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AI taking jobs

White-collar jobs will be more affected by AI than blue-collar roles

Roles involving repetitive tasks, face a substantial 30% potential automation.

Content Insights

White-collar jobs are poised to experience a greater impact.
30% of white-collar positions could potentially be executed by generative AI.
Less than 1% of blue-collar jobs could be similarly automated.

Table of Contents




A recent report by Pearson plc, a prominent provider of online training, textbooks, and skills assessments, suggests that white-collar jobs are poised to experience a greater impact from generative AI compared to their blue-collar counterparts.

The findings indicate that approximately 30% of white-collar positions could potentially be executed by generative AI, contrasting starkly with less than 1% of blue-collar jobs that could be similarly automated. Notably, white-collar roles characterized by repetitive tasks, such as scheduling appointments or managing calls, are likely to be among the most affected. Conversely, jobs in the white-collar sector involving mathematical tasks, such as those performed by engineers, are anticipated to be less impacted.

Blue-collar occupations, encompassing fields like landscaping, mechanics, and construction, involve manual labor and customer service aspects that present challenges for replication by generative AI.

Pearson’s comprehensive analysis, spanning over 5,000 jobs across five countries (Australia, Brazil, India, the US, and the UK), sheds light on the varying degrees of impact on different professions. In the United States, Pearson identified the most and least impacted white-collar and blue-collar jobs based on the percentage of hours spent on tasks susceptible to automation or augmentation by generative AI.

White-collar jobs:

Most impacted:

– Medical secretaries, 40%

– Statement clerks, 38%

– Billing, cost and rate clerks, 38%

– Loan interviewers and clerks, 38%

– Bookkeeping, accounting and auditing clerks, 38%

Least impacted:

– Chief executives, 10%

– Civil engineers, 10%

– Electrical engineers, 11%

– Sales managers, 13%

– Architectural and engineering managers, 13%

Blue-collar jobs:

Most impacted:

– Farm products buyers, 27%

– Amusement and recreation attendants, 26%

– Restaurant, lounge and coffee shop hosts, 24%

– Food service managers, 22%

– Computer-controlled machine tool operators, metal and plastic, 21%

Least impacted:

– Bus and truck mechanics and diesel engine specialists, 0%

– Dishwashers, 0%

– Highway maintenance workers, 0%

– Laundry and dry-cleaning workers, 0%

– Solderers and brazers, 0%


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