Brunei AI Job Risk 2026: Which of 223k Workers Are Most Exposed?

222,720 workers, ILO ILOSTAT 2024 data

Employment data comes from ILO ILOSTAT (CC BY 4.0), based on the Department of Economic Planning and Statistics (DEPS) Brunei Labour Force Survey 2024, using ISCO-08 major group classifications. The 2024 data covers 222,720 employed workers in Brunei Darussalam. DEPS is the statistical agency of the Prime Minister's Office and publishes labour force data with consistent methodology under the ASEAN statistical framework. Brunei's formal employment coverage is high: labour laws, oil sector regulations, and a large public sector mean that most employment is formally registered and captured in the Labour Force Survey.

Brunei's economy is defined by its hydrocarbon wealth to a degree unusual even among oil states. Petroleum and natural gas account for approximately 60% of GDP and over 90% of export earnings. The two principal operators are Brunei Shell Petroleum (BSP), a joint venture between the Government of Brunei (50%) and Shell (50%), and Brunei LNG, which operates one of the world's largest liquefied natural gas export facilities at Lumut on Brunei Bay. Together these entities and their contractors employ a significant share of Brunei's professional and technical workforce, supplemented by the government's Wawasan Brunei 2035 diversification strategy which has sought to build financial services, halal food production, and information and communications technology sectors - so far with limited results in shifting the employment base away from hydrocarbons and public service.

The 2024 DEPS Labour Force Survey data reflects a workforce that is structurally unusual in several ways. The citizen-to-migrant worker ratio is roughly 2:1, but ISCO classifications do not distinguish citizenship. Elementary occupations and craft workers skew heavily toward migrant workers; professionals and managers skew toward Brunei citizens and permanent residents employed in the public sector and oil companies. The 0.91% agriculture share is the lowest of any country in this Tier 3 batch - Brunei's food security strategy relies entirely on imports, primarily from Malaysia, and the small agricultural sector consists of aquaculture, poultry, and vegetable production for the domestic market under government support schemes, with no commercially significant export crops.

The most AI-exposed jobs in Brunei

Clerical support workers score 8.5/10 - the maximum end of AI exposure - and cover 23,130 workers at 10.39% of employment. In Brunei's context, clerical workers are concentrated in three sectors: the civil service of the Government of Brunei (which employs a large proportion of Brunei citizens in ministries, statutory bodies, and the Prime Minister's Office), the administrative functions of Brunei Shell Petroleum and its contractor ecosystem, and the banking and financial services sector anchored by Bank Islam Brunei Darussalam (BIBD) and the development bank Baiduri. These workers process administrative documentation, maintain financial records, manage correspondence and procurement, and perform regulatory filing functions. Their tasks - structured, rule-based, text-heavy, often repetitive - are precisely the workflows that AI tools and robotic process automation handle most effectively.

AI adoption in Brunei's oil sector is not a future projection. Shell's global digital transformation program - which includes AI-assisted process monitoring, predictive maintenance, document processing, and geological data analysis - applies to Brunei Shell Petroleum operations under the same group-level technology roadmap as Shell's operations in the Netherlands, Nigeria, and Kazakhstan. BSP has deployed AI-assisted tools for reservoir simulation and well performance monitoring at the Seria oilfield (Brunei's primary onshore production site, operated continuously since 1929). The clerical and administrative workers who support these operations face the same AI augmentation timeline as their equivalents in Shell's European headquarters, with a 2-4 year lag reflecting Brunei's smaller scale and the additional layer of government joint-venture approval requirements.

Professionals at 6.5/10 (39,720 workers, 17.84%) are Brunei's largest high-exposure group by worker count. This group spans petroleum engineers at BSP, process engineers at Brunei LNG, medical professionals at RIPAS Hospital and the private Jerudong Park Medical Centre, academics and researchers at Universiti Brunei Darussalam (UBD) and Universiti Teknologi Brunei (UTB), and legal and financial professionals in the commercial and government sectors. AI tools for geological analysis, medical imaging, research synthesis, and financial modelling are advancing fastest in exactly the fields that dominate Brunei's professional class. The trajectory is augmentation - more output per professional - rather than replacement on most 5-year horizons, but the productivity differential between AI-enabled and non-AI-enabled professionals will widen significantly over this period.

Why professionals and technicians dominate Brunei's AI risk profile

The 32.64% combined share of professionals and technicians in Brunei's workforce is among the highest of any country in the Tier 3 batch. The reason is the oil and gas sector's labour requirements. Producing and liquefying hydrocarbons at industrial scale requires a large educated workforce: reservoir engineers who model subsurface geology, production engineers who optimise well output, process engineers who manage the LNG train operations at Lumut, laboratory chemists who test product quality, and IT engineers who maintain the industrial control systems that run both the Seria oilfields and the Lumut LNG plant. For a country of Brunei's population, this creates a professional-to-total-worker ratio that is structurally higher than any economy not built on hydrocarbons or financial services.

