Artificial intelligence is rapidly transforming manufacturing industries worldwide, but a new study highlights the growing psychological challenges workers face during this transition, particularly in emerging economies. Researchers found that AI-induced job insecurity significantly increases employee burnout, while psychological safety and confidence in using AI technologies can help reduce these negative effects.

The study surveyed 208 employees working in AI-integrated manufacturing firms in Pakistan across sectors including textiles, surgical instruments, and leather manufacturing. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the researchers examined how AI-related job insecurity affects burnout and how psychological safety and AI self-efficacy influence employee resilience during technological change.

The findings revealed that employees who fear job displacement, skill obsolescence, or reduced career relevance due to AI adoption are significantly more likely to experience emotional exhaustion and disengagement. The study further demonstrated that psychological safety, employees’ ability to openly communicate concerns and seek support without fear of negative consequences, plays a critical role in reducing burnout.

Importantly, workers with higher AI self-efficacy, meaning confidence in their ability to understand and work with AI systems, were better able to cope with uncertainty and maintain psychological safety despite workplace disruption. The study found that AI self-efficacy weakens the harmful impact of job insecurity on employee well-being.

The researchers emphasize that organizations must move beyond simply implementing AI technologies and instead prioritize human-centered AI adoption strategies. Recommended approaches include workforce reskilling programs, transparent communication, ethical leadership, and psychologically safe work environments to help employees adapt to digital transformation.

The study contributes to growing global discussions on the human consequences of AI adoption and provides important insights for organizations and policymakers seeking to balance technological innovation with employee well-being in resource-constrained manufacturing environments.

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