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The Impact of Artificial Intelligence on Human Resource Management in The Indian IT Sector: A Mixed-Method Review

  • Sukeshni Moon
  • 1063-1068
  • Jul 10, 2025
  • Education

The Impact of Artificial Intelligence on Human Resource Management in The Indian IT Sector: A Mixed-Method Review

Sukeshni Moon

Parul University, MCA

DOI: https://doi.org/10.51244/IJRSI.2025.12060085

Received: 23 May 2025; Accepted: 25 June 2025; Published: 10 July 2025

ABSTRACT

This review paper explores the influence of Artificial Intelligence (AI) on Human Resource Management (HRM) within India’s IT sector, employing a mixed-method approach. With data collected from 420 IT employees across 28 Indian states, this study investigates AI’s impact on employee retention, engagement, and decision-making. Key AI features like personalized learning, chatbots, predictive analytics, and virtual reality are examined for their roles in enhancing HRM outcomes. The findings suggest significant correlations between AI integration and improved HR functions, indicating a transformative shift in managing human capital.

INTRODUCTION

The integration of Artificial Intelligence (AI) into HRM practices has revolutionized how organizations manage talent. AI-enabled tools streamline recruitment, improve engagement, and support data-driven decision-making. This paper assesses the effectiveness of AI in the Indian IT industry and its implications for employee retention and engagement. The study applies a mixed-method approach, using both qualitative and quantitative data from 420 respondents.

LITERATURE REVIEW

Studies indicate that AI has positively influenced various HRM functions such as recruitment (Johansson & Herranen, 2019), employee engagement (Prentice et al., 2023), and organizational decision-making (Rožman et al., 2022). Tools such as AI-enabled chatbots and predictive analytics improve response time and personalize experiences, increasing employee satisfaction and retention (Dutta et al., 2023; Bavya et al., 2024).

Further, AI adoption enhances workforce planning (Budrienė & Diskienė, 2020), improves fairness in recruitment, and enables HR analytics for better forecasting. However, some studies also highlight concerns around transparency, algorithmic bias, and the loss of human touch in HR interactions (Tursunbayeva et al., 2018).

RESEARCH FRAMEWORK

Methodology

A mixed-method approach was employed, combining qualitative techniques (in-depth interviews, open-ended questions) and quantitative tools (structured questionnaires, online polls). Primary data was collected from 420 IT employees across 28 Indian states using stratified random sampling.

Variables

Independent Variables: Personalized learning, chatbots, predictive analytics, virtual reality

Dependent Variable: Employee retention

Mediating Variables: Employee engagement, data-driven decision-making

Data Collection & Sampling

420 IT employees were selected from 28 states in India. Employees from union territories were excluded due to smaller sample sizes.

Inclusion Criteria: IT employees, India

Exclusion Criteria: Employees from other sectors, union territories, and other countries

Data was collected through online surveys (Google Forms) and semi-structured interviews. The response rate was 87%, indicating good engagement with the study.

Data Analysis and Results

Data were processed using MS Excel and Smart-PLS software.

Quantitative Findings

Figure 1: State-wise Distribution of Respondents

[Insert bar chart showing varying respondents from each of 28 states.]

Updated Respondent Table

State Respondents
Andhra Pradesh 12
Arunachal Pradesh 13
Assam 14
Bihar 15
Chhattisgarh 10
Goa 8
Gujarat 20
Haryana 17
Himachal Pradesh 9
Jharkhand 10
Karnataka 22
Kerala 18
Madhya Pradesh 14
Maharashtra 24
Manipur 8
Meghalaya 6
Mizoram 7
Nagaland 5
Odisha 13
Punjab 11
Rajasthan 16
Sikkim 4
Tamil Nadu 21
Telangana 20
Tripura 6
Uttar Pradesh 23
Uttarakhand 12
West Bengal 18

Employee Retention Impact

    • 72% reported improved engagement due to AI-driven tools
    • 65% indicated increased satisfaction with HRM services
    • 58% credited chatbots and virtual training for seamless onboarding
    • 80% agreed that predictive analytics helped match roles better

Table 1: Descriptive Statistics

Variable Mean SD VIF HTMT Ratio
Personalized Learning 4.1 0.6 1.4 0.78
Chatbots 3.9 0.8 1.6 0.74
Predictive Analytics 4.0 0.7 1.5 0.76
Virtual Reality 3.8 0.9 1.3 0.72

Figure 2: Impact on Employee Retention

Figure 2: Impact on Employee Retention

Qualitative Insights

  • Many respondents appreciated the transparency of AI in feedback systems.
  • Interviewees noted faster resolution of queries via AI chatbots.
  • Several mentioned increased trust in AI-based recruitment.
  • Some highlighted concerns about reduced human empathy in sensitive HR issues.

Smart-PLS Analysis Screenshots

Smart-PLS Analysis Screenshots

DISCUSSION

The findings confirm that AI significantly influences HRM in IT sectors by increasing operational efficiency and employee satisfaction. Mediating factors like engagement and decision-making strongly correlate with higher retention levels. The study supports prior findings by Ramachandran et al. (2022) and Budrienė & Diskienė (2020) that AI boosts performance and satisfaction.

However, organizations should remain cautious about over-reliance on AI for tasks requiring empathy or nuanced judgment. A balanced hybrid HR model—combining human and AI elements—may provide optimal results.

CONCLUSION

AI has transformed HRM practices in the Indian IT sector. With evidence from 420 employees, this study highlights the role of AI tools in enhancing employee engagement, improving decision-making, and driving retention. Organizations are encouraged to invest further in AI-based HR technologies to remain competitive. A human-in-the-loop approach is recommended to maintain empathy and fairness in HR practices.

