Critical success and failure factors in the AI lifecycle: A knowledge graph-based ontological study

Hao, Xinyue, Demir, Emrah ORCID: https://orcid.org/0000-0002-4726-2556 and Eyers, Daniel ORCID: https://orcid.org/0000-0001-5499-0116 2025. Critical success and failure factors in the AI lifecycle: A knowledge graph-based ontological study. Journal of Modelling in Management 10.1108/JM2-06-2024-0204

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Abstract

Purpose The purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain management (SCM) and operations management (OM). By segmenting the AI lifecycle and examining the interactions between critical success factors and critical failure factors, this study aims to offer predictive insights that can help in proactively managing these factors, ultimately reducing the risk of failure, and facilitating a smoother transition into AI-enabled SCM and OM. Design/methodology/approach This study develops a knowledge graph model of the AI lifecycle, divided into pre-development, deployment and post-development stages. The methodology combines a comprehensive literature review for ontology extraction and expert surveys to establish relationships among ontologies. Using exploratory factor analysis, composite reliability and average variance extracted ensures the validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and significance of relationships between entities, providing metrics for labeling the edges in the resource description framework. Findings This study identifies 11 dimensions critical for AI integration in SCM and OM: (1) setting clear goals and standards; (2) ensuring accountable AI with leadership-driven strategies; (3) activating leadership to bridge expertise gaps; (4) gaining a competitive edge through expert partnerships and advanced IT infrastructure; (5) improving data quality through customer demand; (6) overcoming AI resistance via awareness of benefits; (7) linking domain knowledge to infrastructure robustness; (8) enhancing stakeholder engagement through effective communication; (9) strengthening AI robustness and change management via training and governance; (10) using key performance indicators-driven reviews for AI performance management; (11) ensuring AI accountability and copyright integrity through governance. Originality/value This study enhances decision-making by developing a knowledge graph model that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research. By incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies. These insights assist practitioners in making informed decisions about AI use, improving the overall quality of decisions in managing AI integration and ensuring a smoother transition into AI-enabled SCM and OM.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Business (Including Economics)
Publisher:	Emerald
ISSN:	1746-5664
Date of First Compliant Deposit:	4 January 2025
Date of Acceptance:	3 January 2025
Last Modified:	19 Feb 2025 14:45
URI:	https://orca.cardiff.ac.uk/id/eprint/174988

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