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What regional estimates say about generative AI and jobs
A 2024 working paper by the International Labour Organization (ILO) and World Bank models how generative AI could affect employment across Latin America and the Caribbean. It separates broad task exposure from potential productivity-enhancing change and the narrower possibility of full automation:
| Modeled outcome | Regional estimate | What it means |
|---|---|---|
| Jobs exposed to generative AI | 26–38% | Some tasks could be affected by the technology; this does not mean the jobs will disappear. |
| Jobs that could be transformed to improve productivity | 8–14% | AI could complement or alter work in ways that support productivity. |
| Jobs potentially at risk of full automation under current capabilities | 2–5% | A modeled category of potential full automation, not a count of jobs already lost. |
These are modeled estimates from the ILO–World Bank’s 2024 working paper, not observed employment outcomes. Exposure is not the same as replacement: a job can include tasks that AI may assist with while still requiring human judgment, interaction, or other work. The estimates therefore do not support reading the broad exposure range as a predicted job-loss rate.
Why country estimates can look very different
An Inter-American Development Bank (IDB) paper published in September 2024 measures large language model (LLM) task exposure in Chile, Mexico, and Peru. It maps US O*NET occupations to regional classifications SINCO-2011 and ISCO-2008 and adjusts exposure ratings to account for countries’ capacity to adopt and implement technologies. The shares vary sharply with the threshold used to call a job exposed:
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| Country | Jobs above 10% task exposure | Jobs at 40% or more task exposure |
|---|---|---|
| Mexico | 74% | 9% |
| Chile | 76% | 20% |
| Peru | 76% | 6% |
The figures are the IDB paper’s modeled task-exposure shares, not estimates of unemployment or job losses. A 10% threshold counts jobs with relatively limited exposure, while a 40% threshold selects a narrower group. The paper’s crosswalk and adoption-capacity adjustments also mean its figures should not be treated as interchangeable with the ILO–World Bank regional estimates. See the IDB paper for its methodology.
Who is more exposed—and who may benefit?
The ILO–World Bank analysis finds that potential exposure and potential gains are unevenly distributed. It describes greater automation exposure among women, urban residents, younger and more educated workers, and people in formal-sector jobs. Potential productivity gains are also more likely in urban, educated, and formal work, and among higher-income earners. These patterns do not mean every person in those groups faces the same outcome; they show why a single regional average can conceal unequal risks and opportunities.
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Access matters as well as exposure. The study estimates that digital-access and infrastructure gaps could hinder productivity gains for about 17 million jobs—roughly half of the jobs it identifies as potentially benefiting. It says those barriers would weigh more heavily on workers living in poverty. In practice, a task that technology could assist may not yield a productivity gain if a worker or workplace lacks reliable digital access or the means to adopt the tool. These findings and distributional patterns are discussed in the ILO–World Bank working paper.
What AI job-posting data can—and cannot—show
A separate IDB analysis tracks online job postings in 15 countries. Using a keyword dictionary to identify vacancies related to AI, it reports that those postings reached 7% of online vacancies in June 2025. This is evidence of advertised demand for AI-related skills under that method; it is not a measure of the share of workers employed in AI jobs, total employment, or jobs created across the whole economy. Online vacancies also have a different denominator from task-exposure models. The source is the IDB’s report on online job postings.
How AI could affect productivity across the economy
Task-exposure studies are only part of the economic picture. A January 2026 report from the Economic Commission for Latin America and the Caribbean (ECLAC) examines AI through a theoretical and econometric model of skilled-labor productivity and the interactions among production factors. Its publication summary identifies low AI investment and constraints in human-capital formation as obstacles to realizing potential gains. The available summary does not give country-level numerical results, so it cannot support a country-by-country ranking or a specific regional growth estimate. See the ECLAC report page.
For the region, the distinction is important: a technology’s capacity to affect tasks does not establish how widely firms will adopt it, how much output or productivity will change, or how those changes will be distributed. As William Maloney, then World Bank Chief Economist for Latin America and the Caribbean, put it in an ILO release on 31 July 2024: “In a region where growth is low, inequality remains unacceptably high and one in four households still experience poverty, improving productivity and job quality is critical.” The ILO release gives the quotation and context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence suggests for workers, employers, and policymakers
For workers
Exposure is a reason to pay attention to how tasks are changing, not proof that a particular occupation will vanish. The ILO identifies lifelong learning and foundational skills for using generative AI at work as relevant policy priorities. The cited evidence does not establish that a particular training provider or course is effective across the region.
For employers
Assess tasks and workplace access before describing a role as replaceable or enhanced. The exposure estimates do not account for every organization’s ability to adopt tools or the full set of duties performed in a job. Whether AI complements workers or substitutes for some tasks will depend on implementation as well as technical capability.
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For policymakers
The ILO’s recommendations connect lifelong learning and foundational AI skills with social protection for workers in transition, investment in digital infrastructure, and support for informal workers seeking formal employment. These are recommendations, not proof that any single intervention has already produced a measured region-wide effect. Together with the uneven access identified in the working paper, they point to adoption capacity and worker protections as part of the same policy challenge. See the ILO–World Bank working paper.
How to read the figures without conflating them
- Task exposure estimates whether AI could affect work tasks; it does not count jobs lost.
- Potential productivity transformation describes a possible way work could change, not a measured increase in output.
- Full-automation risk is a narrower modeled possibility under current capabilities, not a prediction that every job in the category will be automated.
- Vacancy shares track online advertisements containing AI-related keywords, not workers or total employment.
- Macroeconomic models consider productivity and production factors at a broader level; they answer a different question from occupational exposure analysis.
Because geography, methods, thresholds, and outcomes differ, the percentages in these studies should not be added together or compared as if they measured one common effect.
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