Ethical Declaration

The data was collected with informed consent from all participants. Participation was voluntary and anonymous. Ethical guidelines of academic research were followed in the conduct and reporting of this study.

APPENDIX

Survey Questionnaire

Open-ended Questions:

How has AI changed your interaction with HR?

What improvements do you see in employee experience due to AI?

Close-ended Questions:

Do you find AI-based onboarding systems effective? (Yes/No)

Rate the impact of chatbots on resolving HR queries (1-5)

Do predictive analytics help your role alignment? (Yes/No)

Smart-PLS Analysis Screenshots

State-wise Respondent Table

[Updated in section 5.1 above.]

References

  1. Alsafadi, Y., & Altahat, S. (2021). Human resource management practices and employee performance: the role of job satisfaction. The Journal of Asian Finance, Economics and Business, 8(1), 519-529.
  2. Aspers, P., & Corte, U. (2019). What is qualitative in qualitative research. Qualitative sociology, 42, 139-160.
  3. Bavya, S. N., Bashapaka, B., & Reddy, G. S. (2024). An Empirical Study on the Role of Artificial Intelligence in Human Capital Management. International Research Journal on Advanced Engineering and Management (IRJAEM), 2(03), 223-227.
  4. Bhardwaj, G., Singh, S. V., & Kumar, V. (2020). An empirical study of artificial intelligence and its impact on human resource functions. Paper presented at the 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM).
  5. Budrienė, D., & Diskienė, D. (2020). Employee engagement: Types, levels and relationship with practice of HRM. Malaysian e commerce journal, 4(2), 42-47.
  6. Dutta, D., Mishra, S. K., & Tyagi, D. (2023). Augmented employee voice and employee engagement using artificial intelligence-enabled chatbots: a field study. The International Journal of Human Resource Management, 34(12), 2451-2480.
  7. Gopika, M. N., Wilson, M. N. C., & Subha, K. (2021). A study on employee engagement in IT firms. UGC Care Journal, 44(1), 49-58.
  8. Hossin, M. S., Ulfy, M. A., & Karim, M. W. (2021). Challenges in adopting artificial intelligence (AI) in HRM practices: A study on Bangladesh perspective. International Fellowship Journal of Interdisciplinary ResearchVolume, 1.
  9. Jatoba, M. N., Gutierriz, I. E., Fernandes, P. O., Teixeira, J. P., & Moscon, D. (2019). Artificial intelligence in the recrutment & selection: innovation and impacts for the human resources management. Paper presented at the 43rd International scientific conference on economics and social development.
  10. Johansson, J., & Herranen, S. (2019). The application of artificial intelligence (AI) in human resource management: Current state of AI and its impact on the traditional recruitment process.
  11. Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, descriptive statistics, and sample size. Indian dermatology online journal, 10(1), 82.
  12. Kot, S., Hussain, H. I., Bilan, S., Haseeb, M., & Mihardjo, L. W. (2021). The role of artificial intelligence recruitment and quality to explain the phenomenon of employer reputation. Journal of Business Economics and Management, 22(4), 867-883.
  13. Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1), 33267.
  14. Lingao, L. (2024). A Feasibility Study on the Application of Artificial Intelligence on the Human Resource Practices among Manufacturing Companies in China. Journal of Digitainability, Realism & Mastery (DREAM), 3(02), 69-75.
  15. Mazhar, S. A., Anjum, R., Anwar, A. I., & Khan, A. A. (2021). Methods of data collection: A fundamental tool of research. Journal of Integrated Community Health (ISSN 2319-9113), 10(1), 6-10.
  16. McLeod, S. (2019). Qualitative vs Quantitative Research Methods & Data Analysis.
  17. Mer, A., & Srivastava, A. (2023). Employee engagement in the new normal: Artificial intelligence as a buzzword or a game changer? The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part A (pp. 15-46): Emerald Publishing Limited.
  18. Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2022). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. The International Journal of Human Resource Management, 33(6), 1125-1147.
  19. Premnath, E., & Chully, A. A. (2020). Artificial intelligence in human resource management: a qualitative study in the indian context. Journal of Xi’an University of Architecture & Technology, XI, 1193-1205.
  20. Prentice, C., Wong, I. A., & Lin, Z. C. (2023). Artificial intelligence as a boundary-crossing object for employee engagement and performance. Journal of Retailing and Consumer Services, 73, 103376.
  21. Ramachandran, K., Mary, A. A. S., Hawladar, S., Asokk, D., Bhaskar, B., & Pitroda, J. (2022). Machine learning and role of artificial intelligence in optimizing work performance and employee behavior. Materials Today: Proceedings, 51, 2327-2331.
  22. Rožman, M., Oreški, D., & Tominc, P. (2022). Integrating artificial intelligence into a talent management model to increase the work engagement and performance of enterprises. Frontiers in psychology, 13, 1014434.
  23. S, R. A. (Aug 10, 2023). Population vs Sample: Definitions, Differences and Examples. from https://simplilearn.com/tutorials/machine-learning-tutorial/population-vs-sample
  24. Sihag, P. (2021). The mediating role of perceived organizational support on psychological capital–employee engagement relationship: a study of Indian IT industry. Journal of Indian Business Research, 13(1), 154-186.
  25. Varpio, L., Martimianakis, M. A., & Mylopoulos, M. (2022). Qualitative research methodologies: embracing methodological borrowing, shifting and importing. Researching medical education, 115-125.

 

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