The AI Impact Initiative

AI Insights and Opportunities from Community Leaders Across the Global South

Executive Summary

The AI Impact Initiative was established by Aiforgood Asia and NELIS Global to better understand where artificial intelligence can create practical, positive impact across communities in the Global South, and what barriers are preventing wider adoption. Drawing on 611 responses from community leaders, social entrepreneurs, educators, health practitioners, and other frontline actors across 56 countries, this study provides a broad evidence base on how AI is currently understood, where it is seen as most useful, and what forms of support are needed to enable responsible deployment.

The findings show that interest in AI is already significant. 44.4% of respondents report that they are already using AI in some form, while a further 29.6% are actively considering adoption. Familiarity is strongest with generative AI, followed by computer vision and natural language processing. This indicates that many respondents are not approaching AI as an abstract future concept, but as a set of tools with practical relevance to their work.

The survey also identifies the core challenges communities are facing day to day. Access to quality education and unemployment emerged as the most widely cited concerns, followed by climate-related issues, healthcare, agriculture and food security, financial inclusion, and public safety. These findings show that the demand for AI is closely tied to existing social and development priorities rather than to technology interest alone.

When asked where AI could create the greatest positive impact, respondents ranked healthcare and education as the highest priorities, followed by agriculture. Across the qualitative responses, AI was most often described not as a replacement for human work, but as a tool that could strengthen and extend ongoing efforts in service delivery, outreach, and decision-making. This is a notable finding. For many of the leaders represented in this study, the central issue is not whether AI should replace jobs, but whether useful tools can be made accessible in ways that support the work already underway in their communities.

At the same time, the report makes clear that adoption is being constrained by structural barriers. Cost is the most significant obstacle, followed by limited technical skills, infrastructure limitations, and internet connectivity. Respondents also identified data privacy, regulatory uncertainty, and community trust as important concerns, showing that interest in adoption is accompanied by serious consideration of responsible use.

The support needs identified in the survey are clear. Training was the strongest priority by a large margin, followed by funding, technical expertise, peer collaboration, access to tools and platforms, local language capability, computing resources, and open datasets. Taken together, these findings suggest that successful adoption will depend not only on access to technology, but on a broader ecosystem of practical support that enables communities to evaluate, adapt, and use AI in context.

This report provides an evidence base for the next phase of the AI Impact Initiative, including the development of the AI Impact Wheel, practical pilot opportunities, expanded training pathways, and stronger partnerships to support community-led AI adoption. The findings point to a clear conclusion: across the Global South, there is already a meaningful appetite for AI where it can address real human needs. What is required now is not more abstract discussion of potential, but practical support that helps communities move from interest to implementation in ways that are responsible, locally relevant, and sustainable.

Background

Artificial intelligence presents a major opportunity to accelerate progress in education, healthcare, agriculture, and economic empowerment. Yet its benefits are not shared equally. AI development remains concentrated in wealthier countries, built on datasets that reflect high-income environments, and deployed primarily in markets where purchasing power already exists. As a result, many of the communities where AI could generate the greatest human impact remain underrepresented in the spaces where these systems are designed, funded, and deployed. Without deliberate intervention, this imbalance risks deepening the very inequalities that social entrepreneurs across the Global South are working to address.

Aiforgood Asia and NELIS Global established the AI Impact Initiative to address this underrepresentation directly. The partnership combines Aiforgood Asia’s applied research expertise and practical experience deploying AI projects in real-world community settings with NELIS Global’s network of community leaders and social entrepreneurs through the One Million Leaders Fellowship. This combination reflects a core premise of the initiative: identifying effective solutions requires both technical rigour and genuine community insight.

This study examined a direct question: where and how can AI create the greatest positive impact for communities across the Global South, as defined by the people living and working within those communities? A structured survey was distributed through the One Million Leaders Fellowship network to capture AI awareness, current challenges, barriers to adoption, and the forms of support that would make deployment more practical and sustainable. In total, the study gathered 611 responses from social entrepreneurs, educators, health workers, and community leaders across 56 countries, making it one of the most extensive studies of its kind. These findings provide the evidence base for the development of the AI Impact Wheel, which is intended to guide future decisions on training, partnerships, and investment.

