AI-Driven Performance Management: Enhancing Objectivity and Efficiency - Basalamah, Indira; P, Muhammad Carda (Journal of Economics and Management Scienties, 2025)
Traditional performance management systems are frequently criticized for subjectivity, inconsistency, and delayed feedback. To address these limitations, organizations are increasingly adopting Artificial Intelligence (AI) to enable real-time, data-driven employee evaluations. While AI enhances objectivity and operational efficiency, its deployment introduces several critical challenges. These include algorithmic bias rooted in historical data, opacity in decision-making logic, employee concerns about digital surveillance, and organizational resistance to automated appraisal systems. This article presents a systematic review of scholarly literature and enterprise case studies published between 2020 and 2024 to examine how AI is reshaping performance management practices. Four core themes are identified: bias mitigation, feedback automation, ethical risks, and large-scale implementation. The analysis reveals that AI can improve evaluation accuracy and responsiveness—particularly in hybrid and digital-first environments—when accompanied by transparency, ethical oversight, and human interpretability. Rather than replacing managerial judgment, AI should serve as an augmentation tool within a human-centered performance ecosystem.