Portfolio management is a critical facet of financial investing, and the rise of AI-driven advisory platforms offers investors novel opportunities for efficient, data-based asset allocation. This study explores the determinants of financial Robo-advisor adoption, focusing on perceived usefulness, social influence, and trust. Data were collected from 130 potential adopters and analyzed using partial least squares structural equation modelling (PLS-SEM). The findings indicate that perceived usefulness significantly boosts both trust in Robo-advisors and the likelihood of adoption. Social influence emerges as another vital predictor of adoption while exerting a statistically meaningful impact on trust. Moreover, trust is strongly associated with Robo-advisor adoption, and it partially mediates the effect of perceived usefulness and social influence on adoption intentions. These insights provide actionable guidance for Fintech companies and policymakers on cultivating trust-centric environments and leveraging social networks to foster broader acceptance of AI-driven portfolio management tools.