The Participants 

The 611 respondents to this survey represent a significant cross-section of social entrepreneurship across the Global South. They include directors and founders, educators and doctors, engineers and youth leaders, researchers and community organisers. Many are operating under real constraints, including post-conflict settings, remote regions, and communities with limited infrastructure. What unites them is a commitment to practical, ground-level impact.

AI engagement across this group is already underway. 44.4% of respondents report already using AI in some form, while a further 29.6% are actively considering adoption. Familiarity is strongest with generative AI, which scored an average of 3.68 out of 5, followed by computer vision at 2.92 and natural language processing at 2.74. This is not a group encountering AI for the first time. It is a group that already understands the technology’s potential and is positioned to move further with the right support.

Geographic Reach

The survey reached respondents across 56 countries, with particularly strong representation from South Asia, Southeast Asia, and Sub-Saharan Africa. The largest concentrations came from Nepal, Pakistan, Indonesia, Afghanistan, and Sri Lanka, reflecting the strength of the One Million Leaders Fellowship network and its engagement with communities working on education, livelihoods, public health, and social development.

This survey reflects the breadth and diversity of social entrepreneurship across the Global South. It captures a wide range of local contexts, institutional realities, and development priorities. This diversity strengthens the findings by showing that the opportunities and barriers identified in this report are not isolated to a single geography, but are visible across multiple regions and community settings.

Figure 1. Survey Responses by Country

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This map shows the geographic distribution of survey responses across 56 countries, illustrating broad participation across the Global South. Darker shading indicates higher response counts, with stronger concentrations in countries such as Pakistan, Nigeria, and Nepal, while representation remains visible across Africa and Asia. Detailed results can be explored in the interactive map linked below.

Interactive Map of Survey Responses

Sector Representation

41.7% of respondents work in the education sector, reflecting the central role that learning plays in community development across these regions. Environment accounts for 15.7% of respondents, followed by health at 14.1%, agriculture at 9.7%, and governance at 4.7%. A further 11.1% work across areas that do not fit a single category, including microfinance, engineering, and community services. Together, these sectors represent key areas of frontline social impact work and align closely with domains where AI has strong potential to support practical community outcomes.

Figure 2. Respondents by Sector

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This chart shows the sector distribution of survey respondents and the fields in which they primarily work. Education represents the largest share of the sample, followed by environment, health, and agriculture, providing useful context for interpreting the findings in this report.

The Community Challenge Landscape

The survey asked respondents to identify the most pressing daily challenges facing their communities.

Access to quality education was cited by 68.6% of respondents, making it the most widespread challenge across all regions and sectors. Unemployment followed at 65.0%, pointing to where a significant share of community effort and attention is concentrated. Climate-related issues were identified by 52.2%, affordable healthcare by 49.9%, and reliable agriculture and food security by 43.2%. Financial services and inclusion (41.6%) and public safety (39.6%) complete the picture.

These challenges do not exist in isolation. Poor educational outcomes can limit employment prospects. Food insecurity is often intensified by climate stress. Limited access to healthcare places additional pressure on households already affected by poverty. Many of the communities represented in this survey are dealing with several of these pressures at once, often with limited resources and infrastructure.

Qualitative responses reinforce these findings. Respondents described building learning programmes for children without access to school, developing agricultural tools for smallholder farmers, training women in remote regions for work in the green economy, supporting communities adapting to climate change, and running health awareness campaigns where preventable diseases remain common. These examples reflect practical efforts already underway, often with limited support.

The relevance of AI to these challenges is direct. Education, healthcare, agriculture, climate resilience, and financial inclusion are all areas where practical AI applications already exist and can be deployed at scale. The main constraints are not technological, but related to access, investment, and localisation. Taken together, these findings provide a practical basis for identifying where support is most needed.

Figure 3. Most Pressing Community Challenges

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This chart ranks the daily challenges most frequently identified by respondents. Access to quality education and unemployment emerge as the most widely cited concerns, followed by climate-related issues, healthcare, and food security, providing a clear view of where community needs are most acute.

Where AI Can Create the Greatest Impact

A central goal of this research was to understand where community leaders believe AI could create the greatest positive impact. Respondents were asked to rank five sectors in order of priority, with lower average rank values indicating higher overall priority across the sample.

