Accenture conducted analysis to understand the potential degree of change for each occupation across the Australian economy.
The Australian economy was broken down into more than 3000 occupations and 2000 tasks to determine the potential change in occupations and impact of Generative AI on tasks performed. For example, a truck driver might ‘operate vehicles’ and ‘secure cargo’, while a sales assistant would ‘process sales’ and ‘advise customers’.
Using survey data, the degree in change of time for each task over the last five years was derived to create a metric for overall annual time change. This was used to create a linear forecast for total task change to 2035, using appropriate ceilings and floors to create this overall time change metric.
To estimate the potential impact of Generative AI on occupations, a combination of human and machine learning classification was used to assess and categorise tasks performed. The assessment was based on the current capabilities of Large Language Models (LLMs).
The analysis of job and task change was completed by breaking down the Australian economy into ~3000 occupations and ~2000 tasks.
The primary data source used in this analysis was the O*NET Database (US). The O*NET database contains survey ratings for a range of job-specific tasks for over 1000 occupations. The study used the 2018 and 2023 databases. Results were then converted to Australian occupations and timeshares. For lower-level occupations where data was not available (for example specialisations), results from higher level occupation groupings were used. There were 4 main transformations of the data.
The first step was to convert the frequency of performing a task to the amount of time spent on a task.
The second step was to map ‘Standard Occupational Classification’ (SOC) to Australian and New Zealand Standard Classifications of Occupations (ANZSCO).
The third analytical step was to calculate the change in the time spent on the same task between 2018 to 2023 for each occupation.
Finally, the projected change in timeshares from 2023 to 2035 was calculated.
To calculate wage growth, employment change and skills levels across regions, the team analysed Australian Census 2011, 2016 and 2021 data from the Australian Bureau of Statistics (ABS). The data was gathered using the ABS TableBuilder tool. Each metric was validated by comparing the distribution to similar ABS statistics.
Wage Growth
Employment
Skill Level
To understand the potential impact of Generative AI (GAI) on occupations, the primary component involved analysing O*NET task-level data to understand which tasks could be automated or augmented by the GAI. The timeshares for each task were then estimated and the results were converted to Australian occupations (similar approach to the one described in Analysis of Task Change).
The steps involved to determine whether a task could be automated or augmented by GAI were:
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