Artificial Intelligence Techniques in Human Resource Management—A Conceptual Exploration

Access to and generation of structured and unstructured HR-specific databases and an increasing reliance on the use of advanced digitalized HRM and AI applications for generating insights, solving problems and engaging in predictive decision-making for HR functions is on the rise.

“Access to and generation of structured and unstructured HRspecific databases and an increasing reliance on the use of advanced digitalized HRM and AI applications for generating insights, solving problems and engaging in predictive decisionmaking for HR functions is on the rise”

Strohmeier and Piazza (2015) were the first to develop a conceptual base for identifying a range of areas for using AI-augmented applications for HRM, followed by Jia et al. (2018), who highlighted several strategic and operational aspects of HRM AI applications that could assist in these activities.

“Perhaps, Strohmeier and Piazza (2015) were the first to develop a conceptual base for identifying a range of areas for using AI-augmented applications for HRM, followed by Jia et al. (2018), who highlighted several strategic and operational aspects of HRM AI applications that could assist in these activities.”

This stage implies evaluating the success potential of an AI technique for a particular HR function and comparing the intended application for checking the effectiveness (improved results) than the already established HR technique.

“This stage implies evaluating the success potential of an AI technique for a particular HR function and comparing the intended application for checking the effectiveness (improved results) than the already established HR technique”

The intended exploration may encompass the investigation of technological functionalities related to a particular activity of HR, task characteristics, and the potential fit of both - task-technique combinations - and the resulting consequences.

“In addition, the intended exploration may encompass the investigation of technological functionalities related to a particular activity of HR, task characteristics, and the potential fit of both - task-technique combinations - and the resulting consequences”

AI algorithms evolve with the data they process based on, for example, machine learning.

“While most software algorithms in HRM are likely to be static, meaning that computer-programmed steps remain the same irrespective of the data inputted (e.g.use of an Excel spreadsheet to rank job applicants or use of SPSS to predict turnover intentions), in the case of AI these evolve with the data they process based on, for example, machine learning (Strohmeier & Piazza, 2015;Vrontis et al., 202”

Most software algorithms in HRM are likely to be static.

“While most software algorithms in HRM are likely to be static, meaning that computer-programmed steps remain the same irrespective of the data inputted (e.g.use of an Excel spreadsheet to rank job applicants or use of SPSS to predict turnover intentions), in the case of AI these evolve with the data they process based on, for example, machine learning (Strohmeier & Piazza, 2015;Vrontis et al., 202”