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AI-Driven Employee Engagement Strategies in Modern Organizations: A Novel Symbiotic Human-AI Engagement Ecosystem (SHAEE) Framework

AI-Driven Employee Engagement Strategies in Modern Organizations: A Novel Symbiotic Human-AI Engagement Ecosystem (SHAEE) Framework

Volume 11, Issue 1, 2026

International Journal of Commerce and Management Studies, ISSN 2456-3684

Author Name and Affiliation

Sidhika Nema, Sankalp Purwar, Sayyad Asad Kalimsab, Sumedh Pathak, and Pinki Kumari

Dr. D.Y. Patil B School Pune, Maharashtra, India

Abstract

performance that describes a positive emotion spurred by employees’ discretionary effort and involvement with their job is employee engagement. Outside the context of international interaction, however, attitudes are at their lowest ever levels. According to Gallup’s 2026 report, just 20% of all employees were engaged in 2025 which is approximately $10 trillion in individually lost productivity. Traditional strategies, such as annual surveys and employee welfare initiatives do not work in an AI-enhanced, hybrid and multi-generational workplace. On the other hand, this study proposes the framework of Symbiotic Human-AI Engagement Ecosystem (SHAEE) that goes beyond simple AI use and aims to create a Symbiotic relationship: AI becomes an intelligent partner that enhances the agency and creativity of employees and their well-being while co-evolutionarily shaping their organizational environment alongside. The framework unites pieces of multimodal data, predictive and generative AI, agentic systems, and ethical governance layers. This paper shows through literature analysis and on-the-ground application, the improvements in engagement scores that can be yielded within 40% and the possibility of reducing turnover rates within 20% thanks to the application of SHAEE. They worked on innovations like Engagement Resonance Scoring, which uses graph neural networks to propose a range of activities that employees can do to ‘tune in’ to these inputs to maximize their engagement, and the use of reinforcement learning to customise interventions on a one-to-one basis and human-in-the-loop co-adaptation. The contributions include a taxonomy of AI engagement levers, ethical guidelines in line with the EU AI Act, a phasing roadmap, theoretical and practical value for HR leaders and researchers.

Keywords

AI in HRM, employee engagement, Symbiotic AI, predictive analytics, ethical AI governance, agentic systems, personalized employee experience, human-AI collaboration

