Artificial intelligence (AI) is becoming seen as the technology of the future, something speculative, experimental or confined to advanced economies. But the role of AI in developing nations today is already shaping decisions that affect food security, public health and poverty reduction. Far from the abstract, these systems are becoming deeply embedded in the daily work of governments, humanitarian agencies and farmers responding to real-world crises.

The question is no longer whether AI will influence development, but how and under what conditions it can support, rather than undermine, human well-being.

Predicting Hunger Before It Becomes Famine

One of the most consequential uses of AI in developing nations is in forecasting food insecurity. Historically, famine response has been reactive: aid arrives after a visible crisis, often too late to prevent mass suffering. AI-driven early warning systems aim to change that.

The World Bank developed the Famine Action Mechanism (FAM) in collaboration with the United Nations (U.N.) and the World Food Programme (WFP). It uses machine learning models to forecast food insecurity months in advance by integrating satellite imagery, climate indicators, market prices, conflict data and household surveys. Complementing this effort is HungerMap LIVE, a real-time food security monitoring platform developed by WFP.

HungerMap LIVE is currently used across Kenya, Ethiopia, Somalia, South Sudan and Bangladesh. The platform integrates mobile phone surveys, remote sensing and predictive analytics to produce continuously updated risk assessments. These systems directly inform when and where resources are deployed, enabling earlier cash transfers, targeted food assistance and preventative interventions.

Evidence from WFP and the World Bank shows that anticipatory action is both more humane and more cost-effective than emergency response after crisis onset.

AI on the Farm: Empowering Smallholder Farmers

Agriculture remains the primary livelihood for hundreds of millions of people in developing nations. Yet smallholder farmers often lack timely agronomic expertise. AI is beginning to close that gap.

In Kenya and across East Africa, the PlantVillage Nuru app uses smartphone-based computer vision to diagnose crop diseases in real time. Designed to operate offline, Nuru enables farmers to identify threats such as cassava mosaic disease and fall armyworm by photographing affected plants. Research published by Penn State University and FAO partners shows that early detection through AI-based diagnostics significantly reduces crop losses and improves smallholder resilience.

Still, limitations remain. Unequal smartphone access, language localization challenges and the need for contextual agronomic knowledge highlight that AI tools must be embedded within broader agricultural support systems, not treated as standalone fixes.

Expanding Health Care Access Through AI Screening

In health care, AI’s most immediate promise lies in early detection, particularly in regions where trained specialists are scarce. In India, the health-tech company Niramai has developed Thermalytix. This AI-based breast cancer screening system uses thermal imaging rather than mammography.

The technology is portable, radiation-free and significantly lower-cost, making it viable for rural clinics and mobile health camps. Clinical studies published in peer-reviewed medical journals show that Thermalytix demonstrates high sensitivity in detecting early-stage breast cancer, particularly among younger women. Importantly, though the system is designed to assist clinicians, not replace them, it reinforces AI’s role as decision-support rather than autonomous authority.

How Institutions Are Integrating AI

AI adoption in developing nations is not happening in isolation. Major institutions, including the World Bank, WFP, FAO, UNICEF and national ministries, are integrating AI into policy planning, service delivery and crisis response. This integration involves building a larger infrastructure for data models and storage, training local staff, establishing accountability mechanisms and partnering with local organizations.

The Risks Beneath the Promise

Despite its potential, AI, while still in its development stages, raises serious concerns. Predictive models are only as good as the data they rely on. In many developing regions, data is incomplete, uneven or biased.

U.N. reports warn that algorithmic bias, financial incentives and extractive data practices can entrench inequality and potentially harm individuals if governance safeguards are absent. The U.N. Technology and Innovation Report 2025 warns that up to 40% of global jobs could be affected by AI, with economies that rely on low-cost labor potentially losing their competitive edge. There is also the risk of over-reliance on algorithmic forecasts, in which predictive outputs are treated as objective truth rather than probabilistic guidance, sidelining local knowledge and accountability.

Recognizing these risks, international bodies and governments are developing safeguards. UNESCO’s Ethics of Artificial Intelligence framework emphasizes human rights, transparency, accountability and data sovereignty. Similarly, UNICEF’s Guidance on AI and Children focuses on protecting children and vulnerable populations from harm, surveillance and exclusion resulting from AI’s prevalence.

Meanwhile, multiple developing nations are drafting national AI strategies to align technological deployment with development priorities rather than external commercial interests.

AI as Development Infrastructure

AI will not end poverty or hunger on its own. But when treated as infrastructure rather than innovation, embedded in institutions, guided by ethics and grounded in local realities, it can meaningfully improve how societies anticipate crises, allocate resources and expand access to essential services. The role of AI in developing nations will not be decided by algorithms alone, but by governance choices: who designs these systems, who controls the data and whose lives they are built to improve.

– Matt Irwin

Matt is based in Brooklyn, NY, USA and focuses on Technology and Solutions for The Borgen Project.

Photo: Pixabay