26 million jobs to be wiped out globally by 2027 – Clerical and administrative roles to see the biggest decline, says WEF’s Future of Work Report

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26 million jobs to be wiped out globally by 2027 – Clerical and administrative roles to see the biggest decline, says WEF’s Future of Work Report
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  • A World Economic Forum survey respondents expect to see a 25-35% decrease in demand for cashiers and ticket clerks, data entry clerks, accounting clerks, bookkeeping clerks, payroll clerks, and secretaries.
  • Organisations estimate that machines are responsible for performing 34% of all business-related tasks, whereas humans are responsible for the remaining 66%.
  • It is expected that task automation in 2027 will range from 35% for reasoning and decision-making, to 65% for information and data processing.
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In the next few years, technology and digitalisation is expected to wipe out many jobs. Low-end and repetitive jobs are likely to experience the fastest decline.

Clerical or secretarial roles will make up the majority of the fastest declining roles, with the largest job losses expected among roles like bank tellers and related clerks, postal service clerks, cashiers and ticket clerks, and data entry clerks, said the World Economic Forum’s Future of Jobs Report 2023.

The report draws its findings from a survey of the world's largest employers regarding their expectations for job trends and directions during the 2023-2027 period. The report brings together the viewpoints of 803 firms that together employ over 11.3 million employees, covering 27 industry clusters and 45 economies from all regions of the world.

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Clerical, traditional administrative roles to see significant cuts

Organisations that were surveyed predict a reduction of 26 million jobs by 2027 across roles related to record-keeping and administration. The respondents expect to see a decrease in demand for cashiers and ticket clerks, data entry clerks, accounting clerks, bookkeeping clerks, payroll clerks, and secretaries by 25-35%.

The trend of declining demand for data entry clerks is consistent worldwide, but is expected to be particularly pronounced in Brazil, with a reduction of 46%.
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On the other hand, this trend is slightly less prevalent in some high-income countries such as Germany, the United States, Singapore, and the United Kingdom, with an estimated decline of around 25%. Together, these constitute more than half of the total anticipated job destruction. Since they are currently popular jobs, it is likely to lead to the loss of 26 million jobs globally, as mentioned already.

“As businesses adopt frontier technologies, tasks such as information and data processing are increasingly automated, reconfiguring labour markets and changing the skills needed for work,” says the report.

Reasoning & decision-making jobs to be less affected

Organisations estimate that machines are responsible for performing 34% of all business-related tasks, whereas humans are responsible for the remaining 66%. The rate of automation is lower than what was estimated in the 2020 edition of the Future of Jobs Survey – respondents had then expected almost half of all business tasks to be automated in the next five years.

This could reflect the view that machines and algorithms have augmented human performance rather than replacing tasks during this period. Employers have revised down their predictions for future automation to 42% of business-related tasks by 2027, from 47% expected earlier. It is estimated that task automation in 2027 will range from 35% for reasoning and decision-making, to 65% for information and data processing.

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“The potential scope of automation and augmentation will further expand over the next few years, with artificial intelligence (AI) techniques maturing and finding mainstream application across sectors,” says the report.

It remains to be seen how rapidly evolving technologies, such as generative AI, will alter the nature of automatable tasks between 2023 and 2027. Recent studies have found that Large Language Models can already automate 15% of tasks, and when combined with applications that can correct factual inaccuracies in these models, this figure may rise to 50%.

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