Somalia AI Job Risk 2026: 1M Workers Ranked

1.04 million workers, SNBS 2019 Labour Force Survey

Employment data comes from ILO ILOSTAT (CC BY 4.0), based on the Somalia National Bureau of Statistics (SNBS) Labour Force Survey, using ISCO-08 major group classifications. Data year: 2019. The 2019 survey was conducted during a period of relative security improvement in Mogadishu and southern Somalia following the AMISOM-supported rollback of Al-Shabaab from major urban centres. It captures a snapshot of the formal and semi-formal economy before the COVID-19 disruption of 2020-2021.

Somalia's economy operates under conditions that make standard economic analysis difficult. The formal banking system is minimal - Somali remittance flows (estimated at $1.3-2 billion annually by the World Bank, equivalent to approximately 20% of GDP) move primarily through hawala networks rather than formal banks. The public sector was effectively non-functional for two decades following the 1991 collapse of the Siad Barre government, and has been rebuilt slowly under the Federal Government of Somalia since 2012 with heavy international support.

The data covers approximately 1,036,200 formally counted workers. Somalia's actual workforce is much larger - the country's population is estimated at 18 million (UN DESA 2024 estimate), and informal and pastoralist employment is extensive and poorly counted. The SNBS survey provides the best available internationally standardised occupational data, but should be understood as covering the more trackable urban and semi-urban workforce rather than the full pastoral and rural economy.

The most AI-exposed occupations in Somalia

Clerical support workers score 8.5/10 on AI exposure, covering 15,910 workers at 1.5% of employment. Somalia's clerical workforce is concentrated in Mogadishu's government administration, international organisation offices (AMISOM headquarters, UN compound, international NGO offices along Via Roma in Mogadishu), and the commercial sector. Though small in number, these workers perform exactly the tasks - data entry, document processing, correspondence, scheduling - that AI tools directly target. The presence of international technology platforms (international NGOs use the same productivity software and increasingly AI tools as their counterparts in Nairobi or Geneva) means Somalia's aid-sector clerical workers face faster AI adoption timelines than their counterparts in purely domestic organisations.

Professionals at 6.5/10 cover 211,600 workers - the second-largest group and the defining anomaly of Somalia's workforce data. The humanitarian aid sector in Somalia has been one of the world's largest and most sustained for over three decades. Major employers include: UNHCR (managing one of the world's largest displacement crises - approximately 3.8 million internally displaced persons as of 2024), WFP (operating the largest humanitarian food operation in Africa), UNICEF, WHO, and hundreds of international and Somali NGOs. These organisations employ programme officers, monitoring and evaluation specialists, financial managers, communications staff, protection officers, and technical advisers - all ISCO classification 2 professionals. The diaspora-funded private sector (Somali-owned banks, telecoms companies including Hormuud Telecom, and trading firms) adds a further professional cohort.

Managers at 5.5/10 covering 112,730 workers (10.9%) reflect the same structural driver: humanitarian sector management layers plus state-rebuilding administration. Technicians at 5.5/10 cover 32,880 workers (3.2%) - a smaller share than in oil-economy peers, reflecting Somalia's limited industrial base.

Why aid sector professionals - not domestic economy - dominate

The concentration of professionals at 20.4% in one of the world's lowest-income countries demands an explanation. The answer is that Somalia's professional class is partially a humanitarian aid sector artefact. The international humanitarian system operating in Somalia employs tens of thousands of Somali nationals in professional roles. Aid organisations with Somalia programmes include UNHCR, WFP, UNICEF, WHO, UNDP, FAO, UNFPA, NRC (Norwegian Refugee Council), IRC (International Rescue Committee), MSF (Medecins Sans Frontieres), Oxfam, Save the Children, and hundreds of smaller organisations - all employing Somali programme staff who are ISCO-classified as professionals.

AI tools are actively reaching this professional cohort through the same international platforms that their employers use globally. An UNHCR Somalia programme officer uses the same Microsoft 365 and Teams environment as their counterpart in Geneva, including the same Copilot AI features. An IRC monitoring and evaluation specialist uses the same data analysis tools as M&E staff in New York. The humanitarian sector's global standardisation of tools means AI adoption timelines in Somalia's aid-sector professional workforce are faster than the country's income level would imply.

The contrast with agriculture (6.2%, 64,290 workers) is striking. Somalia's agricultural base - concentrated in the Jubba and Shabelle river valleys, with pastoral livestock herding across the southern and central regions - employs far fewer formally tracked workers than the professional class. This partly reflects survey methodology (pastoral workers are harder to enumerate) and partly reflects genuine economic structure: Mogadishu's humanitarian and commercial economy employs more people in professionally classified roles than the entire formally counted agricultural sector.

The safest jobs from AI in Somalia

Elementary occupations score 2.0/10 in Somalia, covering 394,840 workers at 38.1% of employment - the largest single group. These workers include market traders and sellers in Mogadishu's Bakara Market (one of East Africa's largest informal markets), domestic workers, construction labourers active in Mogadishu's ongoing rebuilding, porters, rickshaw and tuk-tuk drivers, and general manual workers. Near-term AI displacement risk is zero for this group. Their vulnerability is economic instability, conflict, and displacement - not automation.

Craft and trades workers score 2.5/10 covering 68,250 workers (6.6%). Somalia's craft sector covers construction tradespeople (Mogadishu's building boom has been sustained by diaspora remittances and aid-funded infrastructure), vehicle mechanics serving the large vehicle fleet, electricians, and plumbers. Agricultural workers at 3.0/10 (64,290 workers, 6.2%) include both crop farmers in the southern river valleys and pastoralists recorded in the survey, though the 6.2% figure likely significantly undercounts the full pastoral workforce.

What this means for Somalia workers

Somalia's 3.75/10 AI exposure average is above the Sub-Saharan Africa median for low-income countries, and the reason is the same one that makes the country's professional share so high: the humanitarian sector has created an AI-exposed professional workforce that domestic economic development has not. This is an unusual and somewhat fragile basis for professional class formation - aid funding is cyclical, donor priorities shift, and the Federal Government of Somalia is working toward reducing aid dependency as a deliberate policy goal.

For the 211,600 professionals and 15,910 clerical workers concentrated in Mogadishu and other urban centres, AI adoption timelines are determined by their employers' decisions rather than the Somali economy's own development trajectory. Somali aid-sector professionals who develop AI skills - in data analysis, report generation, monitoring systems design, and grant writing - are better positioned in an increasingly competitive aid-sector labour market than those who do not. The humanitarian sector globally is beginning to adopt AI tools for programme monitoring and impact measurement, and Somalia's professionals are on the receiving end of that transition.

For the 394,840 elementary workers who form the majority of Somalia's formally tracked workforce, the near-term challenge is economic security, conflict, and access to basic services - not AI displacement. The longer-term trajectory depends on whether Somalia's domestic economy can create enough formal employment opportunity to absorb the large informal workforce, which in turn depends on political stability and private investment that no AI forecast can reliably predict.