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AbstractThe development of high-potential employees is paramount for long-term success, innovation, and organisational expansion. Traditional approaches to talent identification generally use subjective measures, which are prone to biases, inconsistencies, and inefficiencies. This research tries to explore the use of artificial intelligence (AI), mainly machine learning (ML) algorithms, in identifying and developing with higher accuracy and objectivity, the so-called high-potential members of staff. By analyzing behavioural metrics, organisational inputs, and past performance data, AI models can predict potential leading to high future performance and thus identify hidden patterns of talent. The study evaluates the efficiency of various machine learning techniques such as decision trees, random forests, and neural networks with real HR datasets while pondering over ethical issues, data privacy, and integration with existing HR practices for AI applications. The results help outline a framework for working companies willing to deploy smart systems for talent development and display applications of AI in improving talent management. Keywords: artificial intelligence, machine learning, workforce analytics, predictive analytics, and high-potential personnel.IntroductionHuman capital is one of the truest factors responsible for the success of an organization in the current-day, fast-paced, and highly competitive world of business. Uniqueness is what high-potential employees possess in that they are leaders-in-the-making, innovators, and strategic thinkers for change-keeping the leadership pipeline filled with continuity. Identifying and developing HiPos has thus become a strategic advantage and a business necessity for organizations amid more and more disruptions, such as the global talent shortage and fast technological advancement.HiPos were mostly identified qualitatively through subjective assessments, performance management, and nomination of managers. Although these methods expose quite a bit of information, they are often restricted in terms of scalability and are prone to cognitive bias, rendering inconsistent results. For instance, a manager could bias hisher decision in favor of some workers just because they are alike in background or style, which unfairly disregards or downplays the value of an unconventional or diverse talent with merit for a remarkable journey forward. Artificial Intelligence (AI) provides a great solution for the same.Using enormous amounts of structured and unstructured data, AI analyses employee behaviour, forecasts future performance, and mines hidden patterns that may not be obvious using traditional means. To provide an objective, data-driven evaluation of employee potential, AI systems analyse a much wider range of factors, includingBackground and Issue of the High-PotentialEmployees (HiPos) AwardMoreover, in todays fast-paced, innovation-driven business landscape, organizations need a ready pipeline of capable leaders to deliver on strategic imperativesand solve complex problems. High-Potential Employees (HiPos) are thoseemployees whom are high-performers in their multiple areas of expertise, they demonstrate strong leadership capability, and have potential to take on more senior or critical-sized roles. Finding and developing these talents iskey to long-term organizational success, continued leadership and agility in a competitive global market.From what we can see at the Corporate Executive Board (CEB), HiPos are substantially more valuable91%to organizations and make the biggest difference in achieving organizational results. As a result, many organizations do not see or make the most of these skills which often leads to losing talent, giving such skills to the wrong person and neglecting their importance

Copyright © 2025 Mr. J.sivakumar swamy1, . This is an open access article distributed under the Creative Commons Attribution License.