This study investigates AI anxiety among 62 Polish workers using a mixed-methods approach. Applying Wang and Wang’s (2022) AI Anxiety Scale (AIAS) and thematic analysis, we compared anxiety levels between automation-prone occupations (e.g., retail) and resilient ones (e.g., teaching). Key results include: (1) the significant difference between the means (72.0 vs. 54.3, \(p < .05\) ) suggests that high-risk workers report 32% greater anxiety, especially concerning job loss; (2) anxiety was driven by perceived lack of control, not technical complexity; and (3) digital literacy moderated anxiety levels. This research contributes by (a) mapping occupation-specific AI anxiety, (b) revealing psychological barriers to AI acceptance, and (c) proposing anxiety-aware design principles, including transparent skill mapping and adaptive interfaces. Findings confirm both research questions (RQ1) and the hypothesis (H1), indicating that occupational risk correlates with elevated AI anxiety. The study emphasizes that effective AI adoption requires emotional, not only technical, readiness, especially in vulnerable job sectors. These results inform workforce policy, education, and AI design. Future use of the AIAS framework may support cross-national comparisons of AI anxiety in European labor markets.