Case studies across key impact areas
AI supports nonprofits in multiple ways, from analyzing poverty trends to detecting misinformation, forecasting disasters, and protecting ecosystems. These examples combine AI tools and use cases with real case studies to show how these technologies work in practice.
Advocacy
| AI tool / Use case | Application / How it's used |
|---|---|
| Sentiment analysis (NLP) | Scans social media, news, and public commentary to detect narratives, misinformation, and rising issues. |
| Data analytics for program evaluation | Enables analysis of programmatic feedback from communities served, which can be used to inform program activities. |
| GenAI for content creation | Produces reports, donor communications, digital campaign assets, and social media content at scale. |
| Donor targeting and engagement | Predicts donor likelihood to give, flags high-risk donors, powers personalized outreach. |
These real-world examples show how AI tools are applied in this impact area.
The Africa Infodemic Response Alliance (AIRA) is a collaborative initiative led by the World Health Organization (WHO) to tackle the problem of infodemics. It brings together inter-governmental and non-governmental organizations working in public health, as well as fact-checking and media organizations. As WHO states, an infodemic occurs when there is an overwhelming amount of information during a public health emergency, including false or misleading details. AIRA combats the spread of false information and provides accurate and timely health data across Africa.
AIRA's key activities include listening to narratives online and offline, providing technical support to African governments to strengthen their information ecosystems, producing health literacy, debunking and prebunking social media content, and continuously monitoring the impact of its work. AIRA use AI for monitoring and analyzing vast amounts of data from online sources (news articles, social media) and from community-based sources to identify misinformation trends and public concerns.
AI can significantly amplify the impact of organizations working in the nonprofit sector by automating routine tasks, enhancing data-driven decision-making, and optimizing resource allocation.
Disaster response
| AI tool / Use case | Application / How it's used |
|---|---|
| Geospatial AI | Conducts rapid damage assessment in the immediate aftermath of a disaster by analyzing satellite imagery for floods, earthquakes, landslides, and conflict, generating insights that inform response and recovery efforts. |
| Aid matching algorithms | Matches humanitarian aid donors to local needs verified by on-the-ground organizations. |
| Predictive modeling (early warning) | Forecasts disasters using environmental and historical data to preposition humanitarian aid. |
| Chatbots for crisis assistance | Provides real-time multilingual information and intake. |
These real-world examples show how AI tools are applied in this impact area.
The Pacific Disaster Center provides insight and innovative tools to support early warning, emergency preparedness, and disaster risk reduction. Its DisasterAWARE platform provides alerting for hazards and tools that support decision makers to prepare for and manage disaster events.
DisasterAWARE is augmented by the PDC's AI for Humanity which enables a much faster processing of huge amounts of hazard monitoring data, and is also capable of recognizing patterns, creating new data, and providing additional insights. It utilizes a variety of AI technologies-machine learning (ML), natural language processing (NLP), generative AI (GenAI), and large language models (LLMs).
Conservation
| AI tool / Use case | Application / How it's used |
|---|---|
| Remote sensing and AI drones | Monitors habitat, deforestation, and wildlife via automated imaging and detection. |
| Predictive modeling | Models climate impacts, species distribution, habitat suitability, and risks such as deforestation to inform conservation priorities, adaptation strategies, and resilience planning. |
| Real-time monitoring | Detects poaching activity, ecological changes, or equipment faults instantly. |
| Resource management | Optimizes ranger deployment, equipment maintenance, and routine conservation tasks. |
These real-world examples show how AI tools are applied in this impact area.
The World Wildlife Federation developed Forest Foresight, an innovative AI-driven technology for preventing illegal deforestation. The tool is an early warning system that forecasts human-induced biodiversity loss by combining geo data with radar-based satellite imagery to predict changes in landscapes. Its data-driven modelling capabilities can predict forest loss up to six months prior with 80 percent accuracy.
By detecting certain patterns of changes and alerting stakeholders, the tool enables local actors to intervene to prevent deforestation.