Transforming Knowledge Management Using Generative AI: From Theory to Practice

Dmitry Kudryavtsev, Umair Khan, Janne Kauttonen

2024

Abstract

Generative AI is revolutionizing the way people and companies create, capture and access knowledge. This study is driven by problems and new opportunities related to knowledge work. We identify, organize, and prioritize generative AI use cases for knowledge management. Our analysis of business needs and in-depth interaction with companies is the main data source used to create insights into this study. In addition to the use cases, the research highlights the challenges of using generative AI for knowledge management and existing research tasks. Creating a reusable toolkit for Generative AI-enhanced knowledge management is proposed as the next step of applied research to address the identified use cases and challenges.

Paper Citation

in Harvard Style

Kudryavtsev D., Khan U. and Kauttonen J. (2024). Transforming Knowledge Management Using Generative AI: From Theory to Practice. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS; ISBN 978-989-758-716-0, SciTePress, pages 362-370. DOI: 10.5220/0013071400003838

in Bibtex Style

@conference{kmis24,
author={Dmitry Kudryavtsev and Umair Khan and Janne Kauttonen},
title={Transforming Knowledge Management Using Generative AI: From Theory to Practice},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS},
year={2024},
pages={362-370},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013071400003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS
TI - Transforming Knowledge Management Using Generative AI: From Theory to Practice
SN - 978-989-758-716-0
AU - Kudryavtsev D.
AU - Khan U.
AU - Kauttonen J.
PY - 2024
SP - 362
EP - 370
DO - 10.5220/0013071400003838
PB - SciTePress