AI-Driven Transformation and Its Human Side: Employee wellbeing and Managerial Practices in Indian SMEs

Abstract

Objective: This study explores the impact of artificial intelligence (AI) adoption on employee wellbeing in Indian small and medium-sized enterprises (SMEs). The aim is to examine how AI adoption creates both job resources and job demands, and how managerial practices influence the balance between these outcomes.

Methodology: A mixed-methods approach was employed. Quantitative data were collected through surveys from 450 employees across 120 SMEs, and qualitative insights were drawn from 20 semi-structured interviews. Structural equation modeling (SEM) was used to test the proposed framework, supported by reliability and validity checks, correlation analysis, and moderation analysis.

Findings: AI adoption has a dual effect: it boosts job resources, such as efficiency, upskilling, and task enrichment, but also creates demands, including technostress, job insecurity, and increased monitoring. Resources improve employee wellbeing; demands harm it. Managerial practices such as training, transparent communication, and participatory leadership can reduce risks and enhance benefits. Sectoral and demographic differences are evident—manufacturing workers and older employees tend to feel more insecure, while younger workers perceive more growth opportunities. Qualitative themes support these findings, highlighting the managerial support's role in shaping perceptions.

Implications: The study provides theoretical contributions by extending the Job Demands–Resources model and sociotechnical systems perspective to Indian SMEs. It also offers practical and policy insights on managing AI adoption through inclusive training, responsible implementation, and workforce readiness initiatives.

Originality/Value: By focusing on SMEs in a developing economy, this study enriches the literature on AI adoption and employee well-being, highlighting the importance of a human-centered approach to digital transformation.