Exploring Implementation Parameters of Gen AI in Companies

Maarten Voorneveld

2024

Abstract

Our work focusses on investigating Gen AI implementation, as the field is developing at such a rapid pace, up to date research on business implementations and outcomes is limited. We systematically evaluate AI applications, analysing challenges/opportunities. We consider adoption beyond pilot projects via a structured approach covering factors such as technological, organizational, and environmental. Our case studies show relevance of data quality, infrastructure, and organizational culture. The paper explores how company leaders can support to create employee trust and deliver on an AI strategy. Companies face competition, customer needs and regulation that shape their technology roadmaps. These complexities are exacerbated by training data problems, internal communications, context challenges and ethics. This research finds that challenges & strategies for responsible Generative AI deployment advocate a holistic and adaptive approach. Which companies need to tailor each application, to achieve desired outcome.

Paper Citation

in Harvard Style

Voorneveld M. (2024). Exploring Implementation Parameters of Gen AI in Companies. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 665-673. DOI: 10.5220/0012618300003690

in Bibtex Style

@conference{iceis24,
author={Maarten Voorneveld},
title={Exploring Implementation Parameters of Gen AI in Companies},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={665-673},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012618300003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Exploring Implementation Parameters of Gen AI in Companies
SN - 978-989-758-692-7
AU - Voorneveld M.
PY - 2024
SP - 665
EP - 673
DO - 10.5220/0012618300003690
PB - SciTePress