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Hao, Xinyue, Demir, Emrah and Eyers, Daniel 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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Journal of Modelling in Management

Critical Success and Failure Factors in the AI Lifecycle: A

Knowledge Graph-Based Ontological Study

Journal:

Journal of Modelling in Management

Manuscript ID

JM2-06-2024-0204.R1

Manuscript Type:

Original Article

Keywords:

Artificial intelligence, Operations management, Innovation, Supply chain

management

Journal of Modelling in Management

Critical Success and Failure Factors in the AI Lifecycle: A Knowledge Graph-Based Ontological

Study

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 (CSFs) and Critical Failure Factors (CFFs), the 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 (EFA), Composite Reliability (CR), and Average Variance Extracted (AVE) ensures the

validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and

significance of relationships between entities, providing metrics for labelling the edges in the Resource

Description Framework (RDF).

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 KPI-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.

Keywords

Artificial intelligence; Critical success and failure factors; Supply chain and operations management;

Knowledge graph; Domain ontology

1. Introduction

Recent advancements in Artificial Intelligence (AI) have significantly transformed Supply Chain

Management (SCM) and Operations Management (OM) (Dhankar et al., 2024, Younis et al., 2022). AI has

emerged as a pivotal technology for Industry 4.0, offering transformative applications that span from text-

to-image generation to enhancing operational efficiency and strategic planning. Specifically, GPT-4, one

of the latest popular large language models, is revolutionizing communication and automating customer

services (Savastano et al., 2024). Technologies like Vision Transformers, BigGAN, and VQ-VAE-2 are

reshaping inventory management by enabling precise product inspections, reducing errors, and expediting

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processes (Yin et al., 2022). On another front, deep learning models such as Deep Q-Networks are utilized

for vehicle routing (Chen et al., 2022), while Proximal Policy Optimization is leveraged for resource

scheduling in dynamic environments (Luo et al., 2022). Furthermore, Recurrent Neural Networks and

operational research algorithms enhance strategic supply chain planning by transforming large-scale data

into actionable insights (Bassiouni et al., 2023, Sadeghi et al., 2023). Collectively, these AI-powered

innovations are redefining SCM and OM by fostering efficient, cost-effective strategies (Giachino et al.,

2024). Despite the immense potential, AI integration in supply chain and operations is often fraught with

challenges such as data silos, implementation bottlenecks, and resistance to change. As AI scholars, we

must address a critical question: How can we develop effective strategies to overcome these specific

challenges in SCM and OM to ensure the seamless adoption of AI, thereby driving resilience, flexibility,

and sustained operational excellence?

Readiness, traditionally defined as “the state of being ready or prepared for something”, must be

reconceptualized when applied to SCM and OM. In this context, readiness goes beyond the internal capacity

of an individual organization and extends across the entire supply chain network. This broader perspective

requires that the organization and also all participants, including suppliers, logistics providers, distributors,

and customers, are fully prepared to adopt and effectively leverage AI solutions. Readiness should cover

both technological factors like data quality (Merhi and Harfouche, 2023), infrastructure (Cannas et al.,

2023), non-technological factors, such as top management (Rahman et al., 2023) and employee resistance

(Hangl et al., 2023). The diffusion of innovation theory suggests that even if a business is prepared

internally, a lack of alignment among network partners could result in delays or even failure in the adoption

process (Rogers et al., 2014). Thus, readiness should be redefined as the “state in which an organization is

ready to accomplish a task effectively” (Pacchini et al., 2019). and it must include the collaboration and

alignment of all partners involved in the supply chain. To deepen the understanding of how to achieve such

readiness, Wang et al. (2022) proposed a three-stage AI lifecycle model that consists of pre-development,

deployment, and post-development. Each stage in this lifecycle is linked to critical success factors (CSFs)

and critical failure factors (CFFs) specific to the challenges and opportunities of integrating AI into SCM

and OM (Hao and Demir, 2024).

