Stakeholder perceptions of artificial intelligence for climate adaptation in Somalia: Opportunities, readiness barriers, and community resilience

Artificial intelligence (AI) is increasingly recognized as a transformative tool for climate change adaptation owing to its capacity for sophisticated data analysis, predictive analytics, and real-time environmental monitoring. This study examines stakeholder perceptions of AI's potential to enhance climate resilience in fragile Somalia, which faces recurrent droughts, floods, desertification, water scarcity, and weak institutional capacity. Using a descriptive quantitative research design, primary data were collected from 353 respondents across government institutions, non-governmental organizations, academia, and the private sector through a structured online questionnaire. The findings reveal that stakeholders broadly perceive AI as valuable for improving early warning mechanisms (M = 3.97), strengthening disaster response (M = 3.95), supporting agricultural decision-making (M = 4.03), and monitoring environmental degradation (M = 3.86). However, the strongest consensus across all respondents was the recognition that Somali communities lacked the technical capacity to utilize AI-based tools (M = 4.17), followed closely by financial constraints (M = 3.95) and infrastructure limitations (M = 3.66). This “high demand -- low preparedness” paradox constitutes the central contribution of the study, challenging the linear assumptions of traditional technology adoption models and extending Technology Adoption Theory and Resilience Theory to fragile state contexts. The study proposes the concept of ‘Readiness-Adjusted Technology Adoption’ as a framework for understanding how institutional and infrastructural readiness moderate technology adoption in fragile states. The findings demonstrate that perceived usefulness alone is insufficient to drive AI adoption without complementary investments in institutional capacity, digital infrastructure and human capital. This study provides empirical evidence that AI applications developed in high-resource environments cannot be directly transferred to fragile states without substantial contextual adaptation. Practically, the research offers actionable recommendations for policymakers, donors, NGOs, and the private sector, emphasizing low-cost, mobile-first AI solutions (e.g., voice-based alerts in Somali, SMS-based climate information), community-based digital literacy programs, participatory design approaches that integrate traditional knowledge, and governance reforms to enhance multi-stakeholder coordination in the region. These findings underscore that while AI holds significant potential to enhance climate adaptation in Somalia, realizing this potential requires coordinated investments in infrastructure, human capacity, and institutional reforms as the essential foundation for scalable and context-sensitive implementations.