Harnessing artificial intelligence for disaster resilience in Uganda to enhance early warning systems and community recovery strategies

Purpose Uganda faces increasing natural disasters, including floods, landslides and droughts, which strain conventional disaster management systems. This study aims to examine the potential of artificial intelligence (AI) to enhance disaster resilience by strengthening early warning systems, response coordination and postdisaster recovery mechanisms. Design/methodology/approach A qualitative research design was used. Data were collected through 18 in-depth interviews with disaster management professionals, policymakers and community leaders, complemented by document analysis of national disaster frameworks and emerging AI applications. Thematic analysis was used to identify key patterns. Findings Findings reveal that while digital disaster monitoring initiatives exist, AI adoption remains limited due to infrastructure constraints, low technical capacity and funding challenges. However, participants emphasized AI’s strong potential to improve flood prediction accuracy, automate resource allocation, enhance damage assessment and support community engagement through mobile-based alert systems. Practical implications The study provides recommendations for integrating AI into Uganda’s disaster management framework, including investments in digital infrastructure, local technical training and strategic public–private collaboration to strengthen adaptive response systems. Originality/value The study offers a localized perspective from a developing-country context, and contributes to the emerging discourse on AI-driven disaster resilience and highlights practical pathways for technology adoption in low-resource environments.