In the pre-development stage of AI integration, the primary objective is to identify the specific

operational challenges that the system should address, thereby ensuring that the AI solution is aligned with

the overarching goals of the organization. This involves a thorough analysis of existing processes to uncover

inefficiencies, bottlenecks, and areas that would benefit most from automation or enhancement. A CSF in

this stage is designing an AI solution that is tailored rather than generic, effectively addressing the identified

issues in a way that integrates seamlessly with the existing operational vision and strategies (Zhang et al.,

2023). In the deployment stage, the emphasis shifts from planning to actual implementation, focusing on

refining the AI technology and ensuring it integrates smoothly into established workflows. During this

phase, ensuring effective data integration and managing human-system collaboration are crucial CSFs. At

the same time, common CFFs include complexities associated with system integration and resistance to

change from employees who may be hesitant about adopting new technologies. Addressing these challenges

requires comprehensive training, transparent communication, and involving personnel from different

operational areas to build trust and acceptance for the AI solution (Belhadi et al., 2024). Finally, in the post-

development phase, continuous monitoring and adaptation are key to maintaining the AI system’s

effectiveness over time. As operational conditions evolve, including changes in organizational strategies or

market dynamics, the AI must be updated and refined to stay relevant and productive. This ongoing

refinement is a CSF that ensures the AI continues to contribute positively, enhancing resilience and agility

in operational processes. Regular updates, real-time feedback mechanisms, and an adaptive approach are

essential to enable the AI to support continuous improvements and adapt to unforeseen changes, ultimately

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contributing to the long-term value (El Jaouhari and Hamidi, 2024). Despite the extensive literature on AI

in business, there remains a significant gap in comprehensive discussions on CSFs and CFFs that are

specific to and span across the different stages of AI integration.

The aim of this research is to dissect the dynamics between CSFs and CFFs during the pre-

development, deployment, and post-development stages by constructing an AI journey model for SCM and

OM. The research questions posed are: “How do success and failure factors dynamically interact in the

lifecycle of AI within SCM and OM, and what strategies can enhance the success factors while reducing

the failure ones?” To do so, this research employs a knowledge graph method to analyze the

interconnectedness of both technological and non-technological factors that are essential for AI

implementation. To better understand the behavior complex systems, knowledge graphs have emerged as

a promising tool for effectively modelling real-world supply chain and operation challenges, i.e., product

quality diagnosis and prediction (Xu and Dang, 2023) and risk management (Kosasih et al., 2022). In a

knowledge graph, each entity, interconnected via edges and described by labels, allows for a representation

and analysis of the relational dynamics among critical factors within a system (Chen et al., 2020).

Employing a knowledge graph can also be a useful tool for assessing the three key elements of entities,

edges, and labels of AI within SCM and OM.

The proposed methodology can focus on pivotal areas to augment the level of critical factors for

effectively embracing AI by enabling a strategic foresight into the interconnected CSFs and CFFs on the

comprehensive readiness of the organization. Our research is one of the first studies to employ knowledge

graphs for examining factor interrelations, thereby advancing the AI research for the SCM and OM. The

differences between this research and prior contributions are significant in two ways. First, in the AI

application domains within the SCM and OM, previous studies have tended to concentrate only on CSFs

(Kumar et al., 2023b, Agrawal et al., 2023). This research moves beyond this standard approach by

proposing an integrated framework that not only combines CSFs with CFFs but also examines their

complex and dynamic interrelationship. The aim is to leverage CSFs to mitigate the impact of CFFs, thereby

optimizing the conditions for an effective and ongoing AI journey. Second, this research contributes a

predictive component that anticipates the potential outcomes of different factor interactions. This is a step

beyond merely describing or cataloging CSFs and CFFs, by actually using the knowledge graph to simulate

and predict how changes in one entity might impact the whole system. Such predictive modelling would be

invaluable for decision-makers seeking to prioritize interventions or investments that would have the most

significant positive impact on AI integration within SCM and OM.

