AI-Enabled Change Management and Employee Readiness: A Systematic Literature Review

Mario Calvine; Purwanto Purwanto

doi:10.69693/ijim.v4i2.894

Authors

Mario Calvine Universitas Esa Unggul
Purwanto Purwanto Universitas Esa Unggul

DOI:

https://doi.org/10.69693/ijim.v4i2.894

Keywords:

Change Management, AI-Enabled HRM, Digital Change Fatigue, AI Learning Self-Efficacy, Algorithmic Fairness

Abstract

This systematic literature review synthesized Scopus-indexed evidence on change management in human resource management during AI-enabled organizational transformation. The review addressed a fragmented body of knowledge in which AI-enabled HRM, digital change fatigue, AI learning self-efficacy, perceived algorithmic fairness, and perceived AI transparency are often examined separately, although they jointly shape employee readiness. Five Scopus CSV datasets were merged, deduplicated by DOI and normalized title, and screened using PRISMA 2020 logic. From 3,562 initial records, 88 duplicate records were removed, 2,080 records were screened at the title and abstract stage, 119 reports were assessed for eligibility, and 50 journal or review studies were retained for synthesis. The findings show that AI-enabled change management is not merely a technology implementation issue but a socio-technical HRM challenge. Training, leadership communication, employee participation, and human-centered HR practices strengthen AI learning self-efficacy and employee readiness. Conversely, digital overload, technostress, AI-induced stressors, algorithmic control, and burnout intensify resistance. Perceived algorithmic fairness and AI transparency strengthen trust by reducing opacity and procedural uncertainty in recruitment, performance appraisal, platform work, and managerial decision-making. This review contributes an integrative framework linking HRM practices, self-efficacy, readiness, trust, resistance, and change success, and it encourages longitudinal and cross-sector research on employee-centered AI adoption.

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