This study investigates the configurations of managerial and organizational conditions that foster successful artificial intelligence adoption in the hospitality sector. Focusing on South Tyrol, a tourism region dominated by family-run, small-sized, leisure-oriented hotels, the research examines how awareness, investment in training, perceived customer impact and data security combine to shape AI implementation decisions. Quantitative data were collected from 76 hotel professionals through a structured questionnaire. A Fuzzy-Set Qualitative Comparative Analysis was employed to model causal complexity and identify sufficient configurations for both adoption and non-adoption. The findings reveal a single core configuration driving high levels of adoption: high AI awareness combined with strong perceived customer impact and attention to data security. In contrast, non-adoption is associated with two configurations: low awareness despite attention to security and a combination of low investment and low perceived impact in contexts where security remains salient. Across all solutions, AI awareness emerges as the pivotal determinant, while data security acts as a stabilizing peripheral condition. Sensitivity analyses confirm the robustness of these patterns under alternative calibration thresholds. The study contributes to the literature by demonstrating equifinal paths to both AI adoption and resistance and offers practical guidance for managers: awareness campaigns and hands-on training are especially valuable when they are tailored to the specific constraints of small, independent hotels.