Integration of AI Agents in Recruitment: Opportunities and Challenges
Keywords:
Artificial Intelligence (AI), AI-Agent, Hiring, Opportunities, ChallengesAbstract
This research illustrates the potential implications of AI agent deployment in the
recruitment ecosystem, primarily through a Bangladeshi multinational cultural lens. The
goal is to streamline procedures, accelerate recruitment, introduce a paperless workflow,
and facilitate more rigorous, robust evaluations of prospective candidates, reducing human
error and bias to ensure optimal job alignment. However, a few international studies have
highlighted AI’s utility in the hiring process, but its actual use and impact in Bangladesh
remain largely unstudied. Hence, the results of this study are anticipated to have a broader
impact on Bangladesh’s digitalization of the traditional hiring process, reducing time and
human intervention while addressing algorithmic and behavioral biases. More importantly,
the research also emphasizes the need to educate applicants and recruiters about technical
errors in AI systems, including the trial period of operation and the duration of data feeding.
Ultimately, this study supports not only AI implementation but also responsible adoption
of AI, backed by a regulation framework, stakeholder understanding, and capacity
building initiatives, which can help digitally transform the hiring landscape. These gaps
were addressed through a mixed-method approach combining quantitative surveys with
qualitative interviews of job seekers and human resource professionals. Data has been
collected from multiple organizations through surveys at different levels, secondary sources,
interviews, and relevant case studies. Here, Gibson’s Theory of Affordance serves as the
analytical structure supporting the study by evaluating user acceptance and engagement
with the AI-agent-based system. Future studies and cases should investigate the long-term
feasibility of integrating AI agents into HR operations, especially the recruitment process,
and evaluate the new challenges arising in other Bangladeshi companies.
References
Akhter, F., Bhattacharjee, A., & Hasan, A. (2024, January). Application of artificial intelligence in
human resource management: A Bangladeshi perspective. Munich Personal RePEc Archive. URL:
https://mpra.ub.uni-muenchen.de/122222/
Bonti , M. (2025, April 7). A systematic literature review on artificial intelligence in recruiting and
selection: A matter of ethics. In M. Mori, S. Sassetti, & V. Cavaliere (Eds.), Personnel Review,
54(3). DOI: https://doi.org/10.1108/PR-03-2023-0257
Borsos, P. (2024, July 19). The possibilities of using artificial intelligence as a key technology
in the current employee recruitment process are discussed. In G. Koman & M. Kubina (Eds.),
Administrative Sciences, 14(7), 157. DOI: https://doi.org/10.3390/admsci14070157
Colbak, L. (2025, May). AI agents: From co-pilots to autopilots. Financial Times. URL: https://
www.ft.com/content/3e862e23-6e2c-4670-a68c-e204379fe01f
Faraj, S., & Azad, B. (2012). The materiality of technology: An affordance perspective. In P. M.
Leonardi, B. A. Nardi, & J. Kallinikos (Eds.), Materiality and organizing: Social interaction in
a technological world (pp. 237–258). Oxford University Press. DOI: https://doi.org/10.1093/
acprof:oso/9780199664184.003.0009
Fritts M., Cabrera F. (2021). AI recruitment algorithms and the dehumanization problem. Ethics and
Information Technology, 23(4), 791–801. DOI: https://doi.org/10.1007/s10676-021-09589-z
Geetha, R., & Reddy, D. M. (2018). Recruitment through artificial intelligence: A conceptual study.
International Journal of Mechanical Engineering and Technology, 9(7), 63–70.
Gelinas, J., Vézina, M., & Dumont, B. (2022). Data-driven human resource management:
Prospects and challenges. Human Resource Management Review, 32(2), 100832. DOI: https://doi.
org/10.1016/j.hrmr.2022.100832
Gibson, J. J. (1979). An ecological approach to visual perception. Houghton Mifflin.
Handunge , V. (2021, September). The lifecycle of algorithmic decision-making systems:
Organizational choices and ethical challenges. In M. Marabelli & S. Newell (Eds.), The Journal
of Strategic Information Systems, 30(4), 101695. DOI: https://doi.org/10.1016/j.jsis.2021.101695
Horodyski, P. (2023). Applicants’ perspectives on AI-assisted recruitment: Opportunities and threats.
Frontiers in Psychology, 14, Article 1020270. DOI: https://doi.org/10.3389/fpsyg.2023.1020270
Lashkari, M., & Cheng, J. (2023, January 27). Fairness in AI-driven recruitment: Challenges, metrics,
methods, and future directions. arXiv Preprint arXiv:2405.19699. DOI: https://doi.org/10.48550/arXiv.2405.19699
Marabelli, M., Newell, S., & Handunge , V. (2021, September). The lifecycle of algorithmic
decision-making systems: Organizational choices and ethical challenges. The Journal of Strategic
Information Systems, 30(4), 101695. DOI: https://doi.org/10.1016/j.jsis.2021.101695
Mori, M., Sassetti, S., Cavaliere, V., & Bonti , M. (2025, April 7). A systematic literature review on
artificial intelligence in recruiting and selection: A matter of ethics. Personnel Review, 54(3). DOI:
https://doi.org/10.1108/PR-03-2023-0257
Mujtaba, B. G., & Mahapatra, S. (2024). Artificial intelligence in recruitment and selection:
Organizational practices and ethical implications. International Journal of Human Resource
Management, 35(4), 1289–1315. DOI: https://doi.org/10.1080/09585192.2023.2267857
Rahman, M., Hossain, A., Miah, S., Alom, M., & Islam, M. (2025, January). Artificial intelligence
(AI) in revolutionizing sustainable recruitment: A framework for inclusivity and efficiency.
International Research Journal of Multidisciplinary Scope. URL: https://www.researchgate.net/
publication/388936696
Statista. (2022). According to talent acquisition professionals worldwide, artificial intelligence
benefits hiring. Statista Research Department.
Tambe, P., Cappelli, P., & Yakubovich, V. (n.d.). Artificial intelligence in human resource
management: Challenges and a path forward. California Management Review. DOI: https://doi.
org/10.1177/0008125619867910
Vedapradha, K., Anuradha, J., & Hema, J. (2019). Machine learning techniques for recognizing the
emotional states of autistic children. Microprocessors and Microsystems 74, 103–111. DOI: https://
doi.org/10.1016/j.micpro.2020.103111
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
IBR contents are under the terms of the Creative Commons Attribution License. This permits anyone to copy, distribute, transmit and adapt the work non-commercially provided the original work and source is appropriately cited.


