The impact of automation to the 2030s are examined in a new report published by PwC.
The research analysed the tasks and skills involved in the jobs of over 200,000 workers across 29 countries in order to assess the potential impact of automation on workers in different industry sectors and of different genders, ages and education levels.
By the early 2020s, the share of jobs at potential high risk of automation is estimated to be around 3%, but this rises to almost 20% by the late 2020s, and around 30% by the mid-2030s.
The study suggests that more women could initially be impacted by the rise of automation, whereas men are more likely to feel the effects in the third wave by the mid-2030s. This is due to the types of tasks that are more susceptible to automation and the current gender profiles of employment by sector.
The estimated share of existing jobs with potential high rates of automation by the mid-2030s varies widely across industry sectors.
Transport stands out as a sector with particularly high longer term potential automation rates as driverless vehicles roll out at scale across economies, but this will be most evident in the third wave of autonomous automation. In the shorter term, sectors such as financial services could be more exposed as algorithms outperform humans in an ever wider range of tasks involving pure data analysis.
Our analysis also highlights significant differences across types of workers. The starkest results are those by education level, with much lower exposures on average for highly educated workers with graduate degrees or above, than for those with low to medium education levels.
In the long run, less well educated workers could be particularly exposed to automation, emphasising the importance of investment in lifelong learning and retraining
More highly educated workers will typically have greater potential for adaptability to technological changes, for example in senior managerial roles that will still be needed to apply human judgement, as well as to design and supervise AI-based systems.
Differences are less marked by age group, although some older workers could find it relatively harder to adapt and retrain than younger cohorts. This may apply particularly to less well-educated men while, female workers could be relatively harder hit in early waves of automation that apply, for example, to clerical roles.