Artificial Intelligence in Human Resource Management: A Systematic Review of Its Influence on Employee Satisfaction

Abstract

Artificial intelligence (AI) is steadily changing how organisations manage their people, from the moment a resume is uploaded to the day an employee reads their latest performance review. Yet as AI tools such as chatbots, predictive analytics and automated screening spread across HR departments, it is still not fully clear how these tools shape the way employees feel about their jobs. This paper addresses that question through a systematic review of secondary data. Peer-reviewed articles published mainly between 2019 and 2026 were sourced from Scopus, Web of Science, ScienceDirect, Emerald, Springer and Google Scholar, and eighteen studies covering recruitment, training, performance management, engagement and workforce analytics were examined in depth using thematic analysis. The review finds that AI tends to raise employee satisfaction when it removes repetitive work, personalises learning and speeds up feedback, but it can just as easily damage trust when employees feel watched, judged unfairly by an algorithm, or left out of decisions that affect them. Four recurring patterns emerge from the literature: efficiency gains, personalisation gains, fairness concerns, and governance gaps. Building on these patterns, the paper proposes a conceptual framework linking AI-based HR practices to employee satisfaction and, through it, to organisational performance. The review adds to the growing conversation on responsible AI in HRM and offers HR managers a realistic, evidence-based starting point for deciding where AI genuinely helps and where a human hand still needs to stay close by.