Exploring employees’ accountability in knowledge management systems enhanced by generative artificial intelligence
Exploring employees’ accountability in knowledge management systems enhanced by generative artificial intelligence

Summary/Abstract: Research objectives and hypothesis/research questionsThe study aims to understand managerial attitudes toward accountability when using GenAI-driven datafor decision-making and to identify procedures or regulations that could minimize erroneous data usage.Research methodsEmploying a qualitative approach, the study collected insights from senior managers through interviews.Participants shared perspectives on employee responsibility for GenAI-informed decisions and suggestedmethods to ensure data accuracy. The analysis of these insights facilitated the development of a potentialframework for GenAI adoption in KM.Main resultsFindings reveal that most managers view employees as ultimately accountable for decisions, althoughthey 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 forminimizing the risks of inaccurate or false data. Managers also highlighted the importance of well-definedroles, with explicit boundaries for GenAI usage.Implications for theory and practiceThe study contributes to theoretical discourse by pinpointing potential accountability structures inGenAI-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 theneed for precise regulations, testing protocols, and ongoing oversight. These insights encourage furtherexploration of the ethical and social dimensions of GenAI in business settings.

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