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AI Impact Project: Mapping Opportunities for AI in the Global South
Mission
The AI Impact Project aims to identify practical and responsible ways artificial intelligence can support social entrepreneurs across the Global South. By working directly with community leaders, innovators, and grassroots organizations, the research seeks to uncover where AI technologies such as generative AI, computer vision, data analytics, and IoT can deliver real world impact. The goal is to translate emerging technologies into accessible tools that strengthen communities, improve livelihoods, and support sustainable development
Outcomes
The research will produce the AI Impact Wheel, a practical framework that maps high impact opportunities for AI adoption across key social sectors. By combining data driven analysis with insights from social innovators working on the ground, the project identifies where AI can most effectively improve education, healthcare, agriculture, climate resilience, and community development. These findings will guide future projects, partnerships, and capacity building efforts aimed at responsible and inclusive AI deployment
Research Overview
The AI Impact Project is a joint research initiative between Aiforgood Asia and NELIS Global designed to understand how artificial intelligence can be applied to solve real world challenges in developing regions. The study focuses on the Global South, where social entrepreneurs are already working on the frontlines of issues such as education access, climate resilience, healthcare delivery, and economic inclusion. By gathering insights directly from these changemakers, the research ensures that AI solutions are grounded in practical needs rather than theoretical assumptions.
Using a mixed method research approach, the project collected both quantitative and qualitative data from social entrepreneurs across more than fifteen countries. The questionnaire explored current technology usage, barriers to AI adoption, and opportunities for practical deployment across sectors such as education, agriculture, healthcare, and environmental conservation. Statistical analysis methods including factor analysis and clustering were applied to identify patterns and high potential areas where AI could create meaningful
RESEARCH COLLABORATORS
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