Oman AI Job Risk 2026: Which of 1.12 Million Workers Are Most Exposed?

The most AI-exposed occupations in Oman

Clerical support workers score 8.5/10 across 52,300 workers (4.66%) in the 2008 NCSI data. This group is politically significant in Oman's context: Omanisation policy - officially launched in 1988 and expanded through Ministerial Decision 1/1995 - specifically targeted clerical and administrative roles as early priorities for replacing expatriate workers with Omani nationals. By 2026, the clerical sector in Oman is substantially Omanised: Omani nationals hold the majority of government administrative, banking, and insurance clerical positions. This matters for AI risk because Omanis in clerical roles have stronger domestic protections and political voice than migrant workers in equivalent roles in Kuwait or Qatar - but the substitution pressure is the same.

Professionals score 6.5/10 across 107,700 workers (9.58%). This group includes engineers, IT professionals, healthcare workers, and teachers. Oman's education sector has grown substantially since 2008 - Sultan Qaboos University, the German University of Technology (GUtech), and a network of polytechnics have expanded, adding to the professional workforce. Technicians and associate professionals score 5.5/10 across 124,100 workers (11.04%), the largest of the three higher-exposure groups. Technicians include oil and gas technical workers at Petroleum Development Oman (PDO) - the joint venture between the Omani government, Shell, Total, and Partex - as well as telecom, utilities, and construction supervisors.

Omanisation policy, Tanfeedh, and the AI frontier

Omanisation is Oman's national policy of replacing expatriate workers with Omani nationals across specified sectors and job categories. It operates through the Labour Law, Ministerial Decisions setting Omanisation percentage targets by sector, and an enforcement mechanism that tracks company compliance. By 2026, Omanisation has achieved significant penetration in government services, banking, insurance, and retail. It has been less successful in construction, agriculture, and domestic work - sectors dominated by South Asian migrant workers.

The interaction between Omanisation and AI automation creates a specific policy tension that does not exist in countries without nationalisation mandates. In a pure market economy, employers deploy AI tools to reduce headcount regardless of worker nationality. In Oman, the nationality dimension complicates this: if an employer uses AI to reduce clerical headcount, the Omanised workers displaced lose positions that were explicitly designated for national employment. The government cannot both enforce Omanisation and passively allow AI to eliminate the Omanised positions that Omanisation created. This tension is likely to slow AI deployment in Omanised sectors relative to the pace seen in Kuwait (where migrant workers bear most displacement first) or Qatar (where Vision 2030 frameworks provide explicit transition support).

Tanfeedh - Oman's National Programme for Enhancing Economic Diversification, launched in 2016 under Sultan Qaboos - explicitly addressed skills and employment transformation. Its successor initiatives under Vision 2040, adopted by Sultan Haitham bin Tarik in 2020, continue this focus. Vision 2040's human development and knowledge economy pillars target upskilling Omani workers for digital economy roles - specifically the roles that AI will create, not only those it displaces. The NCSI Oman now tracks digital skills indicators separately from traditional occupation survey data, reflecting the policy pivot toward AI-compatible workforce development.

The safest jobs from AI in Oman

Service and sales workers are the largest occupation group in Oman at 28.01% (314,900 workers in the 2008 data), scoring 3.5/10. This group includes retail workers, hospitality staff, food service workers, and personal care workers. Oman's tourism sector - growing substantially since 2010 with the opening of Muscat's major hotels, Al Mouj Marina, and Nizwa and Sur heritage tourism - employs a significant service workforce. Omani hospitality workers, particularly in government-supported tourism enterprises, are partly insulated from rapid displacement because of Omanisation requirements in the sector and because AI's current capabilities do not extend to physical service delivery.

Elementary occupations score 2.0/10 across 291,900 workers (25.96%) - the second-largest group. These are physical, location-dependent roles: cleaners, labourers, agricultural helpers, and construction helpers. They score very low on AI substitution potential because AI cannot yet physically perform these tasks at comparable cost. Skilled agricultural workers score 3.0/10 across 102,500 workers (9.11%). Oman's agricultural sector is notably diverse for a Gulf state: the Batinah coast north of Muscat supports date palm cultivation; the Dhofar region in the south has a distinct monsoon climate supporting cattle and frankincense; and the interior wadis support small-scale farming communities. The NCSI records a relatively large agricultural share by Gulf standards, reflecting Oman's geography and more dispersed rural population compared to Qatar or Kuwait.

Plant and machine operators score 3.0/10 across 65,400 workers (5.82%). This group includes LNG and oil processing operators at PDO and Oman LNG - primarily in Sur - as well as utilities operators and industrial machine operators. The absence of a craft/trades group (ISCO 7) in the 2008 NCSI data is the main structural anomaly in this dataset. In 2026, Oman has a substantial construction and trades workforce; the 2008 data likely coded some of these workers under adjacent categories or the survey methodology did not disaggregate them as a separate major group.

What this means for workers

For Omani clerical workers - the group that Omanisation most directly placed in roles now facing AI pressure - the next five years represent a policy test case. Oman's Ministry of Labour cannot credibly tell employers to hire Omani nationals in administrative roles while simultaneously allowing AI to eliminate those roles without transition support. The most likely Omani government response, based on stated Vision 2040 commitments, is to pair any AI-driven clerical displacement with funded retraining toward digital and technical roles - using the SGRF to finance the transition period.

Oman's recovery resilience of 6.9 reflects a realistic assessment. Oman's sovereign wealth fund (SGRF) is smaller than Kuwait's KIA or Abu Dhabi's ADIA, and Oman's fiscal position is more sensitive to oil price movements than Qatar or the UAE - Oman's break-even oil price has historically been higher than the Gulf average. The Vision 2040 ambition is real, but the fiscal resources to execute it at Qatar-level speed are not available. Workers facing displacement in Oman are more exposed than their Qatari counterparts with equivalent resilience scores.

The long-term trajectory under Oman Vision 2040 is toward an economy that looks more like the UAE's 2026 profile: diverse revenue base, large knowledge economy, and reduced dependence on hydrocarbon employment. For workers entering the Omani workforce today, AI-relevant skills development through Omani universities and polytechnics is the clearest available pathway. The 2008 baseline data in this analysis captures where Oman was, not where it is heading - and the direction is consistently upward in skill complexity, which is generally AI-resilient rather than AI-threatened territory.

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