In terms of hours, the 2018 figures stand at 71% being done by humans and 26% by machines, but WEF predicts this to stand at 58% to 42% in 2022 before machines take the larger share, at 52%, just seven years from now.
So it'll be farewell to jobs such as office administrator and driver, and hello to new roles such as drone piloting and remote patient health monitoring.
The report, released on September 17, forecasts that by 2025, half of all "work tasks" will be done by machines. By 2022, the report says, roughly 75 million jobs worldwide will be lost, but that could be more than offset by the creation of 133 million new jobs.
Meanwhile, employees should expect "significant shifts" in the quality, location, and format of new roles, meaning that the typical full time, permanent employee will be less dominant. Among the roles set to experience increasing demand across all industries are data analysts and scientists, software and applications developers, and e-commerce and social media specialists, all of which are significantly based on or enhanced by technology.
However, Klaus Schwab, chairman of the WEF, did warn that employment gains from technology were not a "foregone conclusion" and called for more training and education to help workers adapt.
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'They entail hard transitions for millions of workers and the need for proactive investment in developing a new surge of agile learners and skilled talent globally'.
The reports also suggested that companies, governments, and employees need to cooperate to tackle human skill shortages and separate tasks done by humans and machines. "Without proactive approaches, businesses and workers may lose out on the economic potential of the Fourth Industrial Revolution".
The WEF Report titled "The Future of Jobs 2018" was based on a survey of human resources officers, strategy executives and CEOs from over 300 global companies across industries, representing 15 million employees and 20 developed and emerging economies.
"A lot of these new jobs will be centred around understanding and managing the technology, as the limitations of machine learning means it needs human wisdom to guide it".