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ZENODO
Article . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2024
License: CC BY
Data sources: Datacite
ZENODO
Article . 2024
License: CC BY
Data sources: Datacite
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Artificial Intelligence and the Future of Work: Job Shifting Not Job Loss

Authors: Dr.A.Shaji George;

Artificial Intelligence and the Future of Work: Job Shifting Not Job Loss

Abstract

As artificial intelligence (AI) and automation technologies advance fast, substantial discussion remains about their influence on jobs and employment. Some expect enormous job losses and structural unemployment as computers and algorithms replace human workers in a variety of industries. However, the prevailing scholarly viewpoint is that, while AI will revolutionize work, it will not result in long-term job losses. AI is projected to have a net impact of job shifting rather than job loss by increasing productivity, accelerating economic growth, changing the structure of jobs, and allowing sectoral employment transitions. Detailed productivity evaluations show a strong correlation between productivity gains and net job creation. A 2022 meta-analysis of 127 papers indicated that productivity increases consistently improve employment and wages. Additional cross-country data from the OECD shows this association across a wide range of industrialized and emerging countries. Meanwhile, long-term data reveal that working hours have steadily decreased in recent decades without causing significant job losses, while productivity and earnings have increased in parallel. Integrating AI to automate monotonous jobs and improve human capabilities could fuel this trend. Displaced workers can transfer into new occupational jobs with adequate skilling and transition support rather than facing long periods of unemployment. Sectoral shifts have also characterized historical labor market evolutions following technology disruptions. As innovative industries outcompete legacy ones, economies undergo structural transformations. Current trends show that services are expanding while manufacturing is contracting in most sophisticated countries. AI and automation will most certainly speed the shift of occupations from manual production to skilled service roles, hence facilitating this transition. With appropriate government provisions such as retraining programs and educational expansions, the necessary employment transitions between sectors can occur smoothly rather than disruptively. In summary, while the AI revolution will fundamentally alter labor markets, effective governmental measures can ensure that job shifting outpaces loss. Workers must be supported in regularly adapting their skill sets and transitioning into new roles. Firms should invest in personnel skill development as well as smart technology integration. And governments should develop multiple ways to assist different groups in navigating the transitions, including the extension of social safety nets where appropriate. With coordinated efforts to optimize human-AI collaboration, this technology tsunami does not have to result in negative job impacts, but can instead place people in more rewarding, higher-value jobs. The view remains cautiously encouraging, providing stakeholders focus on boosting human talents to share in the AI-driven productivity windfall rather than simply displacing them.

Keywords

Automation, Artificial Intelligence, Jobs, Skills, Training, Policies, Transition, Displacement, Adaptability, Technology.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Average
Top 10%
Green