Title: Adoption Versus Innovation: Assessing Uganda's Readiness to Bridge the Global Generative AI Divide
Authors: Dr. Arinaitwe Julius, Ahumuza Audrey
Volume: 10
Issue: 4
Pages: 201-210
Publication Date: 2026/04/28
Abstract:
This study examined Uganda's readiness to adopt and potentially innovate with Generative Artificial Intelligence (GenAI) technologies within the broader context of the global AI divide. Employing a mixed-methods cross-sectional survey design with a stratified random sample of 384 respondents drawn from public sector institutions, private enterprises, and civil society organisations across five regions of Uganda, the study assessed five readiness dimensions: digital infrastructure, human capital and digital literacy, policy and regulatory environment, institutional capacity, and data governance. Data were analysed using univariate descriptive statistics, bivariate correlation analyses, and Structural Equation Modelling (SEM). Findings revealed that Uganda's overall AI readiness score stood at a mean of 3.07 out of 5.00, indicating a marginally positive but fragile preparedness for GenAI integration. Digital infrastructure (M = 3.12) and human capital deficits (M = 3.48) emerged as the most critical bottlenecks, while the policy environment (M = 3.38) showed moderate promise. SEM results confirmed that digital infrastructure (? = 0.41, p < 0.001), human capital (? = 0.35, p < 0.001), and policy environment (? = 0.28, p < 0.01) significantly predicted GenAI adoption readiness, which in turn strongly predicted uptake intention (? = 0.62, p < 0.001). The model achieved acceptable fit (CFI = 0.96, RMSEA = 0.048). The study concludes that Uganda currently occupies an adoption-leaning position on the adoption-innovation continuum, with constrained infrastructure and skills gaps limiting its capacity to move beyond technology consumption toward domestic GenAI innovation. Strategic investments in broadband connectivity, targeted AI literacy programmes, and a coherent national AI policy framework are urgently recommended to bridge the growing generative AI divide.