Issue |
BIO Web Conf.
Volume 86, 2024
International Conference on Recent Trends in Biomedical Sciences (RTBS-2023)
|
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Article Number | 01060 | |
Number of page(s) | 11 | |
DOI | https://doi.org/10.1051/bioconf/20248601060 | |
Published online | 12 January 2024 |
Human-Centric AI Adoption and Its Influence on Worker Productivity: An Empirical Investigation
1 Department of management and innovation, National Research University Moscow State University of Civil Engineering, 129337 Yaroslavskoe shosse, 26, Moscow, Russia
2 Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007
3 Lovely Professional University, Phagwara, Punjab, India
4 Associate Professor, GRIET, Bachupally, Hyderabad, Telangana, India
5 K R Mangalam University, Gurgaon, Haryana, India 122003
* Corresponding author: natasha.chepkina@mail.ru
This empirical study looks at how the industrial sector is affected by the deployment of human-centric AI and finds some amazing changes in the workplace. Following implementation, employee productivity increased by 35.5%, demonstrating the significant advantages of AI in automating repetitive jobs and improving overall efficiency. Simultaneously, job satisfaction increased by a significant 20.6%, highlighting the alignment of AI with worker well-being. Employee skill development increased by 29.6% as a result of structured AI training, which is consistent with the larger goals of adopting AI that is human-centric. Significant cost reductions of up to 40% of budgets were also realized by departments, resulting in significant economic benefits. These revelations highlight the revolutionary potential of AI integration in Industry 5.0, promoting a harmonic convergence of intelligent technology and human skills for an industrial future that is more productive, happy, and financially stable.
Key words: Cost savings / worker productivity / employee happiness / human-centric AI adoption
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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