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A Chapter of: Closing the Adoption Gap: How AI Is Being Built in the Global South

Appendix A: Findings Summary

Table A1 | Summary of Challenges and Solutions

Category Challenges Solutions
Compute
  • GPU and compute scarcity
  • Sovereignty policy traps
  • Frugal engineering: modular and edge compute, smaller models
  • Public compute
  • Focus on inference and fine-tuning, not frontier training
  • Regional collaborations and compute pooling
Models
  • Model relevance
  • Model dependency
  • Build and layer small local models onto proprietary models ("barnacles on the hull of Big Tech"?)
  • Open-source models
  • Model ecosystems
Data
  • Data representativeness (contextual, cultural, and linguistic fit)
  • Data accessibility (machine readable, technical and policy silos)
  • Distorted benchmarks and evaluation
  • Synthetic data
  • Pulling data from live APIs
  • Repurposing DPI data (with governance safeguards)
  • Community and participatory data collection
Infrastructure
  • Unreliable electricity
  • Limited or expensive connectivity
  • Bandwidth too low for voice, image, and video
  • Partnerships with telcos for zero-rating
  • Solar integrated hardware
  • "Portable infrastructure": self-contained AI solutions
Social & Institutional Embeddedness
  • Individual discomfort or lack of familiarity with tools
  • Institutional resistance or fear
  • Policy inertia that resists AI adoption
  • Deploy on existing platforms (especially WhatsApp)
  • Voice and image input
  • Work with existing institutions and communities to reach marginalized groups (Indigenous populations, gender)
  • Policy reform, including procurement and public sector mandates
Capacity & Skills
  • Lack of technical and AI skills
  • Lack of domain knowledge combined with technical skills
  • Brain drain
  • Embed technical staff in partner organizations, especially public sector
  • Partnerships with academia
  • Peer support agents and other community interventions
  • Talent-friendly policies
Trust
  • Misinformation concerns, job anxiety, skepticism that AI works
  • Privacy fears
  • Humans in the loop
  • Information accuracy
  • Addressing privacy
  • Trust as a "moving target"
Funding
  • Shortcomings of private sector funding (extractive, lead-generation)
  • Competing priorities for governments
  • Sustainability challenges ("pilotitis")
  • Transfer to government
  • Edge AI (to reduce costs)
  • Community licensing agreements
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Closing the Adoption Gap: How AI Is Being Built in the Global South

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Appendix A: Findings Summary

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