Knowledge Management in the Age of AI: Tacit Knowledge Loss and Process Dependency in Automated HR and Payroll Teams

As organizations embrace AI and process automation, HR and payroll departments experience profound changes in how knowledge is created, transferred, and retained. While automation improves speed and accuracy, it can also lead to an unintended consequence: the erosion of manual expertise. This paper examines this phenomenon in the context of a multinational HR and payroll department managing operations for 25 countries with a team of 26 members. Drawing on professional experience and qualitative interviews with team members, the study explores how reliance on automated tools affects the transfer of tacit knowledge within teams. Special attention is given to onboarding practices, where new team members increasingly learn “how to use the tool” rather than “why the process works,” resulting in a shift from process understanding to tool dependency. Within this study qualitative data collected during interviews with experts is quantified by extracting topic segments as alternatives that consist of specific count of associated keywords to define alternatives for further analysis. Extracted and quantified alternatives are ranked using multi-criteria decision-making methodology Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The result of this ranking reflects the predominant opinion among the HR administration, payroll and business process automation experts in a multinational company on factors that affect preservation and transfer of critical process knowledge.