use at work across ISCO 2-digit occupations, workers and country-level characteristics based on PIAAC data and by subsequently imputing them into individual observations in country-level surveys included in the SEDLAC database. We then use this measure to create two categories among workers who are expected to benefit from GenAI use because of the nature of their occupations: those who have access to digital technologies, and those who do not. The size of the latter is an indicator of the number of workers who will not be able to enjoy the productivity benefits of GenAI even though their jobs could theoretically benefit from the transformation. We also discuss the detailed demographics of the groups that are most likely to be negatively affected by these infrastructure limitations.

Our findings indicate that between 30 and 40 percent of employment in the LAC is exposed in some way to GenAI. This exposure is linked with the economic status of countries, suggesting that income levels are a strong correlate of GenAI’s impact on labor markets. This total level of exposure includes three categories: exposed to automation, augmentation, and “the big unknown”. The latter includes occupations, which – depending on the progress of technology and the use of adjacent technological applications, such as LLM-based agents – could fall closer to automation or augmentation.

Certain characteristics consistently correlate with higher overall GenAI exposure. Specifically, urban-based jobs that require higher education, are situated in the formal sector, and are held by individuals with higher relative incomes are more likely to come into interaction with this technology. The share of jobs exposed to automation is relatively small but nontrivial at about 2 to 5 percent of total employment. Younger and female workers tend to face greater automation exposure, particularly in the finance, insurance, and public administration sectors. At the same time, the shares of jobs that could benefit from a productive transformation with GenAI are consistently higher than those with automation risks across all LAC countries, ranging between 8 and 12 percent of employment across countries. This is particularly the case for the jobs in education, health and personal services. In addition, the sectors oriented towards customer service (retail, trade, hotels, restaurants, etc.) face an elevated exposure to "the big unknown". This category encompasses the largest (14-21 percent) share of employment in our estimates, demonstrating that, while the concept of occupational exposure is easier to establish, the precise effects on how many occupations might evolve are harder to predict for a large share of today’s labor markets.

Finally, we find that access to digital technologies is a critical determinant of the extent to which workers can harness the potential benefits of GenAI. Nearly half of the positions that could potentially benefit from augmentation are hampered by digital shortcomings that will prevent them from realizing that potential. Specifically, 6.24 percent of jobs held by women and 6.22 percent of those held by men are affected due to these gaps. Similar limitations apply to the jobs in the “big unknown” category: even though some of them could potentially pivot towards augmentation through increasing complementarity between GenAI and the human worker in these occupations, the digital gaps will prevent large shares of these jobs from such a scenario.

The rest of this study is structured as follows: section 2 provides a general overview of the LAC region and elaborates on the theoretical effects one could expect from the interaction of GenAI 3