This study from the International Labour Organisation examines the global impact of Generative AI, particularly Generative Pre-Trained Transformers (GPTs), on various occupations and job quality. Using the GPT-4 model, it assesses potential exposure at the task level and estimates its effects on employment globally and by income group. Overall, the technology is likely to augment work by automating some tasks within occupations, preserving other responsibilities, rather than fully automating entire professions.
This study presents a global analysis of the potential exposure of occupations and tasks to Generative AI, and specifically to Generative Pre-Trained Transformers (GPTs), and the possible implications of such exposure for job quantity and quality. It uses the GPT-4 model to estimate task-level scores of potential exposure and then estimates potential employment effects at the global level as well as by country income group. Despite representing an upper-bound estimate of exposure, we find that only the broad occupation of clerical work is highly exposed to the technology with 24 per cent of clerical tasks considered highly exposed and an additional 58 percent with medium-level exposure. For the other occupational groups, the greatest share of highly exposed tasks oscillates between 1 and 4 per cent, and medium exposed tasks do not exceed 25 per cent. As a result, the most important impact of the technology is likely to be of augmenting work – automating some tasks within an occupation while leaving time for other duties – as opposed to fully automating occupations.
The potential employment effects, whether augmenting or automating, vary widely across country income groups, due to different occupational structures. In low-income countries, only 0.4 per cent of total employment is potentially exposed to automation effects, whereas in high-income countries the share rises to 5.5 percent. The effects are highly gendered, with more than double the share of women potentially affected by automation. The greater impact is from augmentation, which has the potential to affect 10.4 percent of employment in low-income countries and 13.4 percent of employment in high-income countries. However, such effects do not consider infrastructure constraints, which will impede the possibility for use in lower-income countries and likely increase the productivity gap.
We stress that the primary value of this analysis is not the precise estimates, but rather the insights that the overall distribution of such scores provides about the nature of possible changes. Such insights can encourage governments and social partners to proactively design policies that support orderly, fair, and consultative transitions, rather than dealing with change in a reactive manner. Moreover, the likely ramifications on job quality might be of greater consequence than the quantitative impacts, both with respect to the new jobs created because of the technology, but also the potential effects on work intensity and autonomy when the technology is integrated into the workplace. For this reason, we also emphasize the need for social dialogue and regulation to support quality employment.