The impacts of three overlapping waves of automation to the 2030s are examined in a new report published by PwC: the algorithm wave, the augmentation wave and the autonomy wave.
The research analyzed 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.
On average across the 29 countries covered, the share of jobs at potential high risk of automation is estimated to be only around 3% by the early 2020s, 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 (see table below). This is due to the types of tasks that are more susceptible to automation and the current gender profiles of employment by sector.
The Algorithm wave is already well underway and involves automating structured data analysis and simple digital tasks, such as credit scoring. This wave of innovation could come to maturity by the early 2020s.
The Augmentation wave is also already underway but likely to come to full maturity later in the 2020s. The augmentation wave is focused on automation of repeatable tasks and exchanging information, as well as further developments of aerial drones, robots in warehouses and semi-autonomous vehicles.
In the third Autonomy wave, which could come to maturity by the mid-2030s, AI will increasingly be able to analyse data from multiple sources, make decisions and take physical actions with little or no human input. Fully autonomous driverless vehicles could roll out at scale across the economy in this phase, for example.
John Hawksworth, chief economist at PwC and co-author of the study, commented that:
“Our estimates are based primarily on the technical feasibility of automation, so in practice the actual extent of automation may be less due to a variety of economic, legal, regulatory and organisational constraints. Just because something can be automated in theory does not mean it will be economically or politically viable in practice.
“Furthermore, other analysis we have done suggests that any job losses from automation are likely to be broadly offset in the long run by new jobs created as a result of the larger and wealthier economy made possible by these new technologies. We do not believe, contrary to some predictions, that automation will lead to mass technological unemployment by the 2030s any more than it has done in the decades since the digital revolution began.”
Potential impacts by country
The estimated proportion of existing jobs with high potential automation rates by the mid-2030s varies significantly by country.
These estimates range from only around 20-25% in some East Asian and Nordic economies with relatively high average education levels, to over 40% in Eastern European economies where industrial production, which tends to be easier to automate, still accounts for a relatively high share of total employment.
Countries like the UK and the US, with services-dominated economies but also relatively long ‘tails’ of lower skilled workers, tend to have intermediate potential automation rates.
Potential impacts by industry sector
The estimated share of existing jobs with potential high rates of automation by the mid-2030s varies widely across industry sectors, from a median across countries of 52% for transportation and storage to just 8% for the education sector.
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.
Potential impacts by gender, age, education
Our analysis also highlights significant differences across types of workers and these will also vary across our three waves of automation (see chart below). 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