AI Insights

How to Build and Implement AI Products in the NGO Sector

May 9, 2023

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Artificial intelligence (AI) has the potential to transform the way non-governmental organizations (NGOs) operate and deliver their services. By leveraging AI products, NGOs can streamline their operations, maximize their impact, and make data-driven decisions. However, implementing AI in the NGO sector requires a product mindset, as well as careful consideration of the challenges and limitations involved. In this article, we will explore the key steps involved in building and implementing AI products in the NGO sector, and how NGOs can overcome the challenges to fully realize the potential of AI.

Why the NGO sector needs a product mindset

“The product must solve a problem that is meaningful to the customer.” – Eric Ries, Author of The Lean Startup.

And who is the customer in the NGO world? The communities and the people they serve. However, in the NGO sector, it often happens that solutions are being built without enough user feedback. This approach can result in ineffective solutions that do not meet the actual needs and preferences of the intended beneficiaries.

“In a corporation when you have a product, you launch it, and you know if people like it or not. The feedback is instant and direct. For NGOs, their users often have no choice, and even if the users do not like the service or product from NGOs, they have to use it.” There is almost no feedback mechanism in NGOs., Save the Children US CEO Janti Soeripto on the Omdena ImpACT Leadership Podcast

A lack of feedback mechanisms where users (communities and people in need) have no choice means there is no real drive for innovation. However, by focusing on product development principles such as accountability, user testing, and feedback mechanisms, NGOs can create products that are tailored to the needs of their users. 

As part of the Omdena ImpACT leadership podcast, Omdena´s CEO Rudradeb Mitra interviewed Vishal Ghotge, CEO of, the world’s biggest micro-lending platform. It has lent over $1.88B to 4M people in 80 countries.

Vishal, said at “Growth is a moral obligation”. 

“There are 1,4 billion people who are outside of the traditional loan system, and while we at Kiva reach 500,000 borrowers yearly, how can we reach 5 million, 50 million, or even 500 million people?” 

The key question for Vishal is how to reach those people through a (mind)set of products and services by answering some of the following questions:

  • What do we need to build from the customer perspective to address their needs?
  • How do people think about microfinance and lending? 
  • How do we bring network effects to our products? 
  • How can we enable an environment of “Learn fast, fail fast”
  • How do we enable communities to participate in the product development process?
  • How do we calculate impact? Breadth of impact vs. depth of impact. 

The role of AI 

AI can play a significant role in helping the NGO sector adopt a product mindset. By leveraging AI-powered tools and techniques, NGOs can collect, analyze, and utilize data to create products that are more targeted and effective. AI can also help NGOs to identify patterns and trends in user behavior, which can inform product development and design. 

For example, an NGO providing mental health services can use AI to identify which age groups are most susceptible to certain mental health issues, while an NGO focused on disaster relief can use AI to determine which types of disasters are most likely to occur in a particular region and when. This information can be used to develop targeted interventions and response plans that better meet the needs of the communities they serve.

Furthermore, AI-powered feedback mechanisms can enable NGOs to gather user feedback in real time, allowing for rapid iteration and improvement. AI can also help NGOs to measure the impact of their products and interventions, providing valuable insights into what works and what doesn’t.

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

I. Identifying the problem

  • The importance of understanding the problem that needs to be addressed before building an AI product.
  • The importance of involving the local community for problem understanding and solving 
  • The role of data in problem identification.

Before building an AI product, it is crucial to identify the problem that needs to be solved. This requires a deep understanding of the needs and challenges faced by the community being served. 

At Omdena, we frequently utilize our local chapters to gain a deeper understanding of the problem context by engaging with citizens who may be impacted by the problem at hand. An illustration of this is our collaboration with GovTech Bhutan, as outlined in this article: Govtech Agency Bhutan Partners with Omdena for a Hybrid Program to Raise a New Generation of Local AI. Through this partnership, we aim to foster a new generation of local AI leaders who can use their expertise to address pressing societal issues in Bhutan.

