Embracing Artificial Intelligence and Digital Personnel to Create High-Performance Jobs in the Cyber Economy
Purpose: The purpose of the chapter is to study the process of creating highly efficient jobs in the cyber economy through the integration of AI and employees’ mastering new digital competencies.
Methodology: Evolutional (historical) methods, analysis, synthesis, and algorithmization are used.
Conclusions: It is determined that the modern labor market is peculiar for the emergence of a new type of employee—AI. The management of labor efficiency in the cyber economy is oriented not at humans but at robots, which reduces production costs. Depending on the level of coding of operations, highly efficient jobs in the cyber economy are either fully replaced by AI or envisage effective interactions between humans and AI. In the latter case, human employees will need to continually improve and develop their cyber competencies. In order to measure the efficiency of a job working with AI, there has to be an integral indicator taking account of the usage of resources, involvement of employees, and work satisfaction.
Originality/value: The authors propose competencies that employees have to possess with the wide implementation of AI technologies. They reflect on the conditions in which highly efficient jobs could be created, and offer a vision for the transformation of jobs into highly efficient jobs within the cyber economy.
KeywordsLabor efficiency Cyber competencies Highly efficient jobs Labor intellectualization
JEL CodeB51 D24 E24 O32 O33 O47
- Aghion P, Jones BF, Jones CI (2018) Artificial intelligence and economic growth. The economics of artificial intelligence: an agenda from NBER. https://econpapers.repec.org/bookchap/nbrnberch/14015.htm. Accessed 04 March 2019
- Ford M (2015) The rise of the robots: technology and the threat of mass unemployment. Oneworld, OxfordGoogle Scholar
- Graetz G, Michaels G (2015) Robots at work. The London school of economic and political sciences: centre for economic performance (CEP). Discussion paper no 1335, March, p 53Google Scholar
- Horx M (2005) Wie wir leben werden. Unsere Zukunft beginnt jetzt. Campus Verlag (Frankfurt). Auflage. 397 SeitenGoogle Scholar
- IMF (2017) World economic outlook. April 2017: gaining momentum? https://www.imf.org/en/Publications/WEO/Issues/2017/04/04/world-economic-outlook-april-2017. Accessed 04 March 2019
- Manyika J, Chui M, Bughin J, Dobbs R, Bisson P, Marrs A (2013) Disruptive technologies: advances that will transform life, business, and the global economy. McKinsey Global Institute, San Francisco, CAGoogle Scholar
- Miller B, Atkinson RD (2013) “Are robots taking our jobs, or making them?” (information technology and innovation foundation, September 2013). https://itif.org/publications/2013/09/09/are-robots-taking-our-jobs-or-making-them. Accessed 04 March 2019
- Muller VC, Bostrom N (2013) Future progress in artificial intelligence: a survey of expert opinion. Springer, Fundamental Issues of Artificial Intelligence, Synthese Library, BerlinGoogle Scholar
- Odegov YG, Pavlova VV (2018) New technologies and their impact on the labour market. Living standards of Russian regions 2(208):60–70Google Scholar
- PwC (2017) Sizing the prize. What’s the real value of AI for your business and how can you capitalise? https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf. Accessed 06 March 2019
- WEF (2016) The future of jobs. Employment, skills and workforce strategy for the fourth industrial revolution. http://www3.weforum.org/docs/WEF_Future_of_Jobs.pdf. Accessed 06 March 2019
- White House (2016) Artificial intelligence, automation, and the economy. https://obamawhitehouse.archives.gov/blog/2016/12/20/artificial-intelligence-automation-and-economy. Accessed 04 March 2019
- Wolfgang M (2016) The robotics market – figures and forecasts. RoboBusiness, Boston Consulting Group, Boston, MAGoogle Scholar