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Abstract

Purpose: This study aims to systematically analyze the roles of Artificial Intelligence (AI) and HR Analytics in enhancing strategic human resource decision-making, focusing on their integration, impact, and implementation challenges.


Research Method: This research employs a Systematic Literature Review (SLR) to analyze peer-reviewed journal articles on AI, HR Analytics, and strategic HR decision-making. Relevant studies were identified, screened, and synthesized to ensure a structured and comprehensive evaluation.


Results and Discussion: The findings indicate that AI and HR Analytics significantly enhance decision quality, speed, and predictive accuracy, enabling a shift from reactive to proactive decision-making. HR Analytics plays a mediating role in transforming workforce data into actionable insights. However, the impact varies depending on organizational readiness, data quality, and analytical capability. Key challenges include data integration, skill gaps, and ethical concerns such as algorithmic bias and transparency.


Implications: This study provides theoretical contributions by offering an integrated framework linking AI, HR Analytics, and strategic decision-making. Practically, organizations should strengthen data governance, analytical capabilities, and leadership support to maximize the benefits of AI-driven HR systems.


Originality: This study offers originality by integrating AI and HR Analytics into a unified framework that explains how both technologies support strategic human resource decision-making while addressing organizational and ethical implementation challenges.

Keywords

artificial intelligence HR analytics strategic decision-making human resource management data-driven organization

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

How to Cite
Bakri, M., & Yassir, Y. (2026). The Role of Artificial Intelligence and HR Analytics in Enhancing Strategic Human Resource Decision-Making. Advances: Jurnal Ekonomi & Bisnis, 4(3), 451–464. https://doi.org/10.60079/ajeb.v4i3.799
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References

