Geneva - Generative Artificial Intelligence (AI) is more likely to augment than destroy jobs by automating some tasks rather than taking over a role entirely, a new study from the International Labour Organization (ILO) has found.
The study, Generative AI and Jobs: A global analysis of potential effects on job quantity and Quality, suggests that most jobs and industries are only partly exposed to automation and are more likely to be complemented rather than substituted by the latest wave of Generative AI, such as chatGPT. Therefore, the greatest impact of this technology is likely to not be job destruction but rather the potential changes to the quality of jobs, notably work intensity and autonomy.
Clerical work was found to be the category with the greatest technological exposure, with nearly a quarter of tasks considered highly exposed and more than half of tasks having medium-level exposure. In other occupational groups – including managers, professionals, and technicians – only a small share of tasks was found to be highly exposed, while about a quarter had medium exposure levels.
The study, which is global in scope, documents notable differences in the effects on countries at different levels of development, linked to current economic structures and existing technological gaps. It finds that 5.5 percent of total employment in high-income countries is potentially exposed to the automating effects of the technology, whereas in low-income countries, the risk of automation concerns only some 0.4 percent of employment. On the other hand, the potential for augmentation is nearly equal across countries, suggesting that with the right policies in place, this new wave of technological transformation could offer important benefits for developing countries.
The potential effects of Generative AI are likely to differ significantly for men and women, the study finds, with more than twice the share of female employment potentially affected by automation. This is due to women’s over-representation in clerical work, especially in high and middle-income countries. Since clerical jobs have traditionally been an important source of female employment as countries develop economically, one result of Generative AI could be that certain clerical jobs may never emerge in lower-income countries.
The paper concludes that the socioeconomic impacts of Generative AI will largely depend on how its diffusion is managed. It argues for the need to design policies that support an orderly, fair, and consultative transition. Workers’ voices, skills training, and adequate social protection will be key to managing the transition. Otherwise, there is a risk that only a few, well-prepared countries and market participants will benefit from the new technology.
The authors note that the “outcomes of the technological transition are not pre-determined. It is humans that are behind the decision to incorporate such technologies and it is humans that need to guide the transition process”.