Next, data plays a crucial role in this process, as it can provide insights into the root causes of the problem, the demographics of the affected population, and the most effective interventions. 

For example, an NGO working to improve maternal and child health may use data to identify the leading causes of maternal mortality and infant morbidity in a specific region and to understand the underlying socio-economic factors that contribute to these issues. 

Similarly, an NGO working to address food insecurity may use data to identify the specific communities that are most affected, the underlying causes of food insecurity, and the most effective interventions to address these issues. By leveraging data to identify the problem, NGOs can build AI products that are better targeted, more effective, and more likely to achieve impact.

Building resilience against hunger and malnutrition in Burkina Faso

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

II. Data collection and preparation

  • The importance of data in AI product development.
  • The types of data needed for AI models.
  • Methods for collecting data and overcoming roadblocks

In order to build effective AI models, it is necessary to collect and prepare high-quality data that is relevant to the problem being addressed. This requires an understanding of the types of data needed for AI models, which can include structured and unstructured data, such as text, images, and sensor data. Methods for collecting and preparing data can vary depending on the specific problem being addressed but may include surveys, interviews, data scraping, and data cleaning. 

For example, an NGO working to address water scarcity may use data collected through sensors to identify areas with low water availability, while also using satellite imagery to identify changes in water resources over time. 

Similarly, an NGO working to address education inequity may use surveys and interviews to collect data on the educational needs and experiences of students in different communities. By collecting and preparing high-quality data, NGOs can build AI products that are more accurate, more targeted, and more effective in achieving their goals.

Path suggestions comparison

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. 

III. Choosing the right AI model

When building an AI product for the NGO sector, it is important to choose the right AI model for the specific problem being addressed. There are various types of AI models, such as decision trees, neural networks, and support vector machines, each with its own strengths and weaknesses. Understanding the different AI models and their applications is critical in choosing the right one for the specific problem. 

For example, an NGO working to identify illegal fishing activities may choose to use a neural network model to analyze satellite imagery, while an NGO working to predict the spread of disease may choose to use a decision tree model to analyze epidemiological data. By choosing the right AI model, NGOs can build more accurate and effective AI products.

What are the ethical considerations we need to address?

Ensuring that the AI product is developed and implemented in an ethical manner is critical for building trust and avoiding unintended consequences.

Many people may believe that having a team of AI engineers with good intentions is sufficient to develop ethical AI. However, good intentions are necessary but not sufficient for this purpose.

As the old proverb goes, “The road to hell is paved with good intentions,” which is also true for AI. We have witnessed several instances of “bad” and unethical AI developed by tech giants like Amazon, who created a sexist AI-assisted recruitment tool, and Twitter, who developed an image-cropping algorithm biased against black people. It is unlikely that the engineers at Amazon or Twitter had bad intentions, but they still produced unethical AI solutions.

“The best products come from people who are trying to solve a problem they’re facing themselves.” – Jason Fried, Co-founder of Basecamp.

The democratization of knowledge is raising a new generation of talented AI engineers worldwide, giving hope for the future. Previously, educating oneself on AI required relocating to major cities, but now anyone with a laptop and internet connection can gain the necessary knowledge from anywhere.

This new wave of AI talent worldwide is our best defense against unethical AI. By involving these individuals in collaborative AI projects, we can collectively work towards building ethical AI and addressing real-world issues.

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

IV. Implementation and impact

After building an AI product for the NGO sector, it is important to implement it effectively and measure its impact. Implementation strategies can vary depending on the specific product but may include piloting the product in a limited context before scaling up, partnering with other organizations to expand reach, and ensuring that the product is accessible and user-friendly. 

Measuring the impact of the AI product is critical in determining its effectiveness and making improvements over time. Impact measurement may involve gathering user feedback, analyzing changes in behavior or outcomes, and comparing the impact of the AI product to alternative interventions. By effectively implementing and measuring the impact of AI products, NGOs can ensure that their interventions are effective and make a meaningful difference in the communities they serve.

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

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.

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