Artificial Intelligence (AI) is transforming the operational backbone of the development aid sector. It is now used in real-time decision-making, resource allocation, and crisis management. What sets AI apart from traditional data management methods is its ability to process massive datasets quickly and make predictive decisions, enabling development actors to be proactive rather than reactive.
Data-driven targeting enables more precise delivery of aid. For example, the World Food Programme’s Building Blocks initiative uses blockchain and AI to track food distribution to ensure that the right people receive assistance. AI also supports humanitarian logistics, helping organizations such as Zipline in Rwanda and Ghana to deliver medical supplies via autonomous drones.
UNICEF’s U-Report chatbot demonstrates the use of AI in facilitating two-way communication between young people and decision-makers, helping local governments and agencies to respond more effectively to emerging needs.
These applications showcase how AI shifts aid from being reactive to becoming anticipatory, often making the difference between managing a crisis and preventing one.
As organizations move from paper-based to digital systems, the AI revolution is no longer merely a vision – it’s an active force on the ground.
Source: WFP
Drivers of AI adoption in aid work
AI’s growing role in development is no coincidence. Several drivers are pushing humanitarian agencies to integrate AI solutions, the first being the rapid advance of technologies such as cloud-based platforms, cheap sensors, and mobile devices, which have lowered the barriers to AI integration. For example, organizations can now deploy a small AI model on mobile phones for use in rural health clinics, with costs significantly lower than a decade ago.
Another driver of AI adoption is changing donor behaviour. Organizations such as the Gates Foundation and the UK’s Foreign, Commonwealth and Development Office (FCDO) are demanding greater accountability and measurable results. AI helps to meet these demands by tracking impact in real-time. A case in point is the International Rescue Committee using its AI-powered SignpostChat to reach 10 times more displaced individuals with trauma-informed, multilingual support.
Finally, collaboration is driving innovation. NGOs and private tech companies now co-develop tools. For instance, UNICEF has partnered with IBM to improve educational access using AI in remote regions.
Beyond major donors, local governments in countries like Rwanda and Bangladesh are experimenting with investing in their own AI infrastructures. These investments are part of national digital strategies that aim to build resilient, data-informed institutions that can anticipate crises and optimize public service delivery.
Source: IBM
Challenges to effective AI use in aid
While AI promises efficiency, it also introduces risks that can undermine development goals. Bias in training data remains a major problem, and predictive tools for resource allocation could deprioritize marginalized communities if their data is underrepresented.
See also: The ghost in the machine: AI biases and hallucinations | Opinion
Digital inequality is another issue. In regions like sub-Saharan Africa and South Asia, internet penetration is far lower than global averages. This limits access to AI-enhanced services and often excludes those most in need of aid.
Privacy and surveillance risks are also significant. AI tools often rely on collecting sensitive data such as biometrics, health records, or location information. In fragile or authoritarian contexts, this data could be misused for political purposes or surveillance, putting already at-risk populations in greater danger.
There is also the risk of an over-reliance on technology. As AI tools become more widespread, there is a danger that human judgment will be sidelined, even in situations where contextual understanding and flexibility are essential, such as disaster response or in conflict zones.
See also: How AI is widening the global/human development gap
Furthermore, opaque AI systems – so-called ‘black boxes’ – create governance challenges. If a prediction algorithm wrongly deprioritizes a vulnerable community, it is difficult to know why and how to fix this. Many AI systems operate in ways that are difficult to interpret, especially by non-technical humanitarian workers. This reduces trust in the tools and makes it hard to understand or challenge decisions that could significantly impact people’s lives.
Operational staff face real dilemmas. Do they trust AI forecasts when human experience contradicts these? What if an algorithm suggests deprioritizing an area that a field worker knows to be at risk? These tensions reveal the critical need for combining AI insights with human judgment.
AI for development – examples from the field
Some countries stand out in their adoption of AI for development. In Kenya, the government partnered with IBM Research Africa to build predictive models for food security. India has leveraged AI to manage its disaster response network, using AI-powered dashboards to monitor floods and heatwaves.
In Jordan, the UNHCR uses AI-driven chatbots to support Syrian refugees, providing 24/7 legal and health guidance. Meanwhile, Indonesia has collaborated with the World Bank to deploy AI-enhanced logistics systems for vaccine delivery to remote areas.
Brazil, in contrast, while technologically capable, has focused more on fintech and AI health applications domestically rather than using AI for foreign aid or humanitarian initiatives.
These examples illustrate the importance of political will, infrastructure readiness, and public-private partnerships for the successful adoption of AI in the development sector.
Future perspectives and recommendations
Looking ahead, the integration of AI into development aid should be pursued carefully and be guided by several strategic pillars.
1️⃣ Firstly, inclusive design is essential. AI systems should be developed with local input to ensure cultural appropriateness and practical relevance. For instance, language models need to support local dialects if they are to serve rural populations.
2️⃣ Secondly, capacity building must accompany tech deployment – small NGOs and humanitarian organizations should be trained in data science. Scaling such initiatives would ensure that local actors can maintain and adapt AI systems.
3️⃣ Thirdly, ethical governance frameworks must be strengthened. The European Commission’s ethics guidelines for trustworthy AI could serve as a model for developing regions.
Beyond these pillars, there is a need for evaluation and iteration. AI systems must be continuously tested and refined based on feedback from the communities they serve. Local knowledge should not only inform AI design but also be used to audit and validate algorithmic outputs.
Conclusion
AI is redefining how development aid is delivered, particularly in dynamic and complex environments. Its ability to process large datasets, predict events, and streamline logistics is unmatched. However, the benefits must be balanced against risks such as exclusion, bias, and ethical opacity.
The road ahead features both opportunity and the need for caution. As the world steps into a more data-driven future, AI’s success in development will hinge on our ability to wield it wisely.
In short, while AI offers efficiency and new capabilities to humanitarian aid, its risks –ranging from bias and privacy concerns to accountability and security – must be carefully managed through ethical design, transparent governance, and human oversight.