The remainder of this paper is organized as follows. Section 2 presents the theoretical underpinnings

of embracing AI in operations and supply chain. The research methodology is explained in Section 3;

findings follow in Section 4. Section 5 discusses the results and provides theoretical and managerial

implications. Finally, Section 6 summarizes the study findings and outlines future research opportunities.

2. Literature review

2.1 AI research in supply chain and operations management

AI has undergone an enormous transformation since its humble beginnings in 1965. It began as a

niche technology and has evolved into a monumental leap that is increasingly revolutionizing the way

humans perceive and interact with the world. In its nascent stages, the immense ambition of AI was

constrained by the computational power available at the time (Smolensky, 1987), as well as the scarcity of

data and the rudimentary algorithms at its disposal (Hepner et al., 1990). Thanks to accelerated computing,

processing power (Belhadi et al., 2024) and storing power (Ajani et al., 2024), the latest AI breakthrough

of deep neural networks, long-short memory networks, and convolutional neural networks has led to an AI-

enabled boom in automating tasks that have previously been possible for humans only (Islam et al., 2023).

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AI, in this paper, is defined as the capacity of a machine to execute cognitive functions that are typically

associated with human intelligence, underlying perceiving, reasoning, learning, and problem-solving

capabilities (Duan et al., 2019).

Contingent upon its functional paradigms and objectives, AI can be further classified into a number

of sub-fields: artificial neural networks and rough set theory (‘thinking humanly’); machine learning, expert

system, metaheuristics and natural language processing (‘acting humanly’); fuzzy logic and bayesian

networks (‘thinking rationally’); agent-based systems and reinforcement learning (‘acting rationally’) (Min,

2010). The multifaceted nature of SCM and OM has been adeptly navigated by deploying the diverse

functionalities and applications of AI. Researchers has done notable progress in both automation and

augmentation techniques ranging from discrete supplier selection models to a comprehensive risk

management framework as listed in Table 1.

Table 1. Integration of AI in operational and supply chain tasks.

Model

Technology/

Packages

Application domain

Reference

Demand forecasting

Sarveswararao et al. (2023)

Predictive maintenance

Kosasih and Brintrup (2021)

Price optimization

Zhang and Razmjooy (2024)

Load forecasting

Lotfipoor et al. (2024)

Customer segmentation

Joung and Kim (2023)

Artificial neural

networks

TensorFlow

Keras

PyTorch

Inventory management

Moon et al. (2023)

Supplier evaluation

Pamucar et al. (2023)

Risk management

Sarwar et al. (2023)

Product classification

Jin et al. (2024)

Rough set theory

Rosetta

Java rough set

Pattern recognition

Sun et al. (2020)

Demand forecasting

Camur et al. (2024)

Supply chain optimization

Hosseinnia Shavaki and

Ebrahimi Ghahnavieh (2023)

Routing and scheduling

Kayhan and Yildiz (2023)

Inventory management

Demizu et al. (2023)

Machine learning

scikit-learn

Apache Spark MLlib

Weka

Predictive analytics

Paneque et al. (2023)

Diagnostic analysis

Anwar (2023)

Decision support

Tanhaeean et al. (2023)

Rule-based system for inventory

control

Raziee (2023)

Expert system

CLIPS

Drools

Compliance monitoring

Li et al. (2024)

Facility layout planning

Lamba et al. (2020)

Vehicle routing problem

Lai et al. (2024)

Scheduling optimization

Goli et al. (2023a)

Metaheuristic

OptaPlanner

JMetal

Supply network design

Rajabi-Kafshgar et al. (2023)

Sentiment analysis

Nguyen et al. (2022)

Chatbots for customer service

Panigrahi et al. (2023)

Automated report generation

Khatri (2023)

Natural language

processing

NLTK

SpaCy

GPT

Text analysis for market

intelligence

Thayyib et al. (2023)

Uncertainty modeling

Goli et al. (2023b)

Quality control

Cannas et al. (2023)

Human resource allocation

Lo et al. (2024)

