How to Build and Implement AI Products in the NGO Sector

Introduction

Artificial intelligence (AI) can change how non-governmental organizations (NGOs) operate and deliver support. By using AI tools, NGOs can simplify internal processes, make quicker and more informed decisions, and increase the impact of their programs.

However, adopting AI in the NGO sector requires more than adding new technology. It requires a product mindset and a careful look at the real needs of the communities being served. Without this approach, NGOs risk building solutions that do not match user needs or solve the underlying problem.

This article outlines the key steps for building and implementing AI products in the NGO sector. It also explains how NGOs can address common challenges and make the most of AI to improve outcomes and create meaningful, lasting change.

Why the NGO sector needs a product mindset

Eric Ries, Author of The Lean Startup:

“A core principle in product development is that a solution must solve a meaningful problem for the user. In the NGO sector, the “user” is the community being served. However, many NGO-driven solutions are created without enough feedback from these communities. As a result, the final product may not match the real needs or preferences of the people it was designed to help.”

Janti Soeripto, CEO of Save the Children US, explains this challenge clearly:

“In a commercial setting, customers choose whether to use a product, and their feedback is immediate. In contrast, communities supported by NGOs often have no alternative. Even if a service does not fully meet their needs, they may still rely on it. This lack of choice limits direct feedback and slows down innovation.”

To address this gap, NGOs can adopt product development practices such as:

• Continuous feedback from the community
• Testing solutions before scaling
• Accountability for outcomes
• Iterative improvement rather than one-time design

This mindset shift encourages NGOs to build solutions with communities, not just for them.

A good example of this approach comes from a conversation between Omdena’s CEO Rudradeb Mitra and Vishal Ghotge, the CEO of Kiva.org. Kiva has provided more than $1.88B in micro-loans to over 4 million people in 80 countries. Vishal explains that “growth is a moral obligation” when 1.4 billion people still lack access to formal financial services. To serve more people, NGOs need to think strategically about product development at scale.

This involves asking questions such as:

• What do people actually need and value?
• How do they make decisions about loans or services?
• How can we learn quickly and improve without delay?
• How do we enable communities to participate in designing the solution?

By adopting a product mindset, NGOs can build tools and services that are more relevant, more accessible, and more impactful for the communities they serve.

The role of AI 

AI can support NGOs in putting a product mindset into practice. By analyzing data and identifying patterns, AI helps organizations design solutions that match the real needs of the communities they serve. It also makes it easier to understand user behavior and adjust programs based on evidence rather than assumptions.

For example, an NGO offering mental health support could use AI to identify which age groups are most affected by specific stress factors. Similarly, an organization focused on disaster response can use AI to predict which regions are at higher risk and when preparation efforts should begin. In both cases, AI provides information that leads to more targeted and effective interventions.

AI can also help create real-time feedback loops. Instead of waiting months for evaluation reports, NGOs can collect ongoing input from users and make improvements quickly. This supports faster learning, better decision-making, and continuous refinement of products and services.

In addition, AI tools can help measure the impact of programs more accurately. By analyzing outcomes across different locations, time periods, or demographic groups, NGOs can understand what is working, what needs adjustment, and where to focus resources next.

By combining human insight with AI-driven analysis, NGOs can design solutions that are more relevant, timely, and aligned with community needs.

Let´s now look into the process of building an AI product.

1. Identifying the problem

Before building an AI product, it is essential to clearly understand the problem that needs to be solved. This starts with learning from the people who are directly affected. Their lived experience provides insight into what matters most, what is missing, and what solutions would be most useful in practice.

At Omdena, our local chapters play a key role in this step. By working directly with community members, we gain a deeper understanding of cultural context, daily challenges, and practical constraints. For example, our collaboration with GovTech Bhutan focuses on empowering local talent to build AI solutions that address Bhutan’s social challenges. When local voices shape the problem definition, solutions become more relevant and sustainable.

Data also helps clarify the problem. It can identify who is affected, where the needs are most urgent, and which factors contribute to the issue.
For example:

• A health-focused NGO may analyze data on maternal health to find the primary causes of maternal and infant mortality in a region.
• An NGO working on food security may map where food shortages are most severe and identify the economic or environmental factors driving them.

When NGOs combine community insights with data analysis, they can define the problem with clarity. This leads to AI products that are more targeted, impactful, and aligned with real needs.

Building resilience against hunger and malnutrition in Burkina Faso. Source: Flickr.com

2. Data collection and preparation

High-quality data is the foundation of any AI product. To build models that are accurate and useful, NGOs need data that reflects the real situations of the communities they serve. This includes both structured data (numbers, records, survey responses) and unstructured data (text, interviews, images, satellite data, etc.).

The data collection process depends on the problem and context. It may involve surveys, interviews, public datasets, satellite imagery, sensor feeds, or web scraping. However, many NGOs face challenges in accessing reliable data. Local data may be limited, outdated, missing, or difficult to collect.

In these cases, creative approaches can help fill the gaps.
For example:

• Web scraping can gather information from publicly available online sources.
• Data synthesis techniques can create new data by combining or simulating existing datasets.
• Transfer learning lets organizations use models already trained on similar problems, reducing the need for large datasets.

These methods help AI teams work efficiently even when data is scarce.

A practical example comes from Omdena’s collaboration with Impact Hub Istanbul. During a project to support earthquake response planning, our team combined street map data with satellite imagery to create a tool that helps families identify the safest route between two locations after an earthquake. This shows how thoughtful data collection can lead to solutions that directly support people’s safety and well-being.

