The Impact of AI Implementation on Job Transformation and Competency Requirements: Prioritising Reskilling and Soft Skills Development

Lucie Vnoučková et al.

Quality Innovation Prosperity2025https://doi.org/10.12776/qip.v29i2.2165article
AJG 1
Weight
0.37

What the paper says

This research contributes to understanding how organisations are navigating AI-driven workforce transformation by prioritising human-centric competencies and strategic reskilling to improve the quality of human resources rather than widespread job elimination. Methodology/Approach: This case study investigates the real impact of AI implementation on job transformation within the top 100 Czech companies. Findings: The study found a strong negative correlation between employee training and new recruitment, suggesting organisations prefer upskilling to replacement. Industry-specific approaches vary significantly. Research Limitation/implication: As AI continues to reshape work, these insights can guide organisations in developing effective strategies that leverage both technological capabilities and uniquely human skills. Originality/Value of paper: These findings provide evidence that while AI is transforming jobs in Czech companies, organisations are strategically adapting through reskilling rather than widespread job cancellations, with soft skills becoming increasingly valued as technical tasks are automated.

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https://doi.org/https://doi.org/10.12776/qip.v29i2.2165

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@article{lucie2025,
  title        = {{The Impact of AI Implementation on Job Transformation and Competency Requirements: Prioritising Reskilling and Soft Skills Development}},
  author       = {Lucie Vnoučková et al.},
  journal      = {Quality Innovation Prosperity},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.12776/qip.v29i2.2165},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.