligence in talent development for proactive retention strategies. Journal of Advanced Computing Systems, 4(8), 13-21. https://doi.org/10.69987/JACS.2024.40804
Yanamala, K. K. R. (2024b). Strategic implications of AI integration in workforce planning and talent forecasting. Journal of Advanced Computing Systems, 4(1), 1-9. https://doi.org/10.69987/JACS.2024.40101
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Academy of Education and Social Sciences Review

This work is licensed under a Creative Commons Attribution 4.0 International License.

















