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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 50 |
| Published: October 2025 |
| Authors: Aditya Pandey, Shivansu Pasi, Saurabh Patel, Swati Joshi |
10.5120/ijca2025925864
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Aditya Pandey, Shivansu Pasi, Saurabh Patel, Swati Joshi . Developing a Scalable and Ethical AI-Driven System for Smart Talent Allocation in Organizational Workforce Management. International Journal of Computer Applications. 187, 50 (October 2025), 29-36. DOI=10.5120/ijca2025925864
@article{ 10.5120/ijca2025925864,
author = { Aditya Pandey,Shivansu Pasi,Saurabh Patel,Swati Joshi },
title = { Developing a Scalable and Ethical AI-Driven System for Smart Talent Allocation in Organizational Workforce Management },
journal = { International Journal of Computer Applications },
year = { 2025 },
volume = { 187 },
number = { 50 },
pages = { 29-36 },
doi = { 10.5120/ijca2025925864 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2025
%A Aditya Pandey
%A Shivansu Pasi
%A Saurabh Patel
%A Swati Joshi
%T Developing a Scalable and Ethical AI-Driven System for Smart Talent Allocation in Organizational Workforce Management%T
%J International Journal of Computer Applications
%V 187
%N 50
%P 29-36
%R 10.5120/ijca2025925864
%I Foundation of Computer Science (FCS), NY, USA
The innovative corporate environment of 2025 requires smart, adaptable workforce management systems that can meet the sophisticated operational demands of hybrid and remote teams. This article introduces the first phase of the Smart Talent Allocation framework, an AI-driven system optimized to align employees with projects by utilizing skills, availability, area expertise, and predictive analytics. The prototype includes a machine learning-powered customized recommendation engine, allowing seamless task assignment via an easy manager dashboard and giving employees a simple interface to manage tasks and skills. The architecture is built for scalability and flexibility across various organizational frameworks, setting a strong foundation for future development, such as AI-based resume parsing, personalized upskilling routes, and AI ethics-driven measures to reduce bias. In this phase-by-phase strategy, the vision is to optimize workforce productivity, reduce skill mismatches, increase operational excellence, and support forthcoming trends like skills-based recruitment, internal mobility, and AI-fair human resource management (HRM).