Embracing Artificial Intelligence and Emotional Intelligence towards Succession Planning

People are using various Artificial Intelligence (AI) tools to increase efficiency, improve accuracy and optimize cost. As Human Resources (HR), they also use Thought Intelligence (TI) and Emotional Intelligence (EI). Higher standards are set, where employees must learn AI tools and work diligently. Behavioural competencies are required in the current era over and above functional competencies. This is why employment of HR is still relevant. Succession Planning (SP) maps the key & critical leadership roles. This study encompasses the impact of TI and EI on SP with recommendation of Learning and Development (L&D) interventions to improve competencies of current workforce.

Artificial Intelligence (AI), Thought Intelligence (TI), Emotional Intelligence (EI), Succession Planning (SP), Learning and Development (L&D)

1. Alherimi, N., Abdulmaksoud, S., Ahmed, V., & Bahroun, Z. (2025). A Systematic Literature Review of Artificial Intelligence Advancements in Green Human Resource Management. In Sustainability (Switzerland) (Vol. 17, Issue 22). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/su172210283 [Google Scholar] [Crossref]

2. Alnamrouti, A., Rjoub, H., & Ozgit, H. (2022). Do Strategic Human Resources and Artificial Intelligence Help to Make Organisations More Sustainable? Evidence from Non-Governmental Organisations. Sustainability (Switzerland), 14(12). https://doi.org/10.3390/su14127327 [Google Scholar] [Crossref]

3. Atoyebi, T. A., & Sopuru, J. (2025). Humanizing AI in Service Workplaces: Exploring Supervisor Support as a Moderator in HPWSs. Sustainability (Switzerland), 17(17). https://doi.org/10.3390/su17177892 [Google Scholar] [Crossref]

4. Ćosić, K., Kopilaš, V., & Jovanović, T. (2025). Importance of mental readiness in highly stressful professions. In Psychiatria Danubina (Vol. 37, Issue 2, pp. 132–140). Medicinska Naklada Zagreb. https://doi.org/10.24869/psyd.2025.132 [Google Scholar] [Crossref]

5. EVELYN VIJAYARAJAN, R., & S K, S. (2025). Remote/Flexible Work and Retention: A Mixed-Methods Study of Work Stress and Organizational Support Using Machine Learning-Based Speech and Text Analysis. Interdisciplinary Journal of Information, Knowledge, and Management, 20, 029. https://doi.org/10.28945/5617 [Google Scholar] [Crossref]

6. Fernández-Miguel, A., Ortíz-Marcos, S., Jiménez-Calzado, M., Fernández del Hoyo, A. P., García-Muiña, F. E., & Settembre-Blundo, D. (2025). Toward the Theoretical Foundations of Industry 6.0: A Framework for AI-Driven Decentralized Manufacturing Control. Future Internet, 17(10). https://doi.org/10.3390/fi17100455 [Google Scholar] [Crossref]

7. Jing, H., & Zhang, S. (2024). The Impact of Artificial Intelligence on ESG Performance of Manufacturing Firms: The Mediating Role of Ambidextrous Green Innovation. Systems, 12(11). https://doi.org/10.3390/systems12110499 [Google Scholar] [Crossref]

8. Kao, P. J. (2025). AI-augmented dynamic capabilities for business sustainability: Enactment through humanistic and instrumental approaches. Journal of Information Technology. https://doi.org/10.1177/02683962251392284 [Google Scholar] [Crossref]

9. Lee, C. (2024). Artificial Neural Networks (ANNs) and Machine Learning (ML) Modeling Employee Behavior with Management Towards the Economic Advancement of Workers. Sustainability (Switzerland), 16(21). https://doi.org/10.3390/su16219516 [Google Scholar] [Crossref]

10. Li, J., & Jin, X. (2024). The Impact of Artificial Intelligence Adoption Intensity on Corporate Sustainability Performance: The Moderated Mediation Effect of Organizational Change. Sustainability (Switzerland), 16(21). https://doi.org/10.3390/su16219350 [Google Scholar] [Crossref]

11. Lipovka, A., Buzady, Z., & Abeshev, K. (2025). MANAGERS’ SUSTAINABLE LEADERSHIP COMPETENCIES ACROSS HUNGARY, KAZAKHSTAN, AND TÜRKIYE: EFFECTS OF PERSONAL, ORGANIZATIONAL, AND INDUSTRY FACTORS. Problems and Perspectives in Management, 23(4), 467–485. https://doi.org/10.21511/ppm.23(4).2025.34 [Google Scholar] [Crossref]

12. Santos, M. R. C., Carvalho, L. C., & Francisco, E. (2025). A Capability-Based Framework for Knowledge-Driven AI Innovation and Sustainability. Information (Switzerland), 16(11). https://doi.org/10.3390/info16110987 [Google Scholar] [Crossref]

13. Schaffernak, I., Cecil, J., Kleine, A. K., & Lermer, E. (2025). Sociotechnical influences on the adoption and use of AI-enabled clinical decision support systems in ophthalmology: a theory-based interview study. BMC Health Services Research, 25(1). https://doi.org/10.1186/s12913-025-13620-w [Google Scholar] [Crossref]

14. Song, H. (2025). AI-Based Intelligent Supply Chains: Theoretical Exploration and Prospects. In Frontiers of Business Research in China (Vol. 19, Issue 3, pp. 261–279). Higher Education Press Limited Company. https://doi.org/10.3868/s070-020-025-0011-8 [Google Scholar] [Crossref]

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