AI-driven poverty interventions across Sub-Saharan Africa often fail to reach marginalized groups despite transformative promises, as technologies designed elsewhere clash with local social structures and community priorities. This study examines how integrating development sociology principles into AI design improves equity and adoption outcomes and identifies the underlying mechanisms explaining this relationship. We conducted a realist synthesis guided by the CIMO framework, analyzing 68 studies across agriculture, health, finance, and governance sectors in 22 Sub-Saharan African countries.

The findings reveal five categories of sociological principles documented in practice: participatory design, community ownership, indigenous knowledge integration, power analysis, and structural inequality focus. These principles operate through six generative mechanisms—trust-building, ownership, cultural congruence, power redistribution, capacity building, and relevance enhancement—that interact and reinforce each other. Mechanism activation depends critically on contextual conditions, including institutional capacity, infrastructure, gender relations, and historical context, explaining why the same intervention succeeds in one setting and fails in another.

The synthesis yields twelve context-sensitive design propositions in if-then form connecting intervention choices to expected outcomes through identified mechanisms. Limitations include reliance on published literature, potentially over-representing successful cases, and exclusion of evidence in African languages. Findings imply that genuine community participation, not tokenistic consultation, is essential, and that context assessment must precede intervention design. We conclude that embedding development sociology at the center of AI development—through community data control, early engagement with marginalized groups, and attention to structural inequalities—produces more equitable and sustainable outcomes. Policymakers should require genuine participation standards, prioritize community data ownership, and fund extended engagement rather than technology delivery alone.