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AI threatens to quickly automate clerical and administrative roles in low-income countries — some of the few better‑quality jobs and a vital pathway to decent work, especially for women and young people, the ILO warns.
Generative artificial intelligence (GenAI) is set to reshape labour markets worldwide, but with uneven impacts across countries. In fact, in developing economies, disruption may materialise faster than productivity gains due to existing digital gaps and differences in how work is performed.
A joint working paper by the International Labour Organization (ILO) and the World Bank examined how GenAI might reshape jobs globally, with a particular focus on the uneven spread of risks and opportunities between advanced and developing economies.
The authors found that while developing economies are, on paper, less exposed to outright automation than advanced ones, they have a similar potential to use GenAI to augment existing tasks. The problem lies in the digital divide. Workers in jobs most vulnerable to automation often maintain sufficient internet connectivity to experience displacement effects even in low-income settings. In contrast, many of those who could benefit from GenAI tools face substantial digital infrastructure gaps that may prevent them from realising productivity gains.
Let’s unpack some of the report’s key findings.
Exposure to GenAI is higher in advanced economies
Across countries, exposure to generative AI tends to rise with income. Advanced economies see the highest shares of jobs touched by GenAI: in the US and France, 30% or more of total employment is exposed. At the other end of the spectrum, lower-income countries such as Ethiopia and Zimbabwe have exposure rates closer to 10% or less.
Most of this exposure — about 17% of jobs globally — falls under the “moderate” category, where GenAI is more likely to support and augment work rather than replace it. Only around 8% of jobs are in the higher-risk bands where automation becomes a real possibility.
This asymmetry suggests that there is a large gap between lower-income and higher-income countries when it comes to automation risk, but a much smaller one for augmentation. That asymmetry matters as the potential productivity benefits of GenAI are spread more evenly across countries than the automation risks, which are concentrated in richer economies. On paper at least, developing countries could be relatively better placed to gain from GenAI than to lose jobs to it.
The digital divide creates an asymmetry between risks and benefits.
Internet access is a precondition for using GenAI tools. When exposure is adjusted for connectivity, income gaps widen further. The paper identified a “small buffer, big bottlenecks” dynamic in developing countries:
- Workers in jobs facing automation exposure are often already connected and therefore may experience displacement pressures relatively quickly.
- Workers in jobs with augmentation potential frequently lack reliable internet access, limiting their ability to realise productivity gains.
Across the countries with detailed data, 441.8mn jobs fall into augmentation-oriented exposure gradients. Of these, around 66.9mn lack internet access, representing unrealised productivity potential.
Exposure to automation concentrates in relatively better jobs in poorer countries
In low- and lower-middle-income countries, the share of jobs exposed to automation is smaller overall. However, these jobs often represent:
- Formal, higher-quality service-sector roles
- Occupations disproportionately held by women and younger workers
- Entry-level clerical and administrative positions that historically served as pathways into decent work.
This raises the risk of a “white-collar bypass”, where office-based jobs that supported upward mobility and women’s labour force participation in advanced economies may not fully materialise in today’s developing countries.
Standard exposure indices overestimate impact in developing countries
A key contribution of the paper indicated that occupational titles do not reflect identical task content across countries.
Using PIAAC and STEP data for 46 countries, the paper found that:
- Workers in developing countries perform fewer non-routine analytical tasks — even within occupations classified as highly exposed.
- The same occupation (at ISCO level) can involve more routine or manual tasks in lower-income contexts.
When country-level exposure scores are adjusted to reflect actual task content, many developing countries move lower in the exposure ranking. This suggests that global exposure measures based solely on occupational structure may overstate GenAI’s transformative potential in lower-income settings.
Distributional dimensions
Across countries:
- Exposure increases with education and household income.
- Women and younger workers are overrepresented in occupations facing higher automation exposure, especially in upper-middle- and high-income countries.
- In poorer countries, the digitally connected segment of skilled workers may face earlier labour-market pressures.
Sectorally, financial and business services show high exposure across all income levels, but connectivity gaps remain significant in low-income countries.
Policy implications
GenAI’s labour-market impact will depend not only on technological capability but on:
- Digital infrastructure
- Task organisation within occupations
- Education and skills systems
- Labour-market institutions and social protection
For developing countries in particular:
- Expanding digital connectivity is essential to enable augmentation and productivity gains.
- Policies should support skills upgrading, especially in non-routine analytical and digital tasks.
- Strengthened labour-market institutions and social protection systems will be critical to manage potential displacement pressures.
- Gender-sensitive policies are needed to mitigate risks in clerical and administrative occupations.
In conclusion, while GenAI is often talked about as a global phenomenon, its adoption and labour market impact will be uneven. Unless governments and firms invest in digital infrastructure and active labour-market policies, most of the productivity gains are likely to flow to places with fewer digital bottlenecks — mainly richer countries and better-equipped companies. For connected workers in poorer countries, however, the risk of being displaced by GenAI may arrive much sooner than the benefits.
Infographics / ILO
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