AI-Driven Performance Management: Enhancing Objectivity and Efficiency - Basalamah, Indira; P, Muhammad Carda (SAFE-Network, 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.