The AI exposure implication is that Brunei's AI risk is concentrated at the top and middle of the skill distribution, not at the bottom. In most developing economies, the highest-exposure workers are clerical and the majority are in agriculture or elementary occupations that dilute the average down. In Brunei, the low-exposure groups - elementary occupations (14.06%), craft workers (8.92%), and the small armed forces (2.82%) - are outweighed by the large professional and technical classes. Service and sales workers at 20.12% (44,810 workers, 3.5/10) are the largest single group and provide the primary downward weight on the average, but even this group scores at mid-low range rather than the very low scores of agricultural economies.

Technicians and associate professionals at 14.80% (32,960 workers, 5.5/10) include process control technicians at BSP's Enhanced Oil Recovery projects at the Seria field, laboratory analysts at the Brunei LNG quality control function, medical imaging technicians and nurses at RIPAS Hospital, and ICT technicians supporting Brunei's e-government infrastructure under the iGovt initiative. These roles face AI augmentation through process monitoring AI (already deployed at analogous Shell facilities globally), diagnostic assistance tools in healthcare, and coding assistance and IT management tools in the government technology sector. The 5.5/10 score reflects that these roles involve substantial technical judgment and physical-process interaction that limits full automation - but they are not low-risk occupations on any 10-year horizon.

The safest jobs in Brunei

Elementary occupations score 2.0/10 - the lowest AI exposure of any group - covering 31,320 workers at 14.06% of Brunei's workforce. This is one of the more significant elementary occupations shares in the Tier 3 batch, and it reflects Brunei's heavy reliance on migrant labour for construction, domestic work, and general labouring. Construction workers building the ongoing infrastructure development under Wawasan Brunei 2035 - road extensions, the Temburong Bridge and its connecting infrastructure, new government facilities - perform physical, site-specific, variable work that AI and robotics cannot cost-effectively automate in Brunei's market context on near-term timelines. Domestic workers in private households (a significant category given Brunei's high household incomes from oil wealth) perform similarly non-automatable physical tasks.

The Royal Brunei Armed Forces at 2.82% (6,280 workers, 2.5/10) is a meaningful employer for a state of Brunei's size. Sultan Hassanal Bolkiah, as Prime Minister and Minister of Defence, has maintained a defence posture that includes an air force (with F/A-18D Hornets), navy (with offshore patrol vessels), and army (with Challenger 2 tanks - an unusual capability for a small tropical nation). Armed forces roles score low on AI exposure: physical presence, security judgment, and operational command functions are not automatable on civilian AI timelines, and military AI deployment follows separate governmental decision paths not captured in commercial AI adoption timelines.

Craft and trades workers at 8.92% (19,870 workers, 2.5/10) include construction tradespeople supporting Brunei's infrastructure pipeline, electricians and mechanical technicians working on oil sector surface facilities, and automotive and equipment repair workers. A welder maintaining pipeline infrastructure at the Seria oilfield, or an electrician wiring a new government building in Gadong, is performing site-specific physical work that current robotics cannot replicate at Brunei market cost points. For a comparison with a neighbouring oil-state economy facing similar AI dynamics, see the analyses of Malaysia and Singapore.

What this means for Brunei workers

Brunei's 4.63/10 weighted average puts it solidly in the amber range and close to the red threshold of 5.0. The structural driver - a professional and technical class that is large relative to population because of oil sector requirements - is not going to change on any near-term policy horizon. Wawasan Brunei 2035 aims to diversify the economy, but the track record of oil-state diversification globally suggests that labour market structure follows economic structure with a long lag. Brunei workers in every occupation group should plan for an AI landscape shaped by the sector-level AI adoption decisions of Brunei Shell Petroleum, the Government of Brunei, and Brunei LNG - all of which have parent organisations or international comparison points that are already deploying AI tools.

For the 23,130 clerical workers, the practical implication is that AI tools are being deployed in their parent organisations' comparable operations now. A civil service administrator in a Brunei government ministry processes documents using workflows that are essentially identical to those in Malaysian, Singaporean, and UK civil service departments where AI-assisted document management and correspondence tools are being piloted and in some cases standardised. The Government of Brunei's iGovt digital transformation initiative includes workflow automation components that will progressively reduce the headcount required for routine administrative processing. Workers in these roles who can position themselves as managers or auditors of AI-produced outputs - rather than processors of manual workflows - are better placed than those who remain purely in execution roles.

For professionals and technicians - the 72,680 workers in these two groups who together represent 32.64% of Brunei's workforce - the AI augmentation timeline is faster in some respects and slower in others. Engineers at BSP face AI tools for geological modelling and production optimisation that Shell is deploying group-wide. Medical professionals at RIPAS face AI diagnostic assistance tools whose deployment in Brunei follows Ministry of Health approval timelines. Academics at UBD face AI tools that are already transforming research and teaching globally. In all cases, the near-term practical guidance is the same: develop AI tool proficiency in the specific platforms relevant to your sector, treat AI as a productivity multiplier rather than a threat to resist, and invest in the judgment and relationship skills that remain differentiators even as AI handles more of the routine technical tasks. For a broader regional perspective, see the analyses of the United States, the United Kingdom, and how the US compares to world averages.