Fuzzy logic

SciKit-Fuzzy

MATLAB Fuzzy Logic

Toolbox

Consumer behavior modeling

Martínez et al. (2020)

Demand forecasting

Chien et al. (2024)

Bayesian networks

Netica

pgmpy

Risk assessment

Qazi et al. (2023)

Agent-based systems

AnyLogic

Supply chain simulation

Zhu et al. (2024)

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Dynamic pricing strategies

Wang et al. (2023a)

NetLogo

Market behavior analysis

Ding et al. (2023)

Autonomous vehicles routing

Rolf et al. (2023)

Adaptive inventory control

Esteso et al. (2023)

Reinforcement

learning

OpenAI Gym

Stable Baselines

Learning-based optimization for

production planning

Zhao et al. (2023)

2.2 Innovation diffusion theory

Innovation diffusion theory seeks to explain how, why, and at what rate new ideas and technology

spread within specific social contexts (Rogers et al., 2014). Initially conceptualized by Rogers in 1979, it

describes the journey of an innovation from its inception through to widespread adoption. It focuses on the

roles of communication channels, time, and the social context during this process (Aslani and Naaranoja,

2015). At its core, the theory identifies four critical components: the innovation itself, the communication

mechanisms through which information about the innovation is conveyed, the temporal dimension over

which diffusion occurs, and the social system within which this diffusion unfolds (Rogers and Adhikarya,

1979). The theory further articulates this process through five sequential stages: acquiring knowledge about

the innovation, being persuaded of its value, deciding to adopt or reject it, implementing the innovation,

and finally confirming the decision (Rogers, 2003).

Concerning the adoption of technological innovations, the stages of acquiring knowledge, becoming

persuaded of the benefits, and deciding form the pre-development stage (Ahmad et al., 2022). This stage

sets the foundation for the lifecycle. The next stage, deployment, is marked by the practical application or

implementation of the technology (Liu et al., 2023). Following this, the confirmation stage corresponds

with post-development activities, where the adoption decision is evaluated and reinforced, ensuring that the

integration of technological innovation is successful, and its value is realized (Hao and Demir, 2024). Each

consecutive stage in Figure 1 is a prerequisite for the next stage. For instance, “persuasion” follows the

“knowledge”, where adopters form their viewpoints on the innovation after gaining an understanding of it

(Antsipava et al., 2024). The knowledge stage is rooted in comprehension, while the persuasion stage is

shaped by emotional engagement (Borghi and Mariani, 2022). In the subsequent decision stage adopters

contemplate whether to accept or reject the innovation. This is followed by the implementation or

deployment stage, where the innovation is put into practice. Even though the decision to adopt or reject the

innovation has been made, adopters seek validation for their decision during the confirmation phase

(Ramachandran et al., 2023). Nevertheless, it remains feasible to revisit and potentially alter decisions

regarding the acceptance or rejection of the innovation during the implementation dimension.

Figure 1. Decision-making process of innovation (Adapted from Rogers, 2003).

The journey of decision-making in the context of AI adoption starts with the acquisition of

knowledge about AI and moves forward to the stage where an attitude towards the innovation is either

shaped or confirmed (Khan et al., 2023). Transitioning to the persuasion stage, adopters typically seek

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answers to queries such as “Where can I implement this AI technology within my SCM and OM?” (Hao

and Demir, 2023), “What are the anticipated outcomes of integrating AI into tasks?” (Dwivedi et al.,

2023), and “What are the advantages and disadvantages of adopting AI in my specific operational

context?” (Venkatesh et al., 2023). This phase marks a heightened psychological engagement of the

adopter with the AI innovation. Following the knowledge and persuasion stages, adopters are confronted

with the decision to either adopt or reject AI in their organization (Almeida et al., 2023). Adoption

signifies the resolution to fully integrate the innovation while rejection represents the choice to forego

adaptation to the innovation in the context of SCM and OM.

Following the decision, the implementation stage is realized when the innovation is actively utilized.

This stage is marked by the active deployment of AI systems in real-world settings (Richey Jr et al., 2023).