  1. Andhika Putra, R., & Mittra Candana, D. (2020). Pengaruh Motivasi Organisasi Dan Komitmen Terhadap Kinerja Karyawan Dengan Organizational Citizenship Behavior (Ocb) Sebagai Variabel Intervening Pada Karyawan Rumah Sakit Umum Daerah (RSUD) Dr. MUHAMMAD ZEIN PAINAN. Jurnal Ekonomi Manajemen Sistem Informasi, 2(1 SE-Articles), 107–116. https://doi.org/10.31933/jemsi.v2i1.337
  2. Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1–11. https://doi.org/10.1111/1748-8583.12090
  3. Ashrafuzzaman, M. D., Prince, K. B., & Rahman, A. (2024). Integrating artificial intelligence in HRM: The role of management information systems in enhancing decision-making processes. International Journal of Management Information Systems and Data Science, 1(4), 1–16. https://doi.org/10.2139/ssrn.5031919
  4. Asif, M., Ali, A., & Shaheen, F. A. (2025). Assessing the Effects of Artificial Intelligence in Revolutionizing Human Resource Management: A Systematic Review. Social Science Review Archives, 3(4 SE-Articles), 2887–2908. https://doi.org/10.70670/sra.v3i3.1055
  5. Bedad, F., Mokhtari, K., & Sammache, A. (2026). Algorithmic Determinism Versus Human Agency: A Systematic Review And Meta-Analysis of Artificial Intelligence and HR Analytics In Organizational Decision-Making. Lex Localis, 24(1), 474–485. https://doi.org/10.52152/pdmv2293
  6. Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2020.102225
  7. Budhwar, P., Malik, A., De Silva, M. T. T., & Thevisuthan, P. (2022). Artificial intelligence – challenges and opportunities for international HRM: a review and research agenda. The International Journal of Human Resource Management, 33(6), 1065–1097. https://doi.org/10.1080/09585192.2022.2035161
  8. Cahyono, A. (2025). Peran Organizational Citizenship Behavior (Ocb) Dalam Memediasi Pengaruh Budaya Organisasi Terhadap Kinerja Pegawai Pada KPP Madya Dua Semarang. Universitas Islam Sultan Agung Semarang.
  9. Cascio, W. F. (2019). Training trends: Macro, micro, and policy issues. Human Resource Management Review, 29(2), 284–297. https://doi.org/https://doi.org/10.1016/j.hrmr.2017.11.001
  10. 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
  11. Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
  12. Ekawati, D., Ichsan, B., & Romadhon, Usuf A. (2025). Pengaruh Komitmen Organisasi Dan Budaya Organisasi Terhadap Kinerja Karyawan Dengan Organizational Citizenship Behavior (Ocb) Sebagai Variabel Mediasi Pada Rumah Sakit di Kota Surakarta. Syntax Literate: Jurnal Ilmiah Indonesia, 10(5), 5447–5459.
  13. Erliani, S., Fairuz, I., Hidayat, T., & Saputra, A. (2024). Public Service Motivation terhadap Kinerja Pegawai dengan Organizational Citizenship Behavior pada Pegawai Rumah Sakit. ASSET: Jurnal Manajemen dan Bisnis, 7(2 SE-Articles), 130–142. https://doi.org/10.24269/asset.v7i2.10201
  14. Falletta, S. V, & Combs, W. L. (2020). The HR analytics cycle: a seven-step process for building evidence-based and ethical HR analytics capabilities. Journal of Work-Applied Management, 13(1), 51–68. https://doi.org/10.1108/JWAM-03-2020-0020
  15. Fernandez, V., & Gallardo-Gallardo, E. (2020). Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption. Competitiveness Review, 31(1), 162–187. https://doi.org/10.1108/CR-12-2019-0163
  16. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
  17. Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer.
  18. HakemZadeh, F., & Rousseau, D. M. (2024). Evidence-based decision-making is a social endeavor. Behavioral Science & Policy, 10(1), 27–33. https://doi.org/10.1177/23794607241265206
  19. Hamilton, R. H., & Sodeman, W. A. (2020). The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 63(1), 85–95. https://doi.org/https://doi.org/10.1016/j.bushor.2019.10.001
  20. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/https://doi.org/10.1016/j.bushor.2018.03.007
  21. Jatobá, M., Santos, J., Gutierriz, I., Moscon, D., Fernandes, P. O., & Teixeira, J. P. (2019). Evolution of Artificial Intelligence Research in Human Resources. Procedia Computer Science, 164, 137–142. https://doi.org/https://doi.org/10.1016/j.procs.2019.12.165
  22. Kamalaldin, A., Linde, L., Sjödin, D., & Parida, V. (2020). Transforming provider-customer relationships in digital servitization: A relational view on digitalization. Industrial Marketing Management, 89, 306–325. https://doi.org/https://doi.org/10.1016/j.indmarman.2020.02.004
  23. Kiran, V. S., Shanmugam, V., Raju, R. K., & Kanagasabapathy, J. R. (2022). Impact of Human Capital Management on Organizational Performance With the Mediation Effect of Human Resource Analytics. International Journal of Professional Business Review, 7(3 SE-Articles), e0667. https://doi.org/10.26668/businessreview/2022.v7i3.0667
  24. Malik, A., Budhwar, P., & Kazmi, B. A. (2023). Artificial intelligence (AI)-assisted HRM: Towards an extended strategic framework. Human Resource Management Review, 33(1), 100940. https://doi.org/https://doi.org/10.1016/j.hrmr.2022.100940
  25. Margherita, A. (2022). Human resources analytics: A systematization of research topics and directions for future research. Human Resource Management Review, 32(2), 100795. https://doi.org/https://doi.org/10.1016/j.hrmr.2020.100795
  26. Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699
  27. McClelland, D. C. (1987). Human motivation. Cup Archive.
  28. Minbaeva, D. B. (2018). Building credible human capital analytics for organizational competitive advantage. Human Resource Management, 57(3), 701–713. https://doi.org/10.1002/hrm.21848
  29. Mohamed, H. H., Matimbwa, H., & Banzi, J. (2025). Unveiling the Potential of Artificial Intelligence in Human Resource Management: A Systematic Review of Adoption Strategies, Challenges, and Future Directions. Cureus Journal of Business and Economics. https://doi.org/10.7759/s44404-025- 03698-0
  30. Nugroho, R. A., & Jazuli, A. (2025). Hubungan Organizational Citizenship Behavior, Kepemimpinan Melayani Dan Budaya Organisasi Guru Sd Di Kecamatan Cilongok, Kabupaten Banyumas. Jurnal Manajemen Pendidikan, 13(01), 36–40. https://doi.org/10.33751/jmp.v13i1.11715
  31. Organ, D. W. (1988). Organizational citizenship behavior: The good soldier syndrome. Lexington books.
  32. Rahmawati, M., & Juwita, K. (2019). Pengaruh komitmen organisasi dan implementasi budaya organisasi terhadap kinerja karyawan Bank Syariah Lantabur. JMD: Jurnal Riset Manajemen & Bisnis Dewantara, 2(2), 63–72. https://doi.org/https://ejournal.stiedewantara.ac.id/index.php/JMD/article/download/350/233.
  33. Robbins, S. P., Judge, T. A., & Campbell, T. T. (2017). “Organizational Behavior. Harlow.” Pearson Education Limited. http://library. lol/main ….
  34. Sanjai Vudugula, Sanath Kumar Chebrolu, Maniruzzaman Bhuiyan, & Farhana Zaman Rozony. (2023). Integrating Artificial Intelligence in Strategic Business Decision-Making: A Systematic Review of Predictive Models. International Journal of Scientific Interdisciplinary Research, 4(1 SE-Articles), 1–26. https://doi.org/10.63125/s5skge53
  35. 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, 2856. https://hdl.handle.net/10520/ejc-sajhrm_v23_n1_a2856
  36. Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018). People analytics—A scoping review of conceptual boundaries and value propositions. International Journal of Information Management, 43, 224–247. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2018.08.002
  37. Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. https://doi.org/https://doi.org/10.1016/j.jjimei.2020.100002
  38. Wahyuni, W., Ahri, R. A., & Hardi, I. (2026). Pengaruh Motivasi dan Pengembangan Karir terhadap Kinerja melalui Kepuasan Kerja Perawat. Jurnal Penelitian Perawat Profesional, 8(1 SE-Articles), 603–614. https://doi.org/10.37287/jppp.v8i1.1864
  39. Widyastuti, I., Yasmir, Y., Esdhona, H., & Rusnaini, S. (2026). Dampak Lingkungan Kerja Terhadap Kinerja Karyawan dengan Motivasi Kerja sebagai Variabel Mediasi. RIGGS: Journal of Artificial Intelligence and Digital Business, 4(4), 9701–9710. https://doi.org/10.31004/riggs.v4i4.5296