In Somalia, AI Helps Food Arrive Before the Window Closes

In Somalia's Burhakaba district, the result of a forecast was not another graphic on a screen. It was food delivered to 48,000 people and nutrition support allocated to 3,000 women and children. The World Food Programme used AI tools on its HungerMap Live platform to detect deteriorating conditions before child malnutrition turned into displacement or death. Here, the model's value can be measured by the time a family gained before the crisis closed the window for intervention.

The map feeds no one, but it changes when a decision is made.The platform brings together data on food insecurity, climate, the economy, agriculture and inflation, helping identify areas that are hungry now or moving in that direction. The first version launched in January 2020 to monitor food security in more than 90 countries. It used AI to estimate hunger levels where local data was incomplete. But its ability to predict future crises remained limited, and it did not track the nutritional quality of available food.

In April, WFP launched the platform's second version with a new interface and broader predictive capabilities. Instead of merely showing where hunger exists, the tool now helps teams interpret the causes of deterioration and estimate where conditions may worsen later. In Somalia, the analysis incorporates indicators such as rainfall deficits, flood risk, market prices, conflict and nutrition. The result is not an automated order to distribute aid. It is a signal supporting a human decision about which community needs intervention first and what form of support is most appropriate, whether cash or specialist treatment for malnutrition.

When funding falls short, setting priorities becomes an ethical decision.Six million Somalis face crisis levels of food insecurity, while the World Food Programme can reach only one in ten people in need. WFP's head of vulnerability analysis and mapping in Somalia says the office needs an additional $192 million through January 2027 to reach everyone who needs food. The algorithm therefore improves the quality of the trade-off, but it does not make depriving the other nine acceptable.

The context makes the calculation harder. Somalia faces cycles of climate change-linked drought followed by flash floods, and has endured 35 years of conflict that has killed hundreds of thousands. It also imports most of its food, leaving it exposed to global price shocks, including the effects of the ongoing conflict in the Middle East. In June, the World Food Programme and the Food and Agriculture Organization added Somalia to the list of hunger hotspots of highest concern. In that sense, the platform is not predicting a single variable. It is trying to read the intersection of weather, markets, violence and nutrition before it becomes fully visible in clinics and displacement camps.

For the Global South, the gain is additional time, not a remedy for scarce resources.Through a Global South solidarity lens, the experience makes a practical case against confining AI to office assistants and advertising markets. Humanitarian institutions and governments with limited resources can use fragmented data to provide early warning, move stocks and choose a less costly intervention before a disaster widens. But justice does not follow from a more accurate model alone. Local data must be representative, decisions must be open to review, and funding must exist to act on what the signal reveals.

This point matters for the Arab region too. Yemen, Sudan and Palestine are included among the same hotspots of highest concern, while shocks in the Middle East affect food prices beyond its borders. Ministries, relief funds and meteorological centres in the region can read Somalia's experience as a model for coordinating weather, market and nutrition data, while keeping field verification central rather than incidental. The World Food Programme itself stresses that the platform's data does not replace assessment on the ground.

The honest conclusion is that HungerMap Live 2.0 shortened the distance between warning and decision in Burhakaba, and helped direct tangible assistance. At the same time, it exposed technology's limits: AI can show where hunger is intensifying, but it cannot create resources that do not exist. The value of this experience lies not in claiming that an algorithm solved the crisis, but in giving aid workers more time to make use of every available meal before it was too late.