Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Wind of change: how ChatGPT and big data can reshape the knowledge management paradigm?
27
Zitationen
5
Autoren
2024
Jahr
Abstract
Purpose The new disruption in the form of ChatGPT can be a valuable tool for organizations to enhance their knowledge management and decision-making capabilities. This article explores how ChatGPT can enhance organizations' KM capability for improved decision-making and identifies potential risks and opportunities. Design/methodology/approach Using existing literature and a small-scale case study, we develop a conceptual framework for implementing artificial intelligence on the internal organizational knowledge base of big data and its integration with a larger knowledge base of ChatGPT. Findings This viewpoint conceptualizes integrating knowledge management and ChatGPT for improved organizational decision-making. By facilitating efficient information retrieval, personalized learning, collaborative knowledge sharing, real-time decision support, and continuous improvement, ChatGPT can help organizations stay competitive and achieve business success. Research limitations/implications This is one of the first studies on the integration of organizational knowledge management systems with ChatGPT. This research work proposes a conceptual model on integration of knowledge management with generative AI which can be further tested in actual work settings to check it's applicability and make further modifications. Practical implications The study provided insights to managers and executives who, in collaboration with IT professionals, can devise a mechanism for integrating existing knowledge management systems in organizations with ChatGPT. Originality/value This is one of the first studies exploring the linkage between ChatGPT and knowledge management for informed decision-making.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.245 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.102 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.468 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.429 Zit.