An Artificial Intelligence Cycle Model Against the Shortage of Skilled Professionals - An AI-based Holistic Solution Approach for Human Resources
In order to counter the impending shortage of skilled professionals in the aging societies of our time in many western countries such as Germany, solutions for business and society are urgently needed. Here, artificial intelligence (AI) can play an important role in mitigating the problem with the help of diverse applications. At the same time, it is important to consider both the needs of the respective employee and the company to ensure that the use of AI has a positive impact on the organization and finds social acceptance.
In this article, we describe the newly developed OSQE model (Optimize, Secure, Qualify, Expand) shown in Figure 1 from Annex, which for the first time outlines an AI cycle against the shortage of skilled professionals in a holistic approach that focuses equally on people and companies. This can serve organizations as a guide for strategy development, decision-making for and implementation of AI-supported measures in an entire cycle of an employee's affiliation with a company.
The model takes three driving forces into account: companies, professionals, and AI applications. In the model, the measures to be implemented are prioritized with ascending numbering based on what would be most urgent for a company to implement. All measures relate to areas of action that place people at the center and can be assigned to the classic cycle of belonging of an employee in the company. In this regard, the opportunities that AI offers to professionals and companies are highlighted.
Copyright© 2023 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Tcharnetsky, M., Vogt, F. (2023). An Artificial Intelligence Cycle Model Against the Shortage of Skilled Professionals - An AI-based Holistic Solution Approach for Human Resources. Journal of Applied Economic Sciences, Volume XVIII, Summer, 2(80), 108 – 120. https://doi.org/10.57017/jaes.v18.2(80).05
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