The World Economic Forum (WEF) forecast that 92 million jobs will disappear worldwide by 2030. It projected that 170 million new jobs will be created by the same changes. The number of jobs created is larger than the number lost.
While optimistic and pessimistic views differ on the overall total, a survey found that among types of employment, the share of entry-level hiring has fallen by the largest margin. This is because as companies cut staff and bring in AI, much of the repetitive work that new hires used to handle is being automated. The barrier to entry has risen.
Repetitive work was tedious, but it served a purpose.
The remedy some companies have come up with is 'mock tasks.' Under this approach, the grunt work experience lost to automation is recreated as practice assignments that resemble real work and given to new hires. A company spending money to create new practice assignments amounts to the company itself acknowledging that the grunt work handed over to machines was a pathway for developing new hires. Which companies run such programs, and for how many people, has not been disclosed.
The work affected by automation goes beyond simple repetitive tasks. Chung-Ang University Gwangmyeong Hospital is working with an outside organization to develop nurse scheduling software, and as a first step is building a 'nurse scheduling AI agent.' An AI agent is a program that, when given a goal, carries out multiple steps of work in sequence on its own.
A nurse duty roster can be drawn up only by weighing staffing by skill level, statutory working-hour and rest standards, and individual leave and shift preferences all at once. The effort aims to hand even work that requires weighing multiple conditions together over to AI.
Depending on how closely mock task assignments match real work, the time it takes for a new hire to handle work alone may grow longer or shorter.
