Artificial intelligence anxiety, employee performance, and demographic differences in India’s BFSI sector

This study investigated artificial intelligence (AI) anxiety, its association with employee performance, and its demographic distribution among employees in India’s Banking, Financial Services, and Insurance (BFSI) sector. Drawing on Cognitive Appraisal Theory, Conservation of Resources Theory, and the Job Demands–Resources Model, the research utilised a cross-sectional quantitative design with a sample of 349 BFSI employees recruited through an online survey. The Artificial Intelligence Anxiety Scale (AIAS) [62] and the Individual Work Performance Questionnaire (IWPQ) [36] were employed as validated psychometric instruments. Pearson correlation analysis revealed a statistically significant negative relationship between AI anxiety and employee performance. Hierarchical multiple regression demonstrated that AI anxiety significantly predicted employee performance beyond demographic controls, accounting for meaningful additional explained variance. Female employees reported significantly higher AI anxiety than male employees. A statistically significant qualification-level effect on AI anxiety was identified at the omnibus level, however, no pairwise contrast reached significance following post hoc correction. No significant differences in AI anxiety were found across job levels. These findings contribute to the theoretical understanding of AI-related psychological strain in the workplace and offer practical implications for AI transition management, gender-responsive human resource practices, and workforce policy in India’s digitally transforming BFSI sector.