The interaction of artificial intelligence and design thinking In the development of HR and decision-making trends

Abstract

This paper is a qualitative exploratory study, and a suggestive theory that aims to explore contemporary trends in HR policies in relation to technology. More precisely, the paper is a content-analysis research, aimed to explore the relationship between decision-making and data-driven business environment, and the extent to which AI and DT augment decision-making, if at all. Artificial intelligence is a physical concept which is used to describe and examine the impact that technology has on HRM practices. Design thinking is an abstract concept used to describe and examine the evolution of best leadership practices in terms of HRM processes. Before I started conducting this research, my focus was on AI, as a concept bound to change the face of traditional decision-making. Copious amount of data that is produced, extracted and stored daily, requires respective analysis. As such, I approached my respondents with the knowledge I gathered through personal research and during the creation of theoretical framework. As the research were advancing, I began to realise the extent to which these concepts provide insights into the relationship between the culture of design (thinking) and notion of artificial (intelligence) in decision-making. These two concepts were used to test the extent to which decision-making can be augmented with their use, and how they influence organisational hierarchy. From the side of the technology, AI is looking into the nature of Big Data and how it is used to exploit information for competitive HRM. DT is used to exploit the extent to which Big Data is used to broaden decision-making solutions. Together, this paper is examining the potential of these relationships, and if it in fact renders greater decision-making advantage, by accelerating the process with AI and disrupting traditional decision-making with DT. The paper used the Big Data Maturity Model (BDMM) to filter the findings and study this relationship accordingly. The model is comprised of five interconnected stages which test big data maturity of companies, as well as of their employees. Stages were divided according to goals of the paper and the two concepts. Moreover, the codes that were used to filter the findings served as additional differentiating points in the stages. The research provides insights into the synthesis of AI and DT and how they are perceived by decision-makers. The conclusions give an overview of advantages and challenges faced by HR managers when implementing AI and DT in decision-making and the subsequent room for research of this relationship.