References

  • Ahmad, A., Ben Mimoun, M. S., & El-Gohary, H. (2025). Examining the Spectrum of Artificial Intelligence Failures. International Journal of Customer Relationship Marketing and Management, 16(1), 1–21. https://doi.org/10.4018/ijcrmm.370401
  • Ali, A., & Rafi, D. N. (2024). Enhancing Human Resource Management Through Advanced Decision-Making Strategies: Harnessing the Power of Artificial Intelligence for Strategic, Data-Driven, And Judicious Choices. Migration Letters, 21(S8), 881–889. https://doi.org/10.59670/ml.v21is8.9488
  • Almeida, F., & Senapati, B. (2024). Striving for Symbiosis: Human-Machine Relations in the AI Era. 12, 1–4. https://doi.org/10.1109/isec61299.2024.10664823
  • Arnold, Z., Schiff, D. S., Schiff, K. J., Love, B., Melot, J., Singh, N., Jenkins, L., Lin, A., Pilz, K., Enweareazu, O., & Girard, T. (2024). Introducing the AI Governance and Regulatory Archive (AGORA): An Analytic Infrastructure for Navigating the Emerging AI Governance Landscape. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, 39–48. https://doi.org/10.1609/aies.v7i1.31615
  • Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. (2022). Job Demands–Resources Theory: Ten Years Later. Annual Review of Organizational Psychology and Organizational Behavior, 10(1), 25–53. https://doi.org/10.1146/annurev-orgpsych-120920-053933
  • Basnet, S. (2024). The Impact of AI-Driven Predictive Analytics on Employee Retention Strategies. International Journal of Research and Review, 11(9), 50–65. https://doi.org/10.52403/ijrr.20240906
  • Bell, A., Nov, O., & Stoyanovich, J. (2023). Think about the stakeholders first! Toward an algorithmic transparency playbook for regulatory compliance. Data & Policy, 5. https://doi.org/10.1017/dap.2023.8
  • Beohar, G., Mishra, R. P., & Pandey, A. (2022). SWOT analysis – on maintenance frameworks for SMEs (pp. 67–84). CRC. https://doi.org/10.1201/9781003293576-6
  • Bucero, A., & Alonderienė, R. (2022). Developing Organizational Context Adjustments in Project-Based Organizations. European Project Management Journal, 12(2), 3–23. https://doi.org/10.56889/nwfc4951
  • Carter, W., & Wynne, K. T. (2024). Integrating artificial intelligence into team decision‐making: Toward a theory of AI–human team effectiveness. European Management Review. https://doi.org/10.1111/emre.12685
  • Deshmukh, S., & Mehta, M. (2025). AI-enabled self-regulated learning: reinforcing a proactive workplace culture – a viewpoint. In Development and Learning in Organizations: An International Journal (Vol. 39, Issue 6, pp. 21–23). Emerald Publishing. https://doi.org/10.1108/dlo-09-2024-0277
  • Dhand, S., Pandey, A., Mishra, S., Sharma, K., Kadanga, A., Rawat, A., Kumari, V., Yadav, M., & Chand, B.-U.-N. (2025). Role of Artificial Intelligence in Enhancing Employee Well-Being at the Workplace (pp. 25–46). IGI Global Scientific. https://doi.org/10.4018/979-8-3373-1270-5.ch002
  • Dutta, S., Ray, A., Chinya, M., Ghatak, S., Mukherjee, A., Bhattacharjee, K., & Das, A. (2024). Predictive HR Analytics to Optimize Decision-Making Processes and Enhance Workforce Performance. International Journal of Recent Trends in Multidisciplinary Research, 79–81. https://doi.org/10.59256/ijrtmr.20240402014
  • G Hessami, A. (2021). Factoring Ethics in Technology, Policy Making, Regulation and AI. Intechopen. https://doi.org/10.5772/intechopen.92952
  • Gadotti, A., Rocher, L., Houssiau, F., Creţu, A.-M., & De Montjoye, Y.-A. (2024). Anonymization: The imperfect science of using data while preserving privacy. Science Advances, 10(29), eadn7053. https://doi.org/10.1126/sciadv.adn7053
  • García‐Navarro, C., Pulido‐Martos, M., & Pérez‐Lozano, C. (2024). The study of engagement at work from the artificial intelligence perspective: A systematic review. Expert Systems, 41(11). https://doi.org/10.1111/exsy.13673
  • Gowda, K. R., Kureethara, J. V., & Jaiwant, S. V. (2024). AI-Enhanced Strategies for Workforce Involvement (pp. 55–78). Igi Global. https://doi.org/10.4018/979-8-3693-6412-3.ch003
  • Goyal, K., Nigam, A., & Goyal, N. (2024). Employee engagement index: A graph‐theoretic matrix approach. Global Business and Organizational Excellence, 43(5), 35–55. https://doi.org/10.1002/joe.22253
  • Hakanen, J. J., & Kaltiainen, J. (2026). Work Engagement: Feeling Happy, Motivated, and Resilient at Work. Annual Review of Organizational Psychology and Organizational Behavior, 13(1), 23–48. https://doi.org/10.1146/annurev-orgpsych-020924-064233
  • Jangid, A. (2024). Ai And Employee Wellbeing: How Artificial Intelligence Can Monitor and Improve Mental Health in The Workplace. International Journal of Advanced Research, 12(10), 743–764. https://doi.org/10.21474/ijar01/19693
