At Pearson, we believe that generative AI can have a positive impact on how people understand and prepare for the changing world of work. One of the best ways for employers and employees to adapt and stay relevant is to help predict the skills that people will need for the future, so that they can prepare for the changes to come.
So for this Skills Outlook, we looked at the impact of generative AI on jobs, specifically on time spent on individual tasks in a working week, in five countries – Australia, Brazil, India, the US and UK. In each one, the research shows that generative AI will have a greater impact on white collar roles over the next 10 years. Blue collar roles - especially ones with more creative, manual, and collaborative tasks - are at less risk from the changes the rise of Gen AI will bring.
Why the difference?
Many white-collar roles contain repetitive and technical tasks – such as scheduling appointments or answering and directing calls – that can easily be replicated by generative AI. We’ve found that around 30%, and up to 46%, of hours spent currently on some white-collar job tasks could be done by generative AI.
On the flip side, many blue-collar roles, such as landscapers, mechanics, or construction workers, include manual labour or customer service elements that can’t easily be replicated by generative AI. In many cases, we found that less than 1% of a blue-collar worker’s job could be done by generative AI.
Most impacted white and blue collar jobs by country
Percentage of time spent on current tasks that will be impacted by Gen AI by 2032, if the role stays the same
White collar: Medical Secretary
40%
Blue collar: Farm Products Buyer
27%
White collar: Medical Secretary
41%
Blue collar: Buyers and Procurement Officer
27%
White collar: Accounting or Book-keeping Clerk
46%
Blue collar: Weaver/Knitter
17%
White collar: Medical Receptionist
42%
Blue collar: Cafe or Restaurant Manager
18%
White collar: Teller
33%
Blue collar: Concierge
19%
What can employers do next?
Generative AI is a quickly evolving area of technology. Employees and employers in white collar sectors need to act faster to adapt to generative AI than those in blue collar roles – looking at how to upskill and reskill, as well as how jobs can evolve, to ride the wave of change.
Targeted learning and development can equip your workforce with the capabilities to thrive, whatever tomorrow brings. Talk to us to discover how your organisation can cultivate a workforce that is fit for the future.
Census and other workforce datasets were consolidated to create a single view of the current workforce in the US, UK, Australia, India and Brazil. Using Pearson’s proprietary occupations ontology of 5,600 jobs and 26,000 tasks, each job can be viewed as a collection of tasks. This allows our machine learning algorithms to calculate future technology impact on each job at a task level. Our models consider the impact of 16 groups of emerging technologies, with the rate of adoption for each technology tailored by country and industry. Alongside technology impact, economic modelling is used to account for country and industry-specific growth trends. In this analysis, the technology impact due to Generative AI technologies was calculated for every job in the five countries, looking 10 years into the future.