The Potential of AI in HRM: Boosting Employee Wellbeing and Engagement in Today’s Hybrid Work Environments
DOI:
https://doi.org/10.70301/SBS.MONO.2025.1.2Keywords:
Key words: Employee engagement; employee wellbeing; AI-based HRM tools; AI-based employee support; HR analytics.Abstract
The Human Resource Management (HRM) is in need to effectively design workplace dynamics where employees feel they are well equipped to perform their daily tasks, whilst protecting their mental and physical wellbeing. This need is accentuated when employees are full or part-time remote. A sense of being connected with the organization, an organization which provides for useful resources to succeed at work may incentivize a desire to reciprocate the employer through higher engagement and better performance. The deployment of AI-powered HRM solutions has the potential to provide for a personalized employee experience that each remote worker can tailor to their daily needs. While working from home (WFH) offers benefits like increased autonomy and work-life balance, it also presents challenges such as work-life spillover and social isolation. This article posits that AI-assisted HRM solutions can significantly improve teleworkers’ engagement through increased wellbeing at work. Through the exploitation of AI, HRM practitioners can design and create a more supportive and personalized remote work environment. This article reviews AI in HRM, highlighting uses like personalized support, predictive analytics, automated tasks, enhanced learning, and real-time feedback. It suggests that combining AI with human efforts can improve employee satisfaction and organizational performance by creating a more inclusive, supportive, and engaging work environment. AI tools such as chatbots, virtual assistants, and predictive analytics can provide real-time support, personalized wellbeing programs, and data-driven insights. These can be brought to the front of the organization’s environment, fostering a more engaged and satisfied workforce. The article reviews AI in HRM, highlighting uses like personalized support, predictive analytics, automated tasks, enhanced learning, and real-time feedback. It suggests that combining AI with human efforts can improve employee satisfaction and organizational performance by creating a more inclusive, supportive, and engaging work environment. Key applications of AI in HRM include automated resume screening, performance evaluation, and employee engagement. Research on AI’s impact on employee engagement is limited, but existing studies highlight AI’s dual role in enhancing efficiency and reducing bias, while also raising concerns about job security, fairness, and privacy. The findings derived from this study suggest AI-assisted HRM solutions may offer significant opportunities to enhance employee engagement and wellbeing, especially for remote or hybrid workers. The overall success of such implementations will depend on surpassing AIs major threats, that is risk of bias, privacy concerns, potential inconsistent feedback, or an over reliance on technology, versus human-to-human interactions. AI-assisted HRM solutions thus must be carefully managed to maximize their benefits.
References
AllHRSoftware. (2024). 10 Best Predictive Analytics Tools for HR 2024. HR List Software. https://allhrsoftware.com/blog/10-best-predictive-analytics-tools-for-hr-2024/
Ayu Gusti, M., Satrianto, A., Candrianto, Juniardi, E., & Fitra, H. (2024). Artificial intelligence for employee engagement and productivity. Problems and Perspectives in Management, 22(3), 174–184. https://doi.org/10.21511/ppm.22(3).2024.14
Bailey, C., Madden, A., Alfes, K., & Fletcher, L. (2017). The Meaning, Antecedents and Outcomes of Employee Engagement: A Narrative Synthesis. International Journal of Management Reviews, 19(1), 31–53. https://doi.org/10.1111/ijmr.12077
Bandura, A., & Cervone, D. (1983). Self-Evaluative and Self-Efficacy Mechanisms Governing the Motivational Effects of Goal Systems. Journal of Personality and SocialPsychology, 45(5), 1017–1028.
Barney, J. B., Ketchen, D. J., & Wright, M. (2011). The Future of Resource-Based Theory: Revitalization or Decline? Journal of Management, 37(5), 1299–1315. https://doi.org/10.1177/0149206310391805
Bhargava, V. (2024). Artifical intelligence and employee engagement: A futuristic approach [Leena AI]. HR Employee Engagement. https://leena.ai/blog/artificial-intelligence-employee-engagement/
Bilderback, S. (2024). I will see you in the summer: Assessing the cognitive impact of social media on employee engagement. Strategic HR Review, 23(6), 206–210. https://doi.org/10.1108/SHR-05-2024-0033
Bloom, N. (2023, December 23). Year in Review (A Special Report): The Changed Workplace—The Biggest Winners and Losers From the Work-From-Home Revolution: Remote or hybrid work has become the new normal for millions of people whose jobs can accommodate it. We are only just starting to see the impact. Wall Street Journal, Eastern Edition. https://www.wsj.com/lifestyle/workplace/work-from-home-impact-revolution-2aa72e0f?msockid=352042d07231603828a85789731a61da
Briere, M., Keip, M., & Le Berthe, T. (2022). Artificial Intelligence for Sustainable Finance: Why it May Help. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4252329
Davis, A. S., & Van Der Heijden, B. I. J. M. (2023). Launching the dynamic employee engagement framework: Towards a better understanding of the phenomenon. Employee Relations: The International Journal, 45(2), 421–436. https://doi.org/10.1108/ER-08-2021-0338
