By Abdulaziz Khattak
Saudi Arabia’s private-sector workforce is entering a decisive phase of structural change as generative artificial intelligence (GenAI) converges with the energy transition, exposing a large share of jobs to automation while simultaneously creating pockets of resilience in environmentally aligned occupations.
Between 75 per cent and 80 per cent of Saudi citizens in private employment are in roles highly exposed to AI, with nearly half concentrated in positions most vulnerable to displacement.
A discussion paper by the King Abdullah Petroleum Studies and Research Centre (KAPSARC) analysed labour market data from 2018 to 2022 using a complementarity-adjusted exposure framework, and found that the distributional impact of AI will depend not only on the extent to which tasks can be automated, but also on whether AI complements or substitutes human labour.
This dual lens reveals a labour market characterised by both productivity-enhancing opportunities and significant displacement risks.
AI EXPOSURE RESHAPES OCCUPATIONAL RISK
The defining feature of GenAI, particularly large language model-based systems, is its capacity to automate non-routine cognitive tasks, extending beyond the manual and repetitive functions targeted by earlier waves of automation.
Tasks, such as document drafting, data analysis, and customer interaction that are core to many white-collar roles are now susceptible to automation, shifting the risk profile of employment across sectors.
The study identifies three principal mechanisms through which AI affects employment: Displacement, where human labour is replaced; productivity enhancement, where AI augments worker output; and job creation, where entirely new roles emerge.
The net employment effect depends on the balance between these forces, but the distributional consequences are uneven.
Occupations are categorised into four groups based on exposure and complementarity.
High Exposure, Low Complementarity (HELC) roles, which primarily include clerical and administrative jobs, face the greatest risk, as AI can substitute for many of their tasks without enhancing worker productivity.
In contrast, High Exposure, High Complementarity (HEHC) roles, such as management and technical professions, are more likely to benefit from productivity gains.
In Saudi Arabia, nearly half of all private-sector employment falls into the HELC category, underscoring a significant vulnerability.
This is particularly evident in sectors such as construction, manufacturing, and retail, where Saudi nationals are often concentrated in administrative rather than manual roles, increasing exposure to automation.
The occupational structure also reveals a potential “hollowing out” of the labour market.
Medium-skilled roles, especially clerical support positions, are disproportionately at risk, while high-skilled occupations benefit from AI augmentation and low-skilled manual roles remain relatively insulated due to limited exposure.
This polarisation mirrors patterns observed in other advanced and emerging economies.
Demographic factors further amplify these risks. Women are disproportionately represented in HELC occupations, accounting for 55 per cent of such roles in 2022 compared with 39 per cent for men.
This reflects occupational sorting into administrative and service roles, which are highly exposed to automation.
Younger workers also face elevated risk, with more than half of those under 25 employed in HELC roles, compared with 35 per cent among workers aged 45-49.
Older workers, by contrast, tend to occupy roles with higher complementarity, benefiting more from AI-driven productivity gains.
GREEN OCCUPATIONS SHOW LOWER VULNERABILITY & HIGHER COMPLEMENTARITY
Against this backdrop of widespread exposure, green and environmentally related occupations present a more resilient segment of the labour market.
Defined as roles incorporating tasks that contribute to environmental sustainability or the energy transition, green jobs account for a relatively small share of employment, at around 14-15 per cent in 2022 under a strict classification threshold, but exhibit markedly different exposure characteristics.
More than 60 per cent of green occupations fall into the HEHC category, indicating strong potential for AI to enhance productivity rather than replace labour.
In addition, a significant share of these roles is classified as Low Exposure, Low Complementarity (LELC), suggesting minimal interaction with AI technologies and, therefore, limited risk of displacement.
This dual profile reflects the skill distribution within green jobs, which tend to cluster at both the high and low ends of the spectrum.
High-skilled roles, such as engineers and sustainability specialists, benefit from AI augmentation, while lower-skilled positions, including waste management and recycling, remain largely unaffected due to their manual nature.
Crucially, green occupations avoid the concentration in medium-skilled roles that characterises much of the broader labour market’s vulnerability.
Brown jobs, defined as occupations concentrated in high-emission sectors, display a similar pattern of relatively high complementarity and low exposure to displacement.
However, their long-term outlook is shaped less by AI and more by environmental policy and decarbonisation pressures, which may reduce demand for labour in these sectors over time.
An important overlap exists between green and brown occupations, reflecting shared skill sets that could facilitate workforce transitions.
Roles that combine environmental and industrial characteristics exhibit high levels of complementarity, with nearly 90 per cent of employment in this category benefiting from AI augmentation.
This suggests potential pathways for reskilling workers displaced from carbon-intensive industries into greener roles.
Despite these advantages, the overall share of green and brown employment remains limited, with more than 80 per cent of Saudi private-sector jobs classified as environmentally neutral.
This segment closely mirrors the broader labour market’s exposure profile, with a dominant presence of HELC roles and correspondingly higher vulnerability to AI-driven displacement.
The findings highlight the importance of aligning workforce development strategies with both technological and environmental trends.
As GenAI continues to reshape task structures across occupations, and as the energy transition drives demand for sustainable skills, the intersection of these forces will define the resilience and adaptability of the labour market.