Prior to this juncture, the decision-making process surrounding the adoption of AI has been predominantly

theoretical (Rogers et al., 2014). Moreover, even beyond the point of deployment, there exists a certain

level of uncertainty regarding the predicted impact and efficiency gains that AI is anticipated to bring to

supply chain dynamics and operational workflows (Helo and Hao, 2022). Questions such as “How am I/we

supposed to use it effectively?” (Charles et al., 2023), “How does it function?” (Brau et al., 2024), and

“What potential challenges might arise, and how can I/we address them?” (Zamani et al., 2023) become

paramount during this phase. Ultimately, the post-development stage arrives, wherein individuals or

organizations either seek validation for their prior decision to adopt or reject the innovation or reconsider

their decision in light of new, conflicting information regarding the innovation.

The transition to successive stages is inherently sequential, with each stage requiring time to foster

acceptance of the innovation (Takahashi et al., 2024). A failure to fulfill any requirements halts diffusion,

that is innovation may be rejected even at any point of deployment and post-development process (Xia et

al., 2022). This framework highlights the imperative for a methodical advancement through these stages to

secure buy-in from innovators (Ratcliff and Doshi, 2016). Conversely, a disregard for these sequential

stages, as noted by Guidolin and Manfredi (2023), may lead to a stagnation in the spread of innovation.

2.3 Critical success and failure factors during the AI adoption

The core determinants of competitive advantage and exceptional performance are guided by the

Resource-Based View (RBV) within strategic management. These determinants stem from the

characteristics of internal resources and the acquisition of external resources and capabilities, which are

both valuable and difficult to replicate (Bromiley and Rau, 2016, Nayak et al., 2023, Jegan Joseph Jerome

et al., 2024). Upon traversing the aforementioned three critical phases, the strategic embracing of AI can

significantly enhance organizational efficiency, fostering a sustainable competitive advantage (Yaroson et

al., 2024, Dey et al., 2023). The success of this transformative process hinges on the systematic

identification and application of CSFs throughout distinct stages (Table 2) (Kumar et al., 2023b, Fosso

Wamba et al., 2022, Govindan, 2024). In contrast, successfully bringing AI is also a complex process with

CFFs across the whole lifecycle (Table 3).

Table 2. Critical success factors.

Lifecycle

Success Factors (S)

Reference

S1: Comprehensive strategic

benefits of AI

Cannas et al. (2023); Sharma et al. (2022)

S2: Insightful and effective strategy

formulation

Zhang et al. (2023); Modgil et al. (2021); Qi et

al. (2023)

S3: Unwavering top management

support

Lai et al. (2023); Belhadi et al. (2024)

Pre-

development

S4: Thorough AI understanding

Helo and Hao (2022); Hendriksen (2023)

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S5: Robust in-house expertise

Dey et al. (2023); Pournader et al. (2021); Jauhar

et al. (2023); (Li et al., 2023)

S6: Advanced IT infrastructure

El Jaouhari and Hamidi (2024); Fosso Wamba et

al. (2022)

S7: Committed stakeholder buy-in

Hangl et al. (2023); Richey Jr et al. (2023)

S8: Proactive competition-driven

innovation

Kumar et al. (2023b); Dora et al. (2022)

S9: Substantial government

incentives

Das et al. (2023); Modgil et al. (2021)

S10: Responsive customer demand-

driven

Pereira and Frazzon (2021); Hao and Demir

(2024)

S11: Diverse external expertise

Belhadi et al. (2024); Riahi et al. (2023)

S12: Effective cross-sector

collaboration

Nwagwu et al. (2023); Hendriksen (2023)

S13: Clear and consistent effective

communication

Belhadi et al. (2024)

S14: Comprehensive and ongoing

employee training

Dey et al. (2023)

S15: Proactive stakeholder

engagement

Richey Jr et al. (2023); Charles et al. (2023)

Deployment

S16: Effective AI governance

Muthuswamy and Ali (2023); Nunan and Di

Domenico (2022)

S17: Regular AI performance

reviews

El Jaouhari and Hamidi (2024); Modgil et al.