By preparing and organizing high-quality data, NGOs can build AI products that are reliable, accurate, and better aligned with real community needs

In the project “Predicting the Safest Path during an Earthquake using AI Planning” partnered with Istanbul’s Impact Hub innovation center, Omdena data scientists combined satellite imagery of Istanbul with street map data in order to build a tool that facilitates family reunification by indicating the shortest and safest route between two points after an earthquake. Source: Omdena

How to overcome data access challenges? 

Access to data is a significant challenge for AI teams working on real-world problems. However, this challenge can be overcome by employing innovative approaches to collecting and augmenting the data. AI teams can use techniques such as web scraping, data synthesis, and transfer learning to overcome data scarcity issues. 

For example, web scraping can be used to gather data from publicly available sources, while data synthesis can help generate new data by combining existing datasets. Transfer learning can also be used to train models on similar but not identical datasets to increase the volume of available data. By leveraging these innovative data collection and augmentation techniques, AI teams can develop more accurate and robust models that can address complex real-world problems.

The article “Overcoming Data Challenges through the Power of Truly Diverse Teams” explores how diverse teams can help organizations overcome data roadblocks to successfully tackle real-world problems through AI. 

3. Choosing the right AI model

Once the problem and data are clear, the next step is choosing the right AI model. Different models work well for different types of problems, so selecting the correct one is essential for building an effective solution.

There are many types of AI models. For example:

• Neural networks can analyze images or patterns in large datasets.
• Decision trees help explain why a prediction was made, which is useful for transparency.
• Support vector machines are helpful for classifying data into clear categories.

The best model depends on the goal of the project and the kind of data available.

For example:

• An NGO working to detect illegal fishing could use a neural network to analyze satellite images and recognize suspicious activity at sea.
• An NGO predicting the spread of a disease may use a decision tree to analyze case data and highlight where risks are increasing.

Selecting the right model is not only a technical task. It also involves ethical judgment and community awareness.

What are the ethical considerations we need to address?

AI must support, not harm, the communities it is meant to help. Good intentions are not enough. Even well-meaning organizations can build models that reinforce bias or exclude the very people they want to serve. As NGOs adopt AI solutions, they must balance innovation with responsible implementation. The guide on unlocking the power of GenAI and AI agents for NGOs outlines practical steps to ensure ethical safeguards remain central throughout development.

We have seen this happen:

• Amazon developed a recruitment algorithm that favored male candidates.
• Twitter’s photo-cropping AI showed biases in image selection.

These outcomes likely had no harmful intent behind them. They happened because real users were not part of the development process.

The most ethical AI is built by teams that:

• Include diverse perspectives
• Test solutions with real users
• Listen to feedback and make changes
• Share responsibility for outcomes

A strong example comes from Omdena collaborator Erum Afzal from Pakistan. She worked on multiple AI projects addressing issues such as online child safety and economic development, while also training students. Her work shows how local talent can shape ethical, community-centered AI. Today, she leads Omdena School and is pursuing a Ph.D. in Education Sciences in Germany.

When NGOs involve local experts and community members, they build solutions that are more responsible, trusted, and impactful.

Discover the inspiring story of Erum Afzal from Pakistan, she has successfully completed multiple Omdena Challenges. She has joined a wide range of topics, ranging from preventing online violence against children to analyzing the role of connectivity on economic and human development. Alongside her impressive contributions to the field of educational technology, she has also been actively engaged in teaching and training students. Her outstanding efforts eventually led her to become the Head of Omdena School. Currently, Erum is pursuing her Ph.D. at the Institute of Educational Sciences at Justus Liebig University Giessen.

Read more: From Omdena Collaborator to Head of OmdenaSchool, to a Ph.D Scholarship in Germany

4. Implementation and impact

Once an AI product is built, the next step is to implement it in the real world. A successful rollout is gradual, practical, and guided by user feedback. Instead of launching the solution everywhere at once, it is better to begin with a small pilot. This allows teams to test the product in a real environment, identify what works, and make improvements quickly.

Effective implementation often involves partnerships. NGOs can collaborate with local organizations, governments, or community groups to increase adoption and ensure the product reaches the people who need it most. Accessibility is also key. The AI solution must be easy to use, clearly explained, and supported with training when needed.

Measuring impact is essential. NGOs should track how the product changes behavior, improves outcomes, or simplifies work. This can include:

• Collecting feedback from community members or frontline workers
• Comparing results before and after the product’s introduction
• Measuring time saved, accuracy gained, or reach expanded

Impact measurement shows whether the AI solution is actually solving the problem and where it can be improved.

A strong example comes from Omdena’s work with Catholic Relief Services. In this collaboration, AI models were developed to help identify and support families at risk of poverty. By scaling these models, the organization improved its ability to target resources and strengthen local support programs. The project demonstrated how AI, when implemented thoughtfully, can directly enhance social impact.

When NGOs apply careful rollout strategies and ongoing evaluation, AI products become tools that genuinely help communities. They move from experiments to meaningful, sustainable solutions.

Overall, the challenges of building AI products for the NGO sector are outweighed by the immense positive impact these solutions can have. With the right approach, collaboration, and a commitment to ethics, NGOs can build AI solutions that change the world for the better.

Read more: Omdena Talent Helps Catholic Relief Services Create and Scale AI Algorithms That Address Poverty