Artificial Intelligence Adoption in Qatar’s Financial Sector: Drivers of Readiness, Trust, and Fairness
TL;DR
The results show that organizational readiness is the strongest predictor of adoption intention, followed by technology readiness and the regulatory environment, while perceived trust partially mediates the relationship between readiness and intention.
Abstract
This study examines the determinants of artificial intelligence adoption intention in Qatar’s financial sector by integrating individual, organizational, and regulatory perspectives within a mixed-methods sequential explanatory design. Drawing on survey data from 215 validated responses collected from financial professionals across conventional, Islamic, foreign, and FinTech institutions, the analysis employs partial least squares structural equation modeling (PLS-SEM) to test the effects of technology readiness, organizational readiness, regulatory environment, perceived trust, and fairness perception on intention to adopt AI. The results show that organizational readiness is the strongest predictor of adoption intention, followed by technology readiness and the regulatory environment, while perceived trust partially mediates the relationship between readiness and intention. Multi-group analysis further reveals heterogeneity across bank types and levels of AI exposure, with stronger effects observed in FinTech and digitally oriented institutions. The findings suggest that AI adoption in Qatar depends not only on technical capability, but also on institutional preparedness and ethical legitimacy. The study contributes to the literature on financial innovation by extending readiness-based models to a Gulf context shaped by state-led modernization, regulated experimentation, and the growing need for responsible AI governance.