 |
 |
The dissertation investigates the potentially transformative role of Artificial Intelligence in reimagining decision-making, programmatic learning, and knowledge democratization in the humanitarian and international development sectors. It focuses on how an AI-driven tool called Propel is adopted and implemented within three regional contexts: Europe, Africa, and Latin America. The implications are therefore that Propel will demonstrate how AI can enable resource allocation, adaptive management, and operational efficiencies for NGOs operating in such resource-constrained, crisis-prone, highly dynamic environments. It uses a qualitative research design that includes expert interviews, webinars, and organizational reports in reviewing the impact and challenges regarding the integration of AI-driven tools within NGO operations. The key observations are that: Propel can synthesize fragmented data into actionable insight through clustering techniques; Propel is GDPR compliant, among other things; and it is adaptable across diverse regional contexts. Challenges include infrastructural disparities, gaps in digital literacy, and overreliance on AI output. These also point to the need for capacity building on a continuous basis and ethical concerns. This dissertation further validates the theoretical underpinning of Propel through its Theory of Change and Community of Practice frameworks in building collaborative learning and knowledge sharing across regions. Whereas the potential of Propel to assure programmatic learning and informed decision-making is high, much may be required in terms of innovation and investment to scale this up and sustain it. The final contribution this research makes involves adding to the growing repository on AI for Social Good, with practical insights for NGOs, technology developers, and policy makers.