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Failure factors of AI projects: results from expert interviews

  • In the last few years, business firms have substantially invested into the artificial intelligence (AI) technology. However, according to several studies, a significant percentage of AI projects fail or do not deliver business value. Due to the specific characteristics of AI projects, the existing body of knowledge about success and failure of information systems (IS) projects in general may not be transferrable to the context of AI. Therefore, the objective of our research has been to identify factors that can lead to AI project failure. Based on interviews with AI experts, this article identifies and discusses 12 factors that can lead to project failure. The factors can be further classified into five categories: unrealistic expectations, use case related issues, organizational constraints, lack of key resources, and, technological issues. This research contributes to knowledge by providing new empirical data and synthesizing the results with related findings from prior studies. Our results have important managerial implications for firms that aim to adopt AI by helping the organizations to anticipate and actively manage risks in order to increase the chances of project success.

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Metadaten
Author of HS ReutlingenSchlegel, Dennis; Westenberger, Jens
URN:urn:nbn:de:bsz:rt2-opus4-45583
URL:https://aisel.aisnet.org/ijispm/vol11/iss3/3
DOI:https://doi.org/10.12821/ijispm110302
ISSN:2182-7788
Erschienen in:International journal of information systems and project management : IJISPM
Publisher:SciKA
Place of publication:Correlha
Document Type:Journal article
Language:English
Publication year:2023
Tag:AI; ML; artificial intelligence; failure; machine learning; success
Volume:11
Issue:3
Page Number:16
First Page:25
Last Page:40
Article Number:3
DDC classes:650 Management
Open access?:Ja
Licence (German):License Logo  Open Access