AI-ENABLED TALENT OPTIMIZATION: TRANSFORMING WORKFORCE PLANNING AND DEVELOPMENT
DOI:
https://doi.org/10.65009/e952sg40Keywords:
Artificial Intelligence, Capability Forecasting, Data-Driven HR, Employee Development, Machine Learning, Natural Language Processing, Predictive Analytics, Skill Mapping, Talent Management, Talent Optimization, Workforce Analytics, Workforce Planning,,Abstract
AI-Enabled Talent Optimization is redefining modern workforce planning and
employee development by enabling data-driven, adaptive, and predictive human resource
strategies. As organizations face rapidly evolving skill demands, dynamic market conditions,
and increasingly diverse work environments, artificial intelligence offers a transformative
approach to understanding, managing, and enhancing human potential. This research explores
how AI-powered analytics, machine learning models, predictive workforce simulations, and
intelligent talent-matching systems can optimize recruitment, performance evaluation, skill
development, and succession planning. By leveraging large-scale employee data, AI systems
can identify capability gaps, forecast future skill requirements, personalize learning pathways,
and enhance role alignment to improve productivity and engagement. The study also examines
the integration of AI-driven decision support tools for strategic workforce planning, enabling
organizations to proactively manage talent risks and build resilient, future-ready teams.
Furthermore, ethical considerations, transparency challenges, and the need for human-AI
collaboration are analyzed to ensure responsible implementation. Overall, this research
demonstrates that AI-enabled talent optimization not only enhances organizational efficiency
but also supports continuous employee growth, equitable opportunities, and long-term
competitive advantage, marking a significant shift toward intelligent, evidence-based human
resource management.
References
] Vhora, R., Bhandwalkar, S., & Rege, N. (2024). AI-driven HR analytics: Enhancing
decision-making in workforce planning. International Journal of Data Science and
Analytics. Retrieved from ResearchGate.
] Kalusivalingam, P., Kumar, A., Sharma, D., Patel, R., & Singh, S. (2020). Machine
learning algorithms for workforce planning and optimization. International Journal of
Artificial Intelligence and Machine Learning, 1(3), 1–15.
] Inaganti, S., Ravichandran, S., Nersu, K. K., & Muppalaneni, N. B. (2021). AI and
predictive analytics for enhanced workforce planning. Applied Intelligence and
Machine Learning Review, 2(1), 45–62.
] Murmu, S. (2025). AI-driven talent management and its implications for future HR
practices. International Journal of Management, 13(2), 50–62.
] Rege, N., Vhora, R., & Bhandwalkar, S. (2024). The impact of AI-powered HR
analytics on strategic workforce planning. International Journal of Research in HRM,
(4), 122–137.
] Kumar, V., Meena, A., Santhanalakshmi, S., Srinu, P., & Rajpoot, D. (2024). Machine
learning and HRM: A path to efficient workforce management. Journal of HR Analytics
and AI, 4(2), 77–89.
] Hema Sundari, M., Matheshkanna, S., Prabhu, M., & Meenakshi, S. (2025). Artificial
intelligence in human resource management: A systematic literature review and human
centered framework. Journal of Business and Technology Research, 15(1), 1–20.
] Mohamed, A., Al-Jarroudi, N., & Khan, S. (2025). Unveiling the potential of artificial
intelligence in human resource management: Adoption strategies, challenges, and
future directions. Cureus Journal of Management and Technology, 9(1), 1–18.
] Suswaram, P. (2024). Predictive workforce planning using AI and machine learning.
SSRN Electronic Journal.
]
https://doi.org/10.2139/ssrn.5344088
]
Bositkhanova, A., Sadykova, G., Nurbayeva, Z., & Ryskaliyeva, G. (2025).
Artificial intelligence in strategic workforce planning: A systematic literature review.
Journal of Workforce Strategy, 3(1), 1–14.
]
Babashahi, M., Taleghani, M., & Maleki, M. (2024). Impact of artificial
intelligence on workforce skills: A systematic review. Administrative Sciences, 14(6),
]
Nosratabadi, S., Zahed, A. N., Ponkratov, V. V., & Kostyrin, E. A. (2022).
Artificial intelligence applications in human resource management: A systematic
review. arXiv preprint arXiv:2209.07335.
]
Ma, S., Liu, H., Zhao, Y., & Tukhvatulina, L. (2024). Predicting employee
attrition using large language models: A GPT-based approach. arXiv preprint
arXiv:2411.01353.
]
Selvamohana, V., Sahu, R., Singh, D., Mohanraj, A., & Sharma, R. (2025).
From HR analytics to AI-driven HRM: Implications for workforce productivity.
Journal of Information Systems & Emerging Markets, 9(2), 200–215.
]
Joglekar, P. (2025). Role of AI and analytics in driving HR innovation and
sustainable financial growth in organizations. Journal of Management Science Review,
(1), 85–102.
