AI-Based Decision Making in Strategic Talent Management: Balancing Organizational Efficiency and Ethics

Abstract

In the era of digital transformation, the integration of Artificial Intelligence (AI) has shifted the decision-making paradigm in talent management from an intuitive approach to a data-driven. While AI offers high efficiency in processing complex information, its use in talent management strategies raises important issues regarding ethics, transparency, and the potential for algorithmic bias that can harm an organization's perception of fairness. This research aims to explore how organizations can balance the demands of operational efficiency through AI with the need to maintain ethical and moral standards in talent management. The primary focus of the research is to identify the intersection between optimizing organizational capabilities and protecting employee rights and well-being. Using a descriptive qualitative approach (systematic literature review) with analyzed various AI implementation frameworks in global companies. The analysis examined mechanisms for performance assessment, employee potential identification, and succession planning, then compared them with principles of business ethics and distributive and procedural justice. The study's findings demonstrate that AI efficiency cannot be achieved without human oversight (human-in-the-loop). This research develops a decision-making framework model that integrates ethical values into the design of mechanisms to minimize bias. The practical contribution of this paper is to provide guidance for strategic stakeholders and organizational practitioners in adopting AI technologies that not only increase productivity but also strengthen employee trust and engagement through transparent and fair management practices.