Automation could potentially impact up to 30% of UK jobs by the early 2030s, affecting different types of workers and industries at different times, according to a new report and study from PwC.
PwC’s report, Will robots really take our jobs? analysed the tasks and skills involved in the jobs of over 200,000 workers across 29 countries, including over 5,500 workers in the UK. Based the findings, PwC’s economists identified three potentially overlapping waves of automation that will progressively impact the working population between now and the mid-2030s.
These waves - the algorithm wave, augmentation wave and autonomy wave - suggest that more women will initially be impacted by the rise of automation, whereas men are much 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 in particular sectors.
Commenting on the findings, John Hawksworth, chief economist at PwC, said:
The timing and progressive impact of the three waves of automation is expected to be:
Algorithm wave - to early 2020s
The algorithm wave is already well underway and involves automating structured data analysis and simple digital tasks, such as credit scoring.
Only a relatively small proportion of jobs in the UK, perhaps between 2-3%, are likely to be impacted during this time, as many of the technologies are still at an early stage of development. PwC finds financial, professional and technical services, and information and communications sectors are likely to be the most affected at around 6-8%.
Women could be more exposed than men at this stage due to their higher representation in clerical tasks in the more affected sectors.
Augmentation wave - to late 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.
PwC estimates the share of potential jobs affected could rise to up to 20% by the end of the 2020s, as the use of AI systems becomes much more widespread and robotics technologies advance and mature. Over this period, the effects will be felt across all industry sectors, although financial services is still expected to be the most impacted sector.
Women are still marginally more exposed than men in this second wave, although the gap is narrowing.
In this wave, the level of education also starts to become a determining factor, with low and medium education levels (i.e. below graduate level) starting to experience higher automation risk levels than those with higher education levels (i.e. graduates and above).
Autonomy wave - by mid-2030s
In the final wave, PwC predicts that AI will be able to analyse data from multiple sources, make decisions and take physical actions with little or no human input.
The share of jobs that could be impacted by automation is estimated to rise to 30% by the mid-2030s, as autonomous robots and driverless vehicles roll out more widely across the economy.
PwC analysis suggests that this is the phase when many more manual tasks could become capable of being automated, pushing sectors like transport, manufacturing and retail to the top of the likely automation list. This is also the phase when men are likely to be more impacted than women, due to their greater share of employment in manual jobs in areas such as driving, factories and warehouses. This is also true for less-educated workers, as the variation between education levels widens.
However, PwC predicts that AI and other related new technologies will also boost productivity, income and wealth. As this additional income and wealth is spent or invested, it will generate increased demand for human labour in less automatable sectors. PwC’s economic modelling suggests that this job creation effect will broadly offset the potential job losses associated with automation in the long run.
Euan Cameron, UK Artificial Intelligence leader at PwC, said:
Notes to editors:
The full PwC report, Will robots really take our jobs? Can be downloaded from the link below.