Generative AI and Jobs: A Refined Global Index of Occupational Exposure

Our 2025 task-level scores are broadly consistent with those of the ILO 2023 study (Gmyrek et al., 2023), although the mean scores at the level of occupations are slightly lower (0.29 in 2025 compared to 0.30 in 2023). However, the score dispersion within occupations is considerably narrower, resulting in a more concentrated overall distribution (Figure 3).

Similar to our 2023 study, clerical occupations exhibit the highest levels of exposure, though with a decline in their mean scores due to a reassessment of certain task-level evaluations (Figures 18 & 19). This decrease highlights the distinction between the theoretical automation potential of a task and the practical insights gained from two years of experimentation with GenAI tools. While tasks such as taking meeting notes or scheduling appointments can significantly benefit from GenAI support, our 2023 scores for such tasks, which reached 0.9 in some cases, reflected an overly optimistic assessment of full automation potential. In reality, many of these tasks still require substantial human effort. Despite the decline in overall mean scores for these occupations, many still fall within the highest exposure gradient (Level 4), since the final classification does not depend solely on the mean score but is determined through a simultaneous assessment of each occupation’s mean and the dispersion of corresponding task-level scores (SD, see Figure 16 and Table 5). Among the 13 occupations classified in gradient 4, the majority represent clerical roles, including data entry clerks, typists and word processing operators, accounting and bookkeeping clerks, and general office clerks.

On the other hand, several highly digitized occupations have seen an increase in their mean scores. This applies, for example, to web and media developers, statistical and database specialists, as well as financial and software-related occupations (Figure 17). A key factor behind this shift is the rapid expansion of AI capabilities since our previous study in 2023. At that time, large language models (LLMs) were primarily viewed as advanced text generators. Since then, they have acquired multimodal abilities, allowing them to process and generate not only text but also images, audio, and video. Additionally, these models have been gaining some agentic capabilities, allowing them to execute multi-step tasks autonomously, interact with software environments, and make decisions based on contextual inputs. These advancements significantly broaden the scope of tasks that can be at least partially automated using GenAI, with software development and multimedia sectors among some of the leading adopters.

While many of these occupations fall within gradient 3 and 4, one must consider that these roles are deeply intertwined with digital technologies, and indeed, many of these occupations have emerged as a result of technological advancements. The scores reflect the high potential of automation among their current tasks, but these occupations also possess a strong capacity to evolve and develop new tasks through innovative application of GenAI and related digital tools. In previous technological transitions, this type of adaptation was exemplified by the well-known case of bank tellers following the diffusion of automatic teller machines (ATMs) in the 1990s (Bessen, 2016). As ATMs automated routine cash withdrawals, they also lowered operational costs, enabling banks to open more branches and, in turn, increasing the demand for bank tellers in roles that required more complex customer service and advisory tasks. One can therefore expect many tasks in these occupations not to remain static but rather to evolve alongside technological advancements, driven by the expanding capabilities of GenAI. A key question – largely dependent on the policies shaping this evolution – is to what extent these jobs can retrain and retain existing workers, rather than drive labor churn through workforce replacement and the recruitment of typically younger specialists (Ananian et al., 2006).