Which jobs could face automation from GenAI?
Workers who are women, working in urban areas, younger, non-poor, in formal sectors (especially in banking, finance, or public administration), or have higher education are more exposed to automation through GenAI. The potential loss of well-paid, formal, and skilled jobs in industries that are dominated by women due to GenAI automation would have negative impacts for the already highly informal and unequal economies in the region.
Will the low level of digital inclusion act as a buffer for the automation impacts?
The short answer is no. Most workers who are exposed to automation from GenAI are already using digital technologies in their job, thereby the potential negative effects for this group of workers may not take long to materialize.
Which jobs are most likely to benefit from GenAI?
The potential transformative benefits of GenAI on jobs are more equally distributed among workers in terms of gender and age, but they are still more likely to affect formal jobs that are in urban areas and held by higher-educated and higher-income workers. Salaried and self-employed workers – such as hairdressers, salespersons, architects, or real estate agents – and those working in education, health, or personal services are more likely to benefit from the transformative effects of GenAI.
What is preventing workers who might benefit from GenAI from realizing this potential?
A large number of workers who stand to benefit from greater productivity from GenAI are often in jobs that do not use digital technologies at work, particularly in the region’s poorest countries. A lack of access to digital technologies and infrastructure could hamper nearly half of the jobs that could benefit from greater productivity from GenAI, preventing these workers from realizing its full potential. This is equal to about 7 million jobs held by women and 10 million jobs held by men.
The potential loss in productivity due to this gap in digital access would have a greater impact on workers living in poverty. For example, in Brazil, while 8.5 percent of workers living in poverty could benefit from GenAI, only 40 percent of them would be able to do so because they use digital technologies at work. In contrast, 14 percent of workers who are not living in poverty could benefit from GenAI, and 60 percent of them could reap such gains because they use digital technologies.