Healthcare and education emerged as the joint top priorities, with very similar overall rankings. Healthcare was ranked first by 29.8% of respondents and had an average rank of 2.56, while education was ranked first by 37.6% and had an average rank of 2.57. As noted earlier, 41.7% of respondents work in the education sector, which may influence these results. This should be considered when interpreting the findings.

In healthcare, respondents identified opportunities ranging from diagnostics and support for community health workers to health data analysis and patient outreach. Across the responses, the consistent view was not that AI would solve every health challenge, but that appropriately selected tools, grounded in relevant local data, could meaningfully extend the reach of already overstretched health systems. The value lies in matching the right tool to the right need, with communities themselves helping define which needs should be addressed and how AI can be applied in practice.

Agriculture ranked third, with 27.8% of respondents placing it first. Respondents described practical applications including crop monitoring, pest diagnosis, weather prediction, and market access. Community development followed at 24.5%.

Conservation ranked last of the five areas, with 20.6% of respondents placing it first and an average rank of 2.70. While environmental concerns featured strongly in the community challenges section, respondents did not prioritise conservation as the primary domain for AI deployment. This likely reflects the more immediate priority given to education, health, and food security in their day-to-day work.

A consistent theme across the qualitative responses was that respondents do not view AI as a replacement for human work. They see it as a tool that can strengthen and extend the work already underway in their communities. This is a significant finding. Much of the broader debate around AI and labour displacement centres on white-collar and knowledge work. For many of the community leaders represented in this survey, the more immediate concern is not replacement, but access to practical tools that can support education, healthcare, agriculture, and environmental resilience.

The breadth and specificity of this dataset position it as a credible evidence base for a wide range of partners. The documented needs and priorities of 611 community leaders across 56 countries make a strong case for targeted support from development agencies, philanthropic foundations, corporations, multilateral institutions, NGOs, and other partners working on digital equity and sustainable development.

Figure 4. Perceived Priority Sectors for AI-Enabled Community Impact

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This chart shows how respondents ranked the sectors where they believe AI could create the greatest positive impact. Education, healthcare, and agriculture emerge as the highest priorities, reflecting where respondents see the strongest potential for practical, community-level deployment.

Barriers to AI Adoption

Although interest in AI is strong across the respondent community, several clear barriers continue to limit wider adoption.

Cost was identified as the primary barrier by 42.4% of respondents. Many indicated that the financial requirements associated with AI tools, infrastructure, and implementation remain beyond the reach of their organisations. Social enterprises often operate with limited budgets, making investment in new technologies difficult without external support.

Limited technical skills ranked second, cited as the primary barrier by 32.6% of respondents. Many respondents expressed interest in using AI but do not yet have the knowledge required to build, deploy, or manage AI systems. This reinforces the need for accessible, practical training, as reflected elsewhere in this report.

Technology access and infrastructure followed closely at 31.4%, while internet connectivity was cited by 29.3% of respondents as a primary constraint. Reliable connectivity, suitable hardware, and access to computing resources are not consistently available across many regions, making even relatively simple AI applications difficult to implement. Advances in edge computing and lightweight AI models may help reduce some of these dependencies, and this remains an area worth further exploration in future research and implementation efforts.

Respondents also raised concerns related to trust, ethics, and responsible use. Data privacy (27.3%), regulatory uncertainty (22.1%), and community trust (20.3%) were each cited as primary barriers by a meaningful share of respondents. This suggests that respondents are thinking carefully about how AI should be adopted, not simply whether it should be adopted.

Overall, the findings point to structural rather than motivational barriers. The communities represented in this study show clear interest in AI and a practical understanding of where it could be useful. What remains missing is the enablement needed to move from awareness to implementation, including training, tools, infrastructure, and support.

Figure 5. Primary Barriers to AI Adoption

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This chart shows the issues most frequently ranked as the biggest barriers to AI adoption. Cost emerges as the most significant obstacle, followed by limited technical skills and infrastructure constraints, highlighting that the main barriers are structural rather than motivational.

Capacity Building Needs

Respondents were asked what forms of support would most help them adopt AI in their work.

Training was the most frequently requested form of support, identified by 86.4% of respondents. Interest in AI is genuine and widespread, but many community leaders do not yet have access to practical learning pathways that translate technical concepts into real-world applications. This is one of the clearest and most actionable needs identified in the study, and one where cloud providers and ecosystem partners could play a meaningful supporting role through training, credits, and technical enablement.

