Exploring Employees’ Accountability in Knowledge Management Systems Enhanced by Generative Artificial Intelligence

Research objectives and hypothesis/research questions The study aims to understand managerial attitudes toward accountability when using GenAI-driven data for decision-making and to identify procedures or regulations that could minimize erroneous data usage. Research methods Employing a qualitative approach, the study collected insights from senior managers through interviews. Participants shared perspectives on employee responsibility for GenAI-informed decisions and suggested methods to ensure data accuracy. The analysis of these insights facilitated the development of a potential framework for GenAI adoption in KM. Main results Findings reveal that most managers view employees as ultimately accountable for decisions, although they acknowledge GenAI as a supportive rather than a substitutive tool. The need for clear guidelines, thorough testing phases, and the implementation of verification procedures emerged as key strategies for minimizing the risks of inaccurate or false data. Managers also highlighted the importance of well-defined roles, with explicit boundaries for GenAI usage. Implications for theory and practice The study contributes to theoretical discourse by pinpointing potential accountability structures in GenAI-driven decision-making and by proposing a framework that addresses data verification challenges. Practically, it offers organizations a structured approach to integrating GenAI into KM, emphasizing the need for precise regulations, testing protocols, and ongoing oversight. These insights encourage further exploration of the ethical and social dimensions of GenAI in business settings.