Can AI Redefine Best Practices in Human Resources?

We break the research into three phases. We first validated the HRBPS using an exploratory factor analysis (EFA), considering its structure and how faithfully it gauges what it is meant to be measuring. Using Cronbach’s alpha to guarantee the consistency and reliability of the data, we then examined the validity of a second scale, that which gauges the degree of artificial intelligence impacting administrative decisions. Finally, we investigate how AI adoption in HR relates to the main characteristics of HRBPS and how general AI influences managerial decision-making through network analysis. In particular in areas such as recruitment, training and corporate culture molding, the results reveal that artificial intelligence is becoming more and more significant in HR procedures. Artificial intelligence has enabled businesses to make judgments grounded on data instead of gut feeling and operate more effectively. Still, there are questions regarding algorithm bias, lack of openness and possible human oversight loss. The network analysis also highlights the increasing complexity of AI’s involvement in workforce management by showing how closely linked AI-driven decision-making is now with important HR operations. By verifying the HRBPS, evaluating how consistently we can quantify AI’s influence in managerial choices and charting the relationships between AI and optimal HR practices, this study adds to the continuing conversation about artificial intelligence in HR. Future studies should expand on these results using long-term studies and confirmatory factor analysis (CFA) to better understand how managers decide and how artificial intelligence shapes HR strategies.