Â鶹¹û¶³´«Ã½

This appendix presents a quantitative analysis of how frequently specific implementation challenges appear across 53 case studies drawn from 13 curated repositories of AI for social impact in the Global South. Each case study was coded against nine challenge categories derived from the project’s framework. 

The table below shows how many of the case studies mention each challenge category and what percentage that represents.

Table A5 | Challenge Categories Mentioned in Case Studies

Note: Percentages reflect the share of case studies that describe each challenge as a substantive obstacle. One case study can be coded for multiple challenges.

Data

Data challenges are the most pervasive implementation barrier, appearing in 42 of 53 case studies (79 percent). These include limited access to locally relevant or high-quality training datasets, fragmented and siloed data, lack of Afrocentric or region-specific datasets, data quality issues (inconsistent imagery, incomplete registries), and high cost of data acquisition.

Social & Institutional Embeddedness

Social and institutional embeddedness challenges appear in 38 of 53 case studies (72 percent). AI systems consistently face friction when they require users to adopt new platforms, learn new interfaces, or change established workflows. Across sectors, successful deployments integrated AI into platforms users already inhabit—most commonly WhatsApp—or adapted to existing institutional workflows.

Infrastructure

Infrastructure challenges appear in 30 of 53 case studies (57 percent). Limited rural internet access constrains both end-user engagement and model training; unreliable electricity affects IoT-enabled AI and device-dependent health tools. Several projects adopted hybrid edge/cloud architectures to address low-bandwidth constraints. Energy infrastructure directly constrains deployment in sub-Saharan Africa, undermining both offline device use and cloud-based model training.

Capacity & Skills

Capacity and skills challenges appear in 24 of 53 case studies (45 percent). This includes end-user digital literacy gaps (especially among rural women and farmers), shortages of AI/ML professionals in the health and nonprofit sectors, inadequate curricula in Tier 2/3 cities, and the need for sustained training programs for frontline workers. Several cases highlight that even when AI tools are technically sound, adoption fails without companion capacity-building efforts. 

Trust

Trust and adoption resistance challenges appear in 22 of 53 case studies (42 percent). This ranges from concerns about the reliability of AI tools and skepticism among farmers about working with researchers to develop AI tools to professional resistance among radiologists and judicial staff fearing job displacement. Trust building is particularly important in contexts where AI tools serve as intermediaries in high-stakes decisions (health care, legal aid, financial services). 

Models

Model-related challenges appear in 20 of 53 case studies (38 percent). The dominant challenge is the underrepresentation of African, South Asian, and Indigenous Latin American languages, dialects, and cultural contexts in foundation models. 

Funding

Funding and sustainability challenges appear in 17 of 53 case studies (32 percent). Most organizations rely on philanthropic grants or donor funding with no clear path to commercial sustainability. Common concerns include the high cost of developing foundation AI models, long cycles for government adoption and cost-sharing, difficulty securing long-term concessional capital for infrastructure-intensive models, and short grant terms that terminate projects before they reach sustainability. 

Policy

Policy and governance challenges appear in 14 of 53 case studies (26 percent). This includes fragmented regulatory frameworks across African countries, evolving regulatory pathways for AI-enabled medical devices, data sovereignty constraints limiting cloud deployment, and the absence of data protection policies that create enabling environments for AI deployment.

Compute

Hardware and compute challenges appear in 7 of 53 case studies (13%). Key issues include the cost of GPU access and cloud compute licensing, dependency on donated compute credits, and pay-per-token LLM charges that make scaling expensive. While less frequently coded as a standalone challenge than other topics, compute constraints interact significantly with the data, models, and infrastructure challenges above.

Thirteen source documents were analyzed:

• (GSMA)—11 case studies
• —16 case studies
• —8 case studies
• —3 case studies
• —1 case study
• —1 case study
• —5 case studies
• —2 case studies
• —1 case study
• —1 case study
• —1 case study
• —1 case study
• —2 case studies

Case study repositories and sources were collected by the authors through an internet search of AI for social impact case studies in the Global South. This search yielded 13 sources that contained varying numbers of case studies (descriptions of organizations implementing AI). Each source was then uploaded in PDF form into Claude (Sonnet 4.6). All case study documents were read in full by Claude. An organization was included as a case study if the organization is located in the Global South and the document describes at least one concrete implementation challenge the organization faced. A topic was coded as present (1) if the text explicitly describes that topic as something the project struggled with, had to work around, or actively managed. A passing reference or positive mention without a described challenge does not qualify. Each case study can be coded for multiple topics. Coding criteria can be found in Section 4.3. 

Claude outputted a document with all coding decisions evidenced by specific quotes or paraphrases from the source text. Authors then uploaded this document into an advanced thinking Claude model (Opus 4.7) to fact check the outputted coding and the evidence and to flag both hallucinations and challenges that were present in the source text but not included in the output document. These hallucinations were removed and missing codes were added.

As a final validation metric, the authors then manually fact checked to validate the coding on 75% of cases. The authors confirmed that each coded piece of evidence in the document was, in fact, present in the source document. 

Nine challenge categories were defined and applied:

• Data: Challenges with data availability, quality, local relevance, annotation, bias, or collection. 
• Infrastructure: Challenges with internet access, bandwidth, electricity, power supply, or energy infrastructure that constrained training, deployment reach, or scale. Connectivity and energy are combined into this category. Projects that built offline solutions but still reported infrastructure affecting model training, deployment reach, or scale are coded as present.
• Models: Challenges related to using or adapting AI models—including Western-centric training data, poor performance in local languages, hallucination risks, and need for fine-tuning for local contexts. 
• Capacity & Skills: Challenges with workforce skills, digital literacy, end-user training, or shortage of AI/ML professionals in the development/nonprofit sector. Includes org team, end users, and government partner capacity.
• Funding: Cases where funding was uncertain/insufficient or where the project flagged difficulty with long-term sustainability, dependence on donor grants, or difficulty finding commercial models.
• Policy: Challenges with regulatory frameworks, governance structures, institutional barriers, data governance policies, data sovereignty laws, or regulatory uncertainty. Data governance is coded here, not under Data.
• Trust: Challenges with community resistance to using the AI tool itself or reluctance to trust AI-generated outputs.
• Compute: Challenges with GPUs, cloud access, computing costs, API call/token costs, cloud credits, or device availability (e.g., imported hardware).
• Social & Institutional Embeddedness: Challenges fitting AI into existing workflows, platforms, interfaces, or institutions. Includes friction from requiring users to adopt new platforms, interface/modality mismatches, and difficulty displacing legacy practices. For this topic, we noticed a fine line between organizations encountering it as a challenge and proactively working around it. For that reason, we coded this challenge as present if an organization mentioned integrating their solution into existing systems, even if they did not explicitly state it as a challenge.

• The sample is not random; case studies were selected for publication because they involve successful or notable implementations, which may underrepresent the most challenging or failed deployments.
• Cases from the health compendium and gender empowerment casebook were submitted to a government-organized summit, which may bias toward successful or government-affiliated projects.
• Challenge coding reflects what organizations chose to report, not necessarily the full range of challenges they faced.