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Conference ProceedingsArtificial Intelligence & Machine Learning

Failure of AI projects: understanding the critical factors

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Abstract & Overview

Adoption of artificial intelligence (AI) has risen sharply in recent years but many firms are not successful in realising the expected benefits or even terminate projects before completion. While there are a number of previous studies that highlight challenges in AI projects, critical factors that lead to project failure are mostly unknown. The aim of this study is therefore to identify distinct factors that are critical for failure of AI projects. To address this, interviews with experts in the field of AI from different industries are conducted and the results are analyzed using qualitative analysis methods. The results show that both, organizational and technological issues can cause project failure. Our study contributes to knowledge by reviewing previously identified challenges in terms of their criticality for project failure based on new empirical data, as well as, by identifying previously unknown factors.

Cite this record

Jens Westenberger, Kajetan Schuler, Dennis Schlegel (2022). Failure of AI projects: understanding the critical factors. Procedia Computer Science. https://doi.org/10.1016/j.procs.2021.11.074

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Westenberger, J., Schuler, K., & Schlegel, D. (2022). Failure of AI projects: understanding the critical factors. Procedia Computer Science. https://doi.org/10.1016/j.procs.2021.11.074
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