Enhancing HR Communication and Retention in Odoo ERP through AI Integration
Keywords:
Artificial Intelligence, Odoo ERP, Human Resources, Employee Performance, Digital CommunicationAbstract
In this rapidly changing business scenario, human resources feature as one of the most pivotal or vital factors of any organization. This article elaborates the incorporation of artificial intelligence in the Odoo ERP system and how it will help improve communications amongst the staff and reduce the attrition rate in any organization. Through machine learning and predictive analytics, organizations make their decision-making process data-driven while managing HR activities and other challenges, including poor communications and a high rate of turnover. Traditional ERP systems are not able to facilitate them and often result in disengagement among employees, thus increasing the rate of attrition.
The present study focuses on the sociological effect of AI in HR management, highlighting how AI can create an engaged workforce without elaborating on the technical aspects of the implementation process. AI-powered solutions provide real-time performance monitoring and predictive analytics that will enable HR professionals to understand the needs of employees more clearly and strategize for effective target retention. This article presents how AI can potentially change HR practices in Odoo ERP through a literature review and case studies. The research underlines the role AI plays in improving employee engagement and communication, hence resulting in lower turnover rates and a more satisfied pool of workers. The present study contributes to providing further valuable insights into other research and practical applications in the management of HR.
References
Ananda, & Wiratama, J. (2022). Evaluation of Enterprise Resource Planning (ERP) and Open-source ERP Modification for Performance Improvement. 2022 Seventh International Conference on Informatics and Computing (ICIC), 1‑9. https://doi.org/10.1109/ICIC56845.2022.10006926
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. DOI: 10.1023/A:1010933404324
Arora, M., Prakash, A., Mittal, A., & Singh, S. (2021). HR Analytics and Artificial Intelligence-Transforming Human Resource Management. 2021 International Conference on Decision Aid Sciences and Application (DASA), 288‑293. https://doi.org/10.1109/DASA53625.2021.9682325
Behera, B., & Kapoor, A. (2023). Impact of Artificial Intelligence on Human Resource Management.
Ganatra, N., & Pandya, J. (2023). The transformative impact of artificial intelligence on hr practices and employee experience : A review. Journal of Management Research and Analysis, 10, 106‑111. https://doi.org/10.18231/j.jmra.2023.018
Huang, M.-H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in shaping employee engagement. Journal of Business Research, 124, 1-12.
Kadasah, E., & Alrwais, O. (2022). EVALUATION OF TRAINING MODULES IN OPEN SOURCE ERP.
Kandpal, B., Sharma, D., Kathuria, S., & Akram, S. V. (2023). Imperative Role of AI in Employee Engagement : The Lens of Job Charactersitics Model. 2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN), 507‑511. https://doi.org/10.1109/ICPCSN58827.2023.00088
Kaur, M., & Gandolfi, F. (2023). Artificial Intelligence in Human Resource Management—Challenges and Future Research Recommendations. Review of International Comparative Management, 24, 382‑393. https://doi.org/10.24818/RMCI.2023.3.382
Konovalova, V., MITROFANOVA, E., MITROFANOVA, A., & GEVORGYAN, R. (2022). The Impact of Artificial Intelligence on Human Resources Management Strategy : Opportunities for the Humanisation and Risks. WISDOM, 2, 88‑96. https://doi.org/10.24234/wisdom.v2i1.763
Kudirat Bukola Adeusi, Prisca Amajuoyi & Lucky Bamidele Benjami (2024). Utilizing machine learning to predict employee turnover in highstress sectors International Journal of Management & Entrepreneurship Research, Volume 6, Issue 5.
Luna, P. B. (2023). Opportunities (but also Challenges) in Applying Artificial Intelligence to Human Resource Management within Companies. Revista CEA, 9(20), Article 20. https://doi.org/10.22430/24223182.2777
Luz, A., & Olaoye, G. (2024). Artificial Intelligence and Employee Experience : Leveraging Technology for Personalization.
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
Mer, Akansha. (2023). Artificial Intelligence in Human Resource Management: Recent Trends and Research Agenda. 10.1108/S1569-37592023000111B003.
Mittal, P., Jora, R. B., Sodhi, K. K., & Saxena, P. (2023). A Review of The Role of Artificial Intelligence in Employee Engagement. 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), 1, 2502‑2506. https://doi.org/10.1109/ICACCS57279.2023.10112957
Olaoye, Favour & Potter, Kaledio & Shad, Ralph. (2024). AI IN HUMAN RESOURCE MANAGEMENT: PREDICTING EMPLOYEE RETENTION AND PERFORMANCE. Artificial Intelligence.
Paigude, S., Pangarkar, S. C., Hundekari, S., Mali, M., Wanjale, K., & Dongre, Y. (2023). Potential of Artificial Intelligence in Boosting Employee Retention in the Human Resource Industry. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), Article 3s. https://doi.org/10.17762/ijritcc.v11i3s.6149
Palos-Sanchez, P., Baena-Luna, P., Badicu, A., & Infante Moro, J. C. (2022). Artificial Intelligence and Human Resources Management : A Bibliometric Analysis. Applied Artificial Intelligence, 36. https://doi.org/10.1080/08839514.2022.2145631
Park, H., Ahn, D., Hosanagar, K., & Lee, J. (2021). Human-AI Interaction in Human Resource Management : Understanding Why Employees Resist Algorithmic Evaluation at Workplaces and How to Mitigate Burdens. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1‑15. https://doi.org/10.1145/3411764.3445304
Rahman, R. (2024). Emerging Trends of Artificial Intelligence in Human Resource Management : A Comprehensive Review And Meta Analysis. International Journal of Innovative Research in Engineering & Management, 06, 2582‑5208. https://doi.org/10.56726/IRJMETS54481
Salman, Hasan & Kalakech, Ali & Steiti, Amani. (2024). Random Forest Algorithm Overview. Babylonian Journal of Machine Learning. 2024. 69-79. 10.58496/BJML/2024/007.
Sammer, J. (2019, December 10). Bringing artificial intelligence into pay decisions. Retrieved from https://www.shrm.org/resourcesandtools/hr-topics/compensation/pages/bringing -artificialintelligence-into-pay-decisions.aspx.
Suri, P. K. (2023). AI-powered Enterprise Resource Planning. Blue Rose Publishers.
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.
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Copyright (c) 2025 Ahmed Gharabti , Imane El Kortbi, Houssame Nekhass, Ahmed Bendahmane

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