Artificial intelligence is becoming part of Somalia’s humanitarian response at a moment when needs are rising faster than available aid. The World Food Programme is using its HungerMap Live platform to identify areas where food insecurity may worsen, allowing assistance to be planned before families exhaust their coping options.
From Warning Signs to Earlier Assistance
The significance of this development lies less in automation itself than in the possibility of reducing the time between recognising risk and delivering support. HungerMap Live combines information on rainfall, flood exposure, market prices, conflict, nutrition and agricultural conditions to identify areas facing present or emerging food insecurity. Its updated version adds predictive features intended to help humanitarian actors anticipate deterioration rather than respond only after a crisis has become visible through displacement or severe malnutrition.
In Burhakaba District, in Somalia’s Bay region, the platform helped WFP identify urgent needs and direct food assistance to 48,000 people, alongside nutritional support for 3,000 women and children. This illustrates the practical value of prediction in a setting where delayed intervention can lead to more serious health consequences and greater pressure on already limited local services.
A Tool for Scarce Resources
The technology also reflects a difficult humanitarian reality. Somalia faces overlapping pressures from drought, flooding, conflict, weak food production and exposure to international price shocks. FAO and WFP identified Somalia as a hunger hotspot of highest concern, warning that around six million people were projected to experience high levels of acute food insecurity between April and June 2026. Nearly 1.9 million were projected to face emergency conditions, while Burhakaba was assessed as facing a risk of famine under a plausible worst case scenario.
Under these conditions, AI enabled forecasting can support more evidence based prioritisation. It may help determine where cash support is more appropriate than food deliveries, where nutrition interventions should be concentrated, and where supplies should be positioned before roads become inaccessible or markets deteriorate. The platform therefore does not simply map hunger. It seeks to connect analysis with operational decisions about timing, location and the form of assistance.
Yet prioritisation is not equivalent to meeting need. WFP reported that it could reach only one in ten Somalis experiencing crisis level food insecurity, and said an additional $192 million was required through January 2027 to assist everyone in need. AI may improve the allocation of finite resources, but it cannot resolve funding gaps or produce food assistance where financing is unavailable.
Data Must Remain Grounded
The use of predictive systems also raises important questions about reliability and accountability. Humanitarian data can be incomplete in conflict affected areas, while rapid changes in rainfall, prices or security conditions may affect forecasts. WFP has emphasised that HungerMap Live is not a substitute for field assessments. Instead, the platform is designed to complement local information and professional judgement.
This limitation is important. Decisions about aid distribution have direct consequences for communities, and technical models should be assessed against local realities. The inclusion of nutritional indicators in the new platform may broaden the analysis beyond calorie availability, helping identify risks linked to inadequate vitamins and minerals. However, this potential depends on data quality, transparent methods and meaningful engagement with local partners.
A Final Note
Somalia’s experience shows how AI can strengthen humanitarian early warning when it is used as a decision support tool rather than a replacement for human judgement. Its greatest value may be in making earlier action possible, but sustained funding, reliable local assessments and access to affected communities will remain essential to turning prediction into protection.