(2021)

S18: Targeted KPIs-focused

reviews

Marinagi et al. (2023); Rasool et al. (2022)

Post-

development

S19: Continuous feedback

mechanisms

Richey Jr et al. (2023), Min (2010)

In the pre-development phase, as Modgil et al. (2021) postulates, the strategic management of

resources can foster capabilities, and in the context of AI, this implies a deliberate alignment of AI resources

with strategic objectives. This strategy should leverage top management support to align with the vision of

AI (Belhadi et al., 2024), fostering a culture that understands and embraces it (Hendriksen, 2023).

Cultivating in-house expertise (Jauhar et al., 2023) ensures that AI solutions are tailored to the unique

operational context while securing stakeholder buy-in (Hangl et al., 2023) guarantees a collaborative

approach to this transformative initiative. Recognizing the impetus provided by competition (Kumar et al.,

2023b) and customer demands (Belhadi et al., 2024) propel the strategy forward, potentially augmented by

external expertise (Belhadi et al., 2024) and government incentives (Das et al., 2023) to optimize the

organization with cutting-edge AI technologies. The deployment stage shifts the focus to the

operationalization of AI. Effective cross-sector collaboration (Nwagwu et al., 2023) and communication

(Belhadi et al., 2024), as well as extensive employee training (Dey et al., 2023), are identified as critical for

the seamless integration of AI into business processes. Additionally, stakeholder engagement (Richey Jr et

al., 2023) and robust AI governance structures (Muthuswamy and Ali, 2023) are requisite for the ethical

deployment of AI. In the post-development stage, the systematic evaluations of AI deployments (El

Jaouhari and Hamidi, 2024), coupled with the measurement against defined Key Performance Indicators

(KPIs) (Marinagi et al., 2023), are essential components in the continuous improvement cycle of such

systems. The establishment of continuous feedback mechanisms is critical for the enhancement of AI

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systems and to ensure their operations are in alignment with the strategic goals of the organization, a process

supported by the findings of Richey Jr et al. (2023) and Min (2010).

Initially, AI initiatives often struggle with the scarcity of high-quality data (Cannas et al., 2023) and

a lack of transparency in decision-making processes. It may also lead to ethical concerns and eroding trust

among potential users (Hendriksen, 2023). The situation is further deepened by the nature of AI to

perpetuate pre-existing biases (Varsha, 2023), alongside a degree of resistance from employees (Fosso

Wamba et al., 2023), which is frequently attributed to an insufficiency in the requisite knowledge for

leveraging AI optimally (Dey et al., 2023). It is crucial for its goals to align with business strategies

(Satornino et al., 2024), but support from leaders (Allahham and Ahmad, 2024), sufficient funding (El

Jaouhari and Hamidi, 2024), and the necessary infrastructure can often fall short (Yadav and Majumdar,

2024). When AI systems are deployed, they must be robust enough to cope with changes and adaptable to

new challenges, which calls for good change management (Brau et al., 2024) and better staff training (Rana

et al., 2022), as well as improved communication (Richey Jr et al., 2023) and teamwork across different

departments (Dolgui and Ivanov, 2023). Post-development, the critical failure attention turns to how it is

overseen to improve continuously, with questions about who is responsible if problems arise and how to

handle the legal aspects of AI use (Hendriksen, 2023).

Table 3. Critical failure factors.

Lifecycle

Failure factors (F)

Reference

F1: Insufficient or low-quality data

Nayal et al. (2022); Cannas et al. (2023)

F2: Uninterpretable and irresponsible

Hendriksen (2023); Johnson et al. (2022)

F3: Unethical and mistrust of AI

Crockett et al. (2021); Xu et al. (2021)

F4: Unfairness and prejudice of AI

Varsha (2023); De Bock et al. (2023)

F5: Human resistance

Fosso Wamba et al. (2023)

F6: Lack of domain knowledge

Dey et al. (2023); Shah et al. (2023)

F7: Unclear business goal

Helo and Hao (2022); Satornino et al. (2024)

F8: Lack of top management support

Cannas et al. (2023); Allahham and Ahmad

(2024)

F9: Funding constraint

El Jaouhari and Hamidi (2024); Varma et al.

