Nigerian Social Protection coverage is limited by booming population, political economy, sociocultural structure, corruption, infrastructure, etc. This makes the Nigerian case unique and highly germane to the overall focus of AI FORA. The Nigerian state is the unit of analysis in this study. This study obtained data from interviews and focus group discussions with SP experts who provided the author with a deep understanding of context dependencies of actors within the Nigerian SP environment. The research strategy adopted made room for understanding social, economic, political, and environmental pressures that drove the discourse on social assessment of technology use in SP in Nigeria. Several works of literature were reviewed and used as sources of secondary data on supply and demand sides of Nigerian SP. Triangulation of data helped to create robust data for this study. Findings from this study show that AI and related technologies were useful in producing high-resolution poverty maps for both Nigerian urban and rural areas, improving the targeting of social safety net program (SSNEP) beneficiaries. General impact assessment dominates genuine social assessment of SP and limits development of robust SP interventions in Nigeria. The future use of AI should extend the technology beyond targeting beneficiaries to include other parts of the SSNEP delivery chain.