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AI Adoption in NGOs: A Systematic Literature Review

Janne Rotter, William Bailkoski

TL;DR

Problem: NGOs face uneven AI adoption and fragmented evidence. Approach: a systematic literature review of $65$ English-language studies from $2020$ to $2025$ guided by the Technology-Organization-Environment framework and the Diffusion of Innovations, using PRISMA-2020 and SPIDER, with narrative/thematic synthesis. Findings: NGOs use AI in six categories—Engagement, Creativity, Decision-Making, Prediction, Management, and Optimization—with adoption concentrated among larger organizations and higher-income contexts; challenges span data governance, finance, ethics, and governance, while solutions emphasize partnerships, capacity-building, and governance. Significance: provides a practical roadmap for NGOs to overcome barriers and sustain social impact through phased adoption, open collaboration, and localized AI deployments.

Abstract

AI has the potential to significantly improve how NGOs utilize their limited resources for societal benefits, but evidence about how NGOs adopt AI remains scattered. In this study, we systematically investigate the types of AI adoption use cases in NGOs and identify common challenges and solutions, contextualized by organizational size and geographic context. We review the existing primary literature, including studies that investigate AI adoption in NGOs related to social impact between 2020 and 2025 in English. Following the PRISMA protocol, two independent reviewers conduct study selection, with regular cross-checking to ensure methodological rigour, resulting in a final literature body of 65 studies. Leveraging a thematic and narrative approach, we identify six AI use case categories in NGOs - Engagement, Creativity, Decision-Making, Prediction, Management, and Optimization - and extract common challenges and solutions within the Technology-Organization-Environment (TOE) framework. By integrating our findings, this review provides a novel understanding of AI adoption in NGOs, linking specific use cases and challenges to organizational and environmental factors. Our results demonstrate that while AI is promising, adoption among NGOs remains uneven and biased towards larger organizations. Nevertheless, following a roadmap grounded in literature can help NGOs overcome initial barriers to AI adoption, ultimately improving effectiveness, engagement, and social impact.

AI Adoption in NGOs: A Systematic Literature Review

TL;DR

Problem: NGOs face uneven AI adoption and fragmented evidence. Approach: a systematic literature review of English-language studies from to guided by the Technology-Organization-Environment framework and the Diffusion of Innovations, using PRISMA-2020 and SPIDER, with narrative/thematic synthesis. Findings: NGOs use AI in six categories—Engagement, Creativity, Decision-Making, Prediction, Management, and Optimization—with adoption concentrated among larger organizations and higher-income contexts; challenges span data governance, finance, ethics, and governance, while solutions emphasize partnerships, capacity-building, and governance. Significance: provides a practical roadmap for NGOs to overcome barriers and sustain social impact through phased adoption, open collaboration, and localized AI deployments.

Abstract

AI has the potential to significantly improve how NGOs utilize their limited resources for societal benefits, but evidence about how NGOs adopt AI remains scattered. In this study, we systematically investigate the types of AI adoption use cases in NGOs and identify common challenges and solutions, contextualized by organizational size and geographic context. We review the existing primary literature, including studies that investigate AI adoption in NGOs related to social impact between 2020 and 2025 in English. Following the PRISMA protocol, two independent reviewers conduct study selection, with regular cross-checking to ensure methodological rigour, resulting in a final literature body of 65 studies. Leveraging a thematic and narrative approach, we identify six AI use case categories in NGOs - Engagement, Creativity, Decision-Making, Prediction, Management, and Optimization - and extract common challenges and solutions within the Technology-Organization-Environment (TOE) framework. By integrating our findings, this review provides a novel understanding of AI adoption in NGOs, linking specific use cases and challenges to organizational and environmental factors. Our results demonstrate that while AI is promising, adoption among NGOs remains uneven and biased towards larger organizations. Nevertheless, following a roadmap grounded in literature can help NGOs overcome initial barriers to AI adoption, ultimately improving effectiveness, engagement, and social impact.
Paper Structure (24 sections, 6 figures, 10 tables)

This paper contains 24 sections, 6 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Theoretical Background
  3. Methodology
  4. Primary Search
  5. Selection Process
  6. Secondary Search
  7. Quality Assessment
  8. Data Extraction
  9. Data synthesis and analysis
  10. Results
  11. Characteristics of included studies
  12. Utilization of AI in NGOs (RQ1)
  13. Challenges and Solutions in AI Adoption (RQ2)
  14. Challenges in Technology Context
  15. Challenges in the Organization Context
  16. ...and 9 more sections

Figures (6)

  • Figure 1: Pipeline for this SLR. Note that this graphic is inspired by shams2025ai
  • Figure 2: PRISMA flowchart as defined by Page et al.page2021prisma
  • Figure 3: Visualization of publication dates and research methodology
  • Figure 4: Overview of identified use cases of AI in NGOs
  • Figure 5: Summary of identified challenges
  • ...and 1 more figures