Revolutionizing Talent Scouting with AI and Blockchain
Year: 2025
Type: article
Abstract: Identifying and nurturing emerging talent requires effective scouting mechanisms, yet traditional methods suffer from significant limitations such as subjective evaluations, lack of transparency, and ineffective progress monitoring. These challenges hinder the fair and efficient identification of high-potential individuals, particularly in fields like artificial intelligence, blockchain, and other cutting-edge technologies. This study presents a novel platform that leverages AI analytics and Blockchain tokenization to overcome the constraints of conventional talent identification and investment methods. The platform fosters fairness, transparency, and real-time progress tracking while enabling seamless collaboration between talents, investors, and industries. At its core, the system combines machine learning algorithms for predictive analysis and talent ranking, Blockchain-based tokenization for secure investments, and dynamic milestone monitoring for real-time progress tracking. For simplicity and scalability, Blockchain is confined to the token system, while the MERN stack supports frontend and backend functionalities. Preliminary findings demonstrate that AI-driven ranking methods provide 95% accuracy in identifying high-potential talents, while Blockchain ensures secure and tamper-proof investment tracking. Dynamic milestone monitoring introduces short-term rewards for investors, enhancing engagement and confidence in the system. By addressing the fundamental issues of accountability, fairness, and transparency, this platform has the potential to revolutionize talent discovery and investment. It sets a new standard for stakeholder collaboration and aligns with the growing industry demand for expertise in emerging technologies, positioning itself as a future-ready solution for talent scouting. Keywords: Talent Scouting, AI Analytics, Blockchain Tokenization, Predictive Analytics, Milestone Monitoring, Transparent Investments.
DOI: 10.55041/ijsrem43025
Author: Dr. Chittineni Aruna
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408848924