Report by Akash Kapur, and Gillian Rosenberg: “The AI conversation is dominated by a preoccupation with frontier capabilities. Yet a more consequential challenge is the gap between what AI can do and what it is actually doing in the world. This adoption gap is particularly acute in the Global South, where AI is deployed amid resource constraints, limited state capacity, and fragile institutions. This report sets out to map these adoption challenges, in order to understand how AI can be better embedded into existing workflows and social structures. Drawing on interviews, surveys, and case studies, it builds this case through granular, ground-up evidence, pushing back against the abstraction that so often characterizes the field.
Key Findings
- AI adoption is fundamentally a problem of alignment with real-world systems, not model capability; contextual fit matters more than benchmark performance.
- Adoption is a whole-of-society challenge that cannot be resolved at the technical layer alone.
- Constraint is generative as well as limiting: Resource scarcity is producing distinctive forms of innovation—frugal engineering, modular compute, edge deployment, participatory data collection—that may have relevance well beyond the Global South.
- Adoption gaps are unevenly distributed and tend to compound for already excluded populations, particularly women…(More)”.