Funding followed at 64.0%. For organisations operating with limited budgets, even modest investment in tools or pilot projects remains out of reach without external backing. Technical expertise was close behind at 63.2%, reflecting the need for experienced practitioners who can guide implementation and help translate ideas into working systems.

Peer collaboration (53.0%) and access to AI tools and platforms (51.4%) were each selected by more than half of respondents, pointing to both the social and practical dimensions of adoption. Around a third of respondents also identified AI models that work in local languages (39.9%), computing resources (37.3%), and open datasets (35.8%) as important needs, underlining that the infrastructure gap is both real and specific.

These findings suggest that isolated pilots are unlikely to succeed without a broader support ecosystem. Respondents are not asking for a single tool or a one-off workshop. They are asking for a more coordinated approach that combines training, funding, technical guidance, local language capability, and the partnerships needed to turn knowledge into sustained practice. This is where the AI Impact Initiative can play a practical role by connecting evidence, community needs, and implementation support.

This is particularly relevant for the communities represented in this study. Advances in small language models and edge AI are expanding what can be deployed without large-scale cloud infrastructure. Models that previously required significant computing resources can increasingly run locally, on modest hardware, and in some cases without continuous internet connectivity. The cost of deploying practical AI tools in low-resource environments is falling, and that shift may create new opportunities for adoption more quickly than many policy and funding systems are prepared for.

The infrastructure barriers that limit adoption today may look different in the near future. Building awareness, skills, and community readiness now is therefore not preparation for a distant possibility, but for a transition already beginning to take shape. Communities that are better prepared to evaluate and apply these tools will be in a stronger position to benefit as access improves.

Figure 6. Support Needed to Enable AI Adoption

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This chart shows the forms of support respondents identified as most necessary for effective AI adoption. Training emerges as the strongest need, followed by funding and technical expertise, underscoring that successful adoption depends not only on access to technology, but also on the broader support ecosystem required to use it well.

Conclusion

This study brings together the perspectives of community leaders, educators, health practitioners, farmers, and social entrepreneurs across 56 countries. Their responses provide a grounded picture of where artificial intelligence stands in relation to the work already underway across the Global South.

The findings reveal both readiness and constraint. Interest in AI is strong across education, health, agriculture, climate, and conservation. Respondents described AI not as a replacement for human effort, but as a tool to extend the reach of work already in progress. What holds most of them back is not ambition but access: to training, technical expertise, and the financial resources needed to move from interest to implementation.

Through the partnership between Aiforgood Asia and NELIS Global, the AI Impact Initiative is designed to close that gap. Working directly with community leaders through the One Million Leaders Fellowship, the initiative deepens understanding of where AI can genuinely serve and builds the capacity of those best placed to lead its adoption. This survey is both a reflection of where things stand and a foundation for what comes next: practical pilots, expanded access to training, and the partnerships needed to support responsible AI deployment in the communities where the return on investment, measured in human outcomes, would be greatest. The leaders represented in this study are already moving. For sponsors and partners, the opportunity is not to create momentum but to join it at a moment when the direction of that momentum can still be shaped.

Disclaimer on Use of AI and Research Support Tools

This report reflects the research design, survey methodology, analytical approach, interpretations, and conclusions developed and overseen by Aiforgood Asia as part of the AI Impact Initiative, conducted in partnership with NELIS Global.

The survey instrument, research framework, and analytical direction were developed by Aiforgood Asia in collaboration with the leadership network of the One Million Leaders Fellowship. All findings are based on responses from 611 participants across 56 countries, gathered through the fellowship network and reviewed, cleaned, and analysed under the supervision of the research team.

AI writing tools were used as assistive aids during the preparation of this report. ChatGPT (model 5.3) and Claude Sonnet (4.6) supported the structuring of draft text, clarity of language, and editorial refinement, based on analysis and direction provided by the research team. These tools were not used to design the research methodology, generate survey questions, independently analyse the dataset, or determine the findings of the study.

All interpretations, findings, and conclusions were reviewed and validated by Aiforgood Asia prior to publication. The organisation takes full responsibility for the accuracy of the information presented and the integrity of the analysis contained within this report.

Jesse Arlen Smith

President & Co-Founder

Aiforgood Asia

David Bergendahl

Co-Founder

Aiforgood Asia

Research Supporter