Integrating artificial intelligence in human Resource management driving innovation in business operations and workforce optimization

Abstract

Purpose: This research aims to understand the significance and perceived impact of artificial intelligence in human resource management through the level of familiarity of the HRM professionals. Despite the disposition towards using AI technologies in HRM to enhance policy-making decisions and automate processes, the literature lacks documentation on the impact of the perception of the use of AI among HR professionals.

Objective: Thus, the present research seeks to respond to three major research questions to reveal how familiarity with AI is associated with perceived workforce productivity, whether such familiarity affects the ratings of the significance of AI in the HRM practices implementation, and to identify how the familiarity with AI influences the probability of its application in the HR decisions.

Methodology: A quantitative research design was adopted to meet the study objectives. Primary data were obtained through a structured questionnaire with 230 HR professionals from different sectors and geographical locations. The questionnaire aimed to assess the target participants’ familiarity with the concept of AI, perceived productivity effects of AI, and perceived relevance of AI in managing human resources. Descriptive data analyses also offered meaningful information; the Chi-Square test, Kruskal-Wallis ‘One-Way ANOVA by ranks test, Spearman rho rank-order correlation, and Kendall’s rank-tau correlation tests were used for data analysis. A descriptive summary of results was made using bar charts, box plots, scatter plots, and violin plots in order to show relations and correlation. These graphical methods maintained a definite sense in the interpretation and display of the correlation involving the variables.

Results: The Chi-Square test results revealed that there was no statistically significant relationship between their familiarity with AI and their perception regarding the importance of AI in HRM (χ² = 11.33, p = 0.254, df = 9). However, results obtained from the Kruskal-Wallis test showed that there was no significant relationship between the opinions on workforce productivity and the level of familiarity with AI Technology (Kruskal-Wallis statistic =2.22 at p ≤ 0.05 = 0.529). In addition, the Spearman Rank Order correlation coefficient was 0.03, and the p-value was 0.616; hence, there is no correlation between familiarity with AI and productivity perceptions based on the Spearman Rank correlation coefficients test. Similarly, The Kendall’s tau correlation coefficients test yielded a Kendall tau correlation of 0.03 and a p-value of 0.605, which indicates no correlation between familiarity with AI and productivity perceptions either. Using the bar chart, box plot, scatter plot, and violin plot, participants were indeed able to see that there were no major associations or correlations between these variables.

Practical Implications: Thus, the results of this study indicate that the improvement of only the level of familiarity with the use of AI may not cause an increase in workforce productivity or the perceived importance of AI in HRM. It is recommended that those organizations that are interested in incorporating AI technologies into their HR activities should incorporate antidotal practices such as training of executives and employees, the development of trust between both entities, as well as the integration of AI technologies within the HRM system alongside other organizational ideals. Knowledge of AI may not be enough to generate a positive attitude toward AI tools; rather, AI should be properly adopted and encouraged by leaders of organizations. Novelty: This paper fills a significant void in the literature by examining the extent of familiarity with AI and HR professionals’ beliefs about productivity and AI-impacting HRM. Since prior research has only investigated the technological advantages of adopting AI in the field of HRM, this research offers fresh findings on how familiarity with AI affects its perceived viability and rejects the belief that familiarity with AI invariably leads to positive perceptions of its use. Conclusion: This research establishes that there is a rather minor correlation between familiarity and the level of workforce output, as well as the importance of AI in HRM. From these results, one might propose a number of other variables, including the organizational culture, the leadership support, and the general level of trust towards AI systems as more influential in the formation of HR professionals’ perception of AI. Organizations need to go beyond mere awareness of the concept and have an intent to implement AI tools and techniques in their operations. It is crucial to link the AI integration process to strategic human resource management objectives. Subsequent studies should examine the relationship between AI familiarity, trust, and organizational variables to learn more about AI usage in the field of HRM.