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Paper A: Exposure explores exposure to Gen AI at the task level among occupations, industries and cohorts. It highlights where exposure skews more towards augmentation and where automation potential is concentrated, including for particular groups in the workforce. Gen AI’s impact depends on exposure, adoption, and adaptation. The study uses a framework where Gen AI’s labour market effects are shaped by how widely it can be applied (exposure), how deeply it is adopted, and how workplaces adapt over time.
Augmentation generally outweighs automation, with current Gen AI technologies more likely to enhance workers' efforts in completing tasks, rather than replace them. The higher potential for automation is concentrated in routine roles. The effects of Gen AI on employment and productivity will vary based on workplace decisions, consumer demand, and policy responses, making proactive planning essential.
Task & Occupation Exposure
Building on recent Australian studies and the Felten (2021) framework, the Study has adapted a method from the International Labour Organization (ILO) (Gmyrek, Berg & Bescond, 2023). We tailored it to the Australian and New Zealand Standard Classification of Occupations (ANZSCO), for a view of Gen AI’s potential across the Australian labour market.
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Each task gets two scores: Augmentability (whether Gen AI could assist or enhance it), & Automatability (whether Gen AI undertake it). Scores range from 0 to 1.
We also look at the spread of scores within each occupation. That means we can see if some tasks are highly automatable while others aren’t, even within the same job.
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These scores show potential, assuming Gen AI is fully adopted and used. In reality, adoption varies, depending on how organisations and workers use the technology.
Exposure is powerful for understanding Gen AI’s potential - but it does not by itself predict job quality or complexity.