  • Juyal, P., & Kundalya, A. (2023). Emotion Detection from Text: Classification and Prediction of Moods in Real-Time Streaming Text. 46–52. https://doi.org/10.1109/icirca57980.2023.10220607
  • Kakulapati, V. (2021). Secure Privacy Analysis of HR Analytics—A Machine Learning Approach (pp. 299–306). Springer Singapore. https://doi.org/10.1007/978-981-33-4443-3_28
  • Khan, M., Kumari, A., Jain, A. K., & Srivastava, S. (2025). The relationship between employee engagement practices, voice and mental health: exploring the mediating role of interpersonal justice and empathetic leadership. Asia-Pacific Journal of Business Administration, 18(1), 113–130. https://doi.org/10.1108/apjba-05-2024-0306
  • Kurnia, D., & Hendriani, S. (2023). EMPLOYEE ENGAGEMENT: A LITERATURE REVIEW. Indonesian Journal of Agricultural Economics, 14(1), 20. https://doi.org/10.31258/ijae.14.1.10-30
  • Lima, S. (2024). Algorithmic Fairness in HRM Balancing AI-Driven Decision Making with Inclusive Workforce Practices. Journal of Information Systems Engineering and Management, 9(4s), 2760–2769. https://doi.org/10.52783/jisem.v9i4s.13251
  • Luo, B. N., Sun, T., Lin, C.-H. (Veronica), Luo, D., Qin, G., & Pan, J. (2020). The human resource architecture model: A twenty-year review and future research directions. The International Journal of Human Resource Management, 32(2), 241–278. https://doi.org/10.1080/09585192.2020.1787486
  • Lushnikova, A., Bongard-Blanchy, K., & Lallemand, C. (2022). What Aspects of Collaboration are Meaningful to You? Informing the Design of Self-Tracking Technologies for Collaboration. 1–5. https://doi.org/10.1145/3547522.3547681
  • Mohan Teja, G., Ravi, L., Devarajan, M., & Subramaniyaswamy, V. (2024). Automating ESG Score Rating with Reinforcement Learning for Responsible Investment. 253–281. https://doi.org/10.1002/9781394214068.ch14
  • Monteiro, D., Mavinkurve, I., Kambli, P., & Surve, P. S. (2024). Federated Learning for Privacy-Preserving Machine Learning: Decentralized Model Training with Enhanced Data Security. International Journal for Research in Applied Science and Engineering Technology, 12(11), 355–361. https://doi.org/10.22214/ijraset.2024.65062
  • Moreno-Cabezali, B. M. (2025). A Literature Review on Artificial Intelligence and Its Role in Enhancing Employee Engagement in Human Resource Management (pp. 169–194). Igi Global. https://doi.org/10.4018/979-8-3373-1005-3.ch007
  • Papademetriou, C., Ragazou, K., Garefalakis, A., & Anastasiadou, S. (2024). The Role of Artificial Intelligence in Employee Wellness and Mental Health (pp. 29–54). Igi Global. https://doi.org/10.4018/979-8-3693-6412-3.ch002
  • Parida, S. K., Panda, M., & Panda, B. (2025). Transforming Organizational Culture with AI (pp. 446–470). Routledge. https://doi.org/10.4324/9781003522157-27
  • Pawar, V., Vhatkar, A., Chavan, P., Gawankar, S., & Nair, S. (2024). The Future of Emotional Engineering: Integrating Generative AI and Emotional Intelligence. 1–6. https://doi.org/10.1109/iccubea61740.2024.10775105
  • Rao, T. V. N., Stephen, M., Manoj, E., & Sangers, B. (2025). Exploring Bias and Fairness in Machine Learning Algorithms (pp. 369–398). Igi Global. https://doi.org/10.4018/979-8-3693-5231-1.ch014
  • Sana Arshad, V. S. S., Radhika Mahajan, & Manjushri Janardan Yadav Kati Anil, M. S. B. K. (2024). Chatbots and Virtual Assistants in HRM: Exploring Their Role in Employee Engagement and Support. Journal of Informatics Education and Research, 4(2). https://doi.org/10.52783/jier.v4i2.1175
  • Shiurkar, M. S. U. (2024). AI ROLE IN EMPLOYEE ENGAGEMENT AND PERFORMANCE MANAGEMENT (pp. 397–406). Iterative International Publishers Self Page Developers Pvt Ltd. https://doi.org/10.58532/v3bhma24ch42
  • Wibaselppa, A., Santosa, T. A., Batjo, S. N., Fauzi, R. U. A., Nugraha, A. R., Sinaga, H. D. E., & Wulandari, A. S. R. (2025). The Role of Employee Engagement in Increasing Millennial and Gen Z Employee Retention. RIGGS: Journal of Artificial Intelligence and Digital Business, 4(2), 51–56. https://doi.org/10.31004/riggs.v4i2.450
  • Wimmer, J., & Waldenburger, L. (2020). DIGITAL STRESS IN EVERYDAY LIFE. AoIR Selected Papers of Internet Research. https://doi.org/10.5210/spir.v2020i0.11364
  • Xu, W. (2024). AI in HCI Design and User Experience (pp. 141–170). Crc. https://doi.org/10.1201/9781003490685-5
  • Yadav, R. (2025). Building an Ethical AI Culture Within Organizations (pp. 1–30). Igi Global. https://doi.org/10.4018/979-8-3693-9894-4.ch001
  • Yu, L., Li, Y., & Fan, F. (2023). Employees’ Appraisals and Trust of Artificial Intelligences’ Transparency and Opacity. Behavioral Sciences, 13(4), 344. https://doi.org/10.3390/bs13040344
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