TLDR: This research paper systematically reviews AI adoption in Non-Governmental Organizations (NGOs) between 2020 and 2025. It identifies six key AI use case categories: Engagement, Optimization, Decision-Making, Management, Creative, and Predictive. The study also outlines common challenges within the Technology–Organization–Environment (TOE) framework, including data availability, ethical concerns, financial constraints, lack of expertise, and environmental factors. Proposed solutions emphasize capacity building, pilot projects, open-source tools, and crucial partnerships with governments, industry, and academia. The review concludes that while AI holds significant potential, its adoption is uneven and biased towards larger organizations, highlighting the need for equitable support and ethical guidelines.
Artificial intelligence (AI) holds immense promise for non-governmental organizations (NGOs), offering new ways to maximize their limited resources for greater societal benefit. However, the adoption of AI within this sector is still in its early stages, marked by uneven progress and a need for clearer guidance. A recent systematic literature review by Janne Rotter and William Bailkoski from Universitat Pompeu Fabra sheds light on how NGOs are currently using AI, the challenges they face, and potential solutions.
The review, which analyzed 65 studies published between 2020 and 2025, found that AI adoption in NGOs is currently between the ‘early adopter’ and ‘early majority’ stages. This means that while there’s growing experimentation and some success stories, widespread integration is still developing. The researchers identified six primary categories for AI utilization in NGOs:
AI Use Cases in NGOs
- Engagement: AI tools like interactive chatbots are used to improve communication with beneficiaries, provide localized information to refugees, and personalize interactions with stakeholders.
- Optimization: AI helps streamline operations such as vaccine distribution, emergency medical services, humanitarian supply chains, and even sustainable packaging design. A significant application is in donation management, where AI enhances donor matching and fundraising effectiveness.
- Decision-Making: NGOs leverage AI for forecasting disease outbreaks, identifying high-risk populations, predicting air quality, analyzing food quality for donations, and informing resource deployment in crises.
- Management: AI assists with internal functions like resource allocation, financial risk mitigation, accounting, procurement, and even screening and hiring staff and volunteers.
- Creative: Generative AI is used for content creation, including donor thank-you notes, newsletters, grant proposals, press releases, and educational materials.
- Predictive: AI models forecast refugee shelter locations, migration patterns, conflicts, climate change impacts, natural disasters, and even eviction risks for vulnerable tenants.
The study highlights that the extent of AI application varies significantly with an NGO’s size and geographic location. Larger organizations, with their greater financial resources and IT infrastructure, tend to engage in more complex AI projects, including developing in-house solutions. Smaller NGOs, on the other hand, often adopt more accessible management and creative AI tools. Geographically, high-income countries with advanced technological infrastructure show higher readiness for AI integration, while low- and lower-middle-income nations present unique challenges and opportunities for localized AI solutions.
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Challenges and Solutions for AI Adoption
The review categorizes challenges using the Technology-Organization-Environment (TOE) framework:
- Technology Context: Issues include the availability and management of diverse and often incomplete data, critical ethical considerations (such as fairness, accountability, bias, and data privacy), and a general lack of trust in AI solutions among the public and within NGOs.
- Organization Context: Financial constraints are a major barrier, with limited budgets and intense competition for funding. There’s also a notable lack of awareness or willingness to adopt AI, often due to fear of job displacement and insufficient leadership support. A significant gap in expertise and skilled workforce, coupled with unclear organizational policies, further complicates adoption.
- Environment Context: External factors include inadequate legal frameworks, a lack of collaboration with academic, government, and industry partners, and the digital divide, which impacts internet penetration and access to technology in different regions.
To overcome these hurdles, the literature proposes several solutions. Capacity building, through improved data quality and comprehensive staff training, is crucial. Targeted funding and starting with small-scale pilot projects are recommended to build confidence and demonstrate value. The use of open-source and low-cost AI solutions can also help mitigate financial barriers. Furthermore, ensuring data anonymity, security through a privacy-by-design approach, and developing a unified, inclusive AI governance framework are essential for ethical implementation.
The most emphasized solution is the importance of strong partnerships and collaborations. This includes cooperation among NGOs to develop shared ethical frameworks, data sharing with government bodies, and working closely with technology companies and academia to develop tailored and effective AI solutions. Policymakers are encouraged to invest in digital infrastructure and create clear legal structures to guide AI adoption.
While AI offers a promising future for NGOs, its successful and equitable adoption requires a concerted effort from all stakeholders. The research underscores the need for a stepwise approach, starting with needs assessment, piloting projects, ensuring secure data governance, and scaling solutions through collaboration. This will help ensure that smaller NGOs and those in lower-income contexts are not left behind in the evolving landscape of AI for social good. You can read the full research paper here.
