Volume 17 / Issue 1 / Pages 81-97 - Papers in the same Issue
Key elements in transferring knowledge of the AI implementation process for HRM in COVID-19 times: AI consultants' perspective
Tuffaha, M., Perello-Marin, M.R., & Suarez-Ruz, E.
Although artificial intelligence (AI) is transforming the workplace structure, very little is known about the strategy that facilitates AI implementation in organizations. The purpose of this paper is to explore key elements in transferring knowledge of the AI implementation process in human resource management (HRM) from the perspective of AI consultants. This study utilizes qualitative data analysis techniques. We first review the literature and then conduct in-depth semistructured interviews with eight AI consultants. We analyze transcripts using the ATLAS.ti software. First, this research reveals that AI implementation is affected by a shortage of employee data, no clear vision, a limited understanding of the AI decisions framework and managers' desire to bypass AI decisions. Second, the combination of an intensive training program and assigning AI specialists is the best way to transfer the knowledge of AI implementation processes to HR managers. Third, HR managers should create communication channels and enhance employees' awareness of the positive impact that AI solutions have on smooth collaboration with AI-employees. The paper also reveals that accelerating the process of implementing AI applications has no negative impact in COVID-19 times. However, an AI bias may be considered a potential threat for AI implementation. This paper attempts to provide a practical understanding of the elements that facilitate AI implementation in the HRM process. It provides vital insights for HR managers and AI developers to benchmark their activities when designing and adopting AI solutions. It also contributes to the literature by responding to the question of how AI implementation should be provided to HR managers and employees.
Keywords: artificial intelligence, implementation, HR manager, employee, COVID-19
Type: Research Paper // Published: 2022-06-01 // Download Citation: BibTex // PDF Downloads: 439 // PDF Filesize: 386Kb
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