(2007)

F10: Poor infrastructure

Kumar et al. (2024); Yadav and Majumdar

(2024)

F11: Lack of expertise

Muthuswamy and Ali (2023)

F12: Inconsistent standards

Belhadi et al. (2024)

Pre-

development

F13: Lack of benchmark cases

Hao and Demir (2024); Cannas et al. (2023)

F14: Non-robust/adaptive systems

Belhadi et al. (2024); Dey et al. (2023)

F15: Poor change management

Brau et al. (2024); Cannas et al. (2023)

F16: Communication gaps

Richey Jr et al. (2023); Das et al. (2023)

F17: Lack of adequate employee

training

Rana et al. (2022); Dora et al. (2022)

F18: Limited cross-department

collaboration

Dolgui and Ivanov (2023); Peng et al. (2023)

Deployment

F19: Lack of stakeholder consensus

Richey Jr et al. (2023); Wang et al. (2023b)

Post-

development

F20: Concerns of AI accountability

Hendriksen (2023); Rodríguez-Espíndola et

al. (2020)

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F21: Inadequate AI governance

Shrivastav (2021); Frederico (2023); Wamba

et al. (2023)

F22: Copyright issues

Mondal et al. (2023); Dwivedi et al. (2023)

Previous research has largely concentrated on exploring CSFs and CFFs independently when

examining AI adoption within organizations. However, our research proposes an innovative approach by

synthesizing both CSFs and CFFs within a singular knowledge graph framework. This integration permits

a comprehensive evaluation of these factors in unison and unveils the complex interplay and

interdependencies among them. Consequently, our methodology bridges a significant gap in the existing

literature and contributes a more comprehensive understanding of ‘how to foster an effective AI journey’

in SCM and OM environments.

3. Methodology

The research procedure is described in Figure 2 with key milestones; (i) conducting a

comprehensive literature review to identify and catalog CSFs and CFFs pertinent to the AI lifecycle; (ii)

designing a questionnaire and analyzing it using Exploratory Factor Analysis (EFA) and Composite

Reliability (CR) and Average Variance Extracted (AVE) to ensure that the validity of the constructed

dimensions; (iii) qualifying and quantifying the strength and significance of relationships between entities

in each dimension, Resource Description Framework (RDF) and Pearson correlation analysis are employed,

providing metrics for labeling the edges.

Figure 2. Research procedure (Source: Authors).

3.1 Research design

To cover the aforementioned gap, a survey is carried out using a questionnaire with a number of

CSFs and CFFs entities drawn from the literature review. The reliability and effectiveness of using

questionnaires to assess CSFs and CFFs have been substantiated by Joshi et al. (2024) and Sunder M and

Prashar (2020). This approach is designed to combine in-depth expert perspectives with findings from the

literature review, ensuring a robust analysis (Kumar et al., 2023a).

The initial section of the questionnaire sought to capture demographic data of the participants, while

the subsequent section probed into the severity of each critical factor. Participants were asked to rate each

factor on a scale where ‘0’ indicated ‘not critical’ and ‘10’ signified ‘exceptionally critical’, as delineated

in Tables 2 and 3. The breakdown of factors was as follows: in the pre-development stage, 24 entities were

identified, 11 of which were success factors and 13 failure factors; the deployment stage comprised 11

entities with five success and six failure factors; and the post-development stage encompassed three of each.

It is important to stress that all critical factors listed in the questionnaire have been fully explained to all

respondents to generate an identical understanding of AI in SCM and OM. Prior to the official publication,

a pilot test was conducted with four subject matter experts, two from academia and two from industry.

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Following a review and incorporation of the feedback from experts, the pilot yielded a Cronbach’s Alpha

coefