This study examines barriers to adopting AI-enabled human resource management (AI-HR) in Zanzibar’s public and private sectors, focusing on how adoption dynamics in resource-constrained, mandatory settings diverge from those theorized in developed economies. An explanatory sequential mixed-methods design was used: a structured survey of HR professionals and managers (n = 133) was followed by semi-structured interviews (n = 15) conducted to explain unexpected quantitative results. Partial Least Squares Structural Equation Modeling (PLS-SEM) tested an integrated Technology-Organization-Environment and Technology Acceptance Model (TOE-TAM) framework. Only technological readiness predicted AI-HR adoption readiness (β = 0.203, p = 0.047); organizational readiness, environmental readiness, perceived usefulness, and perceived ease of use were non-significant. The organizational readiness construct itself exhibited measurement failure negative Cronbach’s alpha (α = -0.105) and opposing outer loadings across items a pattern substantively consistent with institutional decoupling manifesting at the measurement level, where management support and responsiveness to change operated as opposing rather than unified organizational dimensions. The qualitative phase identified four mechanisms behind these null results: vendor lock-in and external IT control; digital and energy infrastructure functioning as a threshold rather than a continuous predictor; institutional decoupling between formal policies and operational practice; and cultural resistance to automating HR judgment. These mechanisms not visible to survey instruments challenge the assumed universality of the Technology Acceptance Model in mandatory, resource-constrained public sector contexts and indicate that AI-HR implementation requires resolving hard infrastructure deficits before organizational and attitudinal levers become operative. The study offers a refined adoption model for Small Island Developing States and practical guidance for Zanzibar’s digital transformation agenda.