How might generative AI impact different occupations?

The figure below shows the results of the analysis, which was based on a global assessment of the 436 detailed occupations that comprise the International Standard Classification of Occupations. 

Select one of the occupations major groups to filter the occupations.

Each occupation is made up of tasks and each of the tasks with an occupation received a 0-1 score on the ability to automate that particular task, with 0 indicating it is not possible and 1 indicating it was entirely possible.

The figure plots these occupations according to its average score (mean of all tasks within an occupation) and its standard deviation (the dispersion of task-level automation scores within an occupation). 

The intersection of these scores was used to elaborate a framework for classification based on four categories:

• Automation potential: Jobs where the majority of tasks today could theoretically be performed with GenAI – such jobs could potentially be automated.
• Augmentation potential: Jobs where some tasks can be performed using GenAI, but the majority need to be done by humans. Such jobs can be augmented by GenAI, speeding up some tasks and allowing more space for creative human work and new tasks.
• Big unknown: A category between automation and augmentation potential, representing jobs where the balance of tasks today is between those that can be done with GenAI and those that cannot. This balance might shift over time as technology improves and occupations evolve, moving some jobs closer to automation and others to augmentation potential.
• Not affected: Occupations where the majority of tasks cannot be performed with GenAI (e.g. physical tasks).