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Wednesday, December 06, 2017

What will your CV look like in 2030? | Onrec

"Trends research points to the top skills you’ll need to get hired in the age of automation" continues Onrec.


Nano-degrees, bio-chipsets and automation certifications – these are just three of the line items that will be commonplace on CVs in 2030, according to predictions released today by global recruitment firm PageGroup.

Working with trends forecasters Foresight Factory, PageGroup recently undertook research to examine the emerging trends and technologies that will shape tomorrow’s workplace, and predict what this means for the skills, experience and achievements required on future CVs.
Other items on the imagined 2030 CV included:
  • Anonymous ID number + personal imprint
  • Practical experience working with chatbots and human interface technology
  • Advanced Learning Ability Score (/100)
  • Social Impact Rating (/10)
The key influencing trends – and their impact on future CVs – are outlined in detail below:

Trend #1: Automation With increased automation, the new jobs landscape will emphasise how people and machines can work most effectively together to complement each other’s skills.
Employees will have to display strong human-to-machine (H2M) communication skills, including the technical ability to work with all forms of automation, AI and robotics – listing practical experience working with non-human ‘colleagues’ and automated systems. Technical qualifications and certifications in these areas will be ‘the new diploma’.

Trend #2: Liquid Skills As technology continues to change the workforces, employees will need to work flexibly and learn quickly – acquiring new skills, dropping old ones and continually updating their skillset to stay relevant.
CVs will need to show a vast array of technical and human skills which could be applied to any role, and single specialism, long-term degrees will be replaced by multiple, shorter-length ‘nano-degrees’. With learning ability a valuable attribute, a scoring system might develop to differentiate candidates. 
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Source: Onrec