Occupational Vulnerability to AI-Driven Change: The Role of Skill Composition, Task Structure, and Psychosocial...

Automation risk is often framed as a question of which jobs can be automated. This perspective neglects how the structure of work itself shapes workers' vulnerability to AI-driven change. In occupational health research, job characteristics such as autonomy, task variety, and social interaction are known to influence worker well-being, yet are rarely incorporated into assessments of AI-related occupational risk.

To capture this complex vulnerability, this study distinguishes between Automation (AI) Exposure and Psychosocial Buffering capacity. We analyzed 664 U.S. occupations across 128 skill dimensions using K-means clustering, an unsupervised machine learning algorithm, to identify groups of occupations with similar skill profiles. AI Exposure was quantified using the Artificial Intelligence Occupation Exposure (AIOE) score, and the Psychosocial Buffer Index (PBI) captured protective job features relevant to occupational health. Principal Component Analysis (PCA) was used to examine the relationships among AI Exposure, PBI, and occupational skill composition.

Five distinct occupational clusters emerged, systematically differentiating themselves in both AI exposure and buffering capacity. High AI exposure clusters varied in PBI, suggesting that structural job features can mitigate automation-related vulnerability, while low-exposure roles with limited buffering may still face occupational stress. PCA results revealed that AI exposure aligns with cognitive intensity, whereas PBI shows independent dispersion across the skill space.

These findings highlight that AI-related occupational risk is multi-dimensional and indicates the relation between automation potential and the job structure. This framework provides actionable insights for targeted interventions, including retraining, workflow redesign, and collaborative AI integration to support worker well-being in an AI-driven labor market.