South Sudan AI Job Risk 2026: 1.2M Workers, Service/Sales at 75.2%

1.2M workers in a conflict-displaced economy

South Sudan became the world's newest nation in 2011 following the 2005 Comprehensive Peace Agreement that ended Sudan's second civil war. The country began nation-building with essentially no infrastructure, no trained civil service, and an economy entirely dependent on oil revenues. The civil war that broke out in December 2013 - between forces loyal to President Salva Kiir and former Vice President Riek Machar - destroyed much of that nascent state-building effort. A second civil war outbreak in 2016, the Revitalized Agreement on Resolution of the Conflict in South Sudan (R-ARCSS) in 2018, and ongoing subnational conflicts have maintained a state of chronic instability.

The economic consequence has been the destruction of formal sector employment outside Juba. Oil production - South Sudan's only significant export - continues in the Unity and Upper Nile fields but is operated by international companies with limited local employment. Juba has grown explosively as displaced persons from across South Sudan's 10 states have sought safety in the capital. The informal economy of Juba's markets - Konyo Konyo, Juba Town market, and dozens of smaller markets - is where most of the tracked employment exists.

The National Bureau of Statistics (NBS) of South Sudan has operated in a severely constrained environment throughout the post-independence period. The ILO's modelled estimates for South Sudan are among the most uncertain in the dataset - reflecting the near-impossibility of conducting nationally representative labour surveys in an active conflict environment with mass displacement and extremely limited statistical infrastructure.

Why service/sales at 75.2% is not what it appears

A 75.2% service and sales employment share would, in a developed economy, indicate a post-industrial service economy like Singapore (74.2%) or the United Kingdom (79.9%). South Sudan is the opposite case: it is a country where most of the actual population remains in pastoralism and subsistence agriculture that the model does not capture, and where the urban-displaced population visible to the model has concentrated in the only survival activity available in a city with no formal labor market - small-scale trade.

Juba's informal economy operates through several market layers. Konyo Konyo market (named after a Dinka phrase meaning "a very busy place") is one of the largest markets in East Africa and a major trading hub. Ugandan, Kenyan, Ethiopian, and Congolese traders bring goods that South Sudan cannot produce domestically (cooking oil, sugar, soap, textiles, electronics). South Sudanese sellers - many displaced from their home states - resell these goods at smaller markets throughout Juba. Food vending, tea stalls, motorcycle taxi (boda boda) services, and phone charging businesses are all concentrated in the service/sales ISCO category.

These workers score 3.5/10 on AI exposure in the standard ISCO-based methodology - a moderate score for the service category. But the 3.5/10 score assumes a service economy in which digital tools are accessible and relevant. For Juba's market traders, the digital economy primarily means mobile money (MTN Mobile Money and M-PESA have significant South Sudan operations), not AI. The practical AI exposure of these 924,670 modelled service workers is essentially zero.

The safest jobs in South Sudan

Agricultural workers score 2.0/10 in South Sudan, covering only 97,670 workers (7.95%) in the ILO model. This is dramatically under-representative of South Sudan's actual economy, where the UN Food and Agriculture Organization (FAO) estimates that more than 80% of the population depends on agriculture and pastoralism for their livelihoods. The model's agriculture figure reflects only those agricultural workers captured through the limited survey data available - likely urban-fringe farmers near Juba and other towns, not the vast pastoral populations of Jonglei, Warrap, and Northern Bahr el Ghazal states.

Craft and trades workers at 2.5/10 cover 41,700 workers (3.40%). These are carpenters, mechanics, welders, and construction workers serving Juba's rapidly expanding built environment. Juba has experienced extraordinary construction growth driven by NGO, UN, and oil sector spending, and craft trades workers serve this demand. Plant and machine operators at 3.0/10 cover 15,730 workers (1.28%), primarily in the oil fields and in Juba's generator-powered infrastructure.

What this means for South Sudan workers

For the vast majority of South Sudan's actual working population - the pastoralists, subsistence farmers, and fishing communities who do not appear in this ILO model - AI job risk is entirely irrelevant. The immediate economic threats they face are conflict-driven cattle raiding, flooding of the Sudd wetlands (worsened by climate change), food insecurity, and the collapse of market access when conflict disrupts roads and trade routes. These are not problems that AI solves or worsens in any near-term sense.

For the 1.23M workers in the model, the picture is more nuanced but still far removed from AI adoption. Juba's informal market traders (the 75.2%) are operating in a cash and mobile-money economy with very limited smartphone penetration and essentially no broadband internet outside international organization compounds. The clerical workers in government ministries and oil company offices are the closest to AI tool access, but they operate in institutions with severe capacity constraints.

The international NGO and UN sector - the largest formal employer in South Sudan by some measures - is the most AI-accessible employer in the country. International organizations use AI in logistics, needs assessment, beneficiary data management, and program planning. South Sudanese professional staff employed by these organizations (in the professionals category at 7.39%) are the workers with the most realistic near-term AI tool exposure. But this is a tiny fraction of the actual workforce.

South Sudan's development trajectory in any optimistic scenario - sustained peace, oil revenue recovery, rural development - would create the conditions for AI to become relevant over a 10-20 year timeframe. In the current context, the country's primary needs are physical security, basic infrastructure (electricity, roads, water), and food security. AI is not on any realistic near-term development agenda for South Sudan.