Engagedly. (2024). Leveraging Artificial Intelligence in Human Resource Management. Engagedly. https://engagedly.com/leveraging-artificial-intelligence-in-human-resource-management/
European Parliament. (2020). Artificial Intelligence: Threats and opportunities (Topics European Parliament). European Parliament. https://www.europarl.europa.eu/topics/en/article/20200918STO87404/artificial-intelligence-threats-and-opportunities
Fachrunnisa, O., & Palupi, I. (2024). Affective Engagement and Cognitive Engagement: Two Different Dimensions to Strengthen Employee Work Ethic. Organizational Cultures: An International Journal, 24(2), 61–85. https://doi.org/10.18848/2327-8013/CGP/v24i02/61-85
Fan, D., Zhu, C. J., Timming, A. R., Su, Y., Huang, X., & Lu, Y. (2020). Using the past to map out the future of occupational health and safety research: Where do we go from here? The International Journal of Human Resource Management, 31(1), 90–127. https://doi.org/10.1080/09585192.2019.1657167
Gajendran, R. S., & Harrison, D. A. (2007). The good, the bad, and the unknown about telecommuting: Meta-analysis of psychological mediators and individual consequences. Journal of Applied Psychology, 92(6), 1524–1541. https://doi.org/10.1037/0021-9010.92.6.1524
Gajendran, R. S., Ponnapalli, A. R., Wang, C., & Javalagi, A. A. (2022). A dual pathway model of remote work intensity: A meta‐analysis of its simultaneous positive and negative effects. Personnel Psychology. https://doi.org/10.1111/peps.12641
Gallup. (2024). State of the global workplace. https://www.gallup.com/workplace/349484/state-of-the-global-workplace.aspx
Ganatra, N. J., & Pandya, J. D. (2023). The transformative impact of artificial intelligence on hr practices and employee experience: A review. Journal of Management Research and Analysis, 10(2), 106–111. https://doi.org/10.18231/j.jmra.2023.018
Halliday, B., Van Der Laan, L., & Raineri, A. (2024). Prioritizing Work Health, Safety, and Wellbeing in Corporate Strategies: An Indicative Framework. Safety, 10(1), 18. https://doi.org/10.3390/safety10010018
IBM. (2023). A new set of enterprise AI agents are now available. IBM Community. https://community.ibm.com/community/user/watsonx/blogs/nickolus-plowden/2024/11/15/a-new-set-of-enterprise-hr-agents-are-now-availabl
International Labour Organization. (2024). Worker wellbeing. www.ilo.org
Kahn, W. A. (1990). PSYCHOLOGICAL CONDITIONS OF PERSONAL ENGAGEMENT AND DISENGAGEMENT AT WORK. Academy of Management Journal, 33(4), 692–724. https://doi.org/10.2307/256287
Labrado Antolín, M., Rodríguez-Ruiz, Ó., & Fernández Menéndez, J. (2024). A time after time effect in telework: An explanation of willingness to telework and self-reported productivity. International Journal of Manpower, 45(1), 200–214. https://doi.org/10.1108/IJM-05-2022-0238
Lan, T., Chen, M., Zeng, X., & Liu, T. (2020). The Influence of Job and Individual Resources on Work Engagement Among Chinese Police Officers: A Moderated Mediation Model. Frontiers in Psychology, 11, 497. https://doi.org/10.3389/fpsyg.2020.00497
Liao, G., Li, M., Yin, J., & Wang, Q. (2022). Electronic communication during nonwork time and withdrawal behavior: An analysis of employee cognition-emotion-behavior framework from Chinese cultural context. Frontiers in Psychology, 13, 1010197. https://doi.org/10.3389/fpsyg.2022.1010197
Malik, A. (2024). A Study on the Relationship of Artificial Intelligence Applications in HR Processes for Assessing Employee Engagement, Performance, and Job Security. International Review of Management and Marketing, 14(5), 216–221. https://doi.org/10.32479/irmm.16838
Manfrino, M. (2024). AI and Employee Wellbeing: Navigating Human-centric Integration, Interactive Coaching, and Turnover Mitigation. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4821351
Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370–396. https://doi.org/10.1037/h0054346
McFarland, A. (2024, October 2). 10 Best Employee Engagement Software Platforms. Best Of. https://www.unite.ai/best-employee-engagement-software-platforms/
Microsoft. (2020). Insights from Microsoft Workplace Analytics help Lumen drive ‘anything is possible’ mentality. Customer Stories. https://www.microsoft.com/en/customers/story/847172-lumen-telecommunications-workplace-analytics
Parasa, S. K. (2023). Impact of AI in Employee Wellness and Well-being Programs. European Journal of Advances in Engineering and Technology, 10(15), 98–100.
Ravinder, T. (2024). Why mental health issues are on the rise and how to support your employees in a hybrid work environment. Training Journal (Online).
Rohuelo, R. (2024). How to build confidence in AI: ensuring privacy with proper design. Ernst & Young. https://www.ey.com/en_fi/insights/consulting/mitigating-ai-privacy-risks-strategies-for-trust-and-compliance
Sadeghi, S. (2024). Employee Well-being in the Age of AI: Perceptions, Concerns, Behaviors, and Outcomes.
Setiyani, A., Djumarno, D., Riyanto, S., & Nawangsari, L. Ch. (2019). THE EFFECT OF WORK ENVIRONMENT ON FLEXIBLE WORKING HOURS, EMPLOYEE ENGAGEMENT AND EMPLOYEE MOTIVATION. International Review of Management and Marketing, 9(3), 112–116. https://doi.org/10.32479/irmm.8114
Zhai, Y., Zhang, L., & Yu, M. (2024). AI in Human Resource Management: Literature Review and Research Implications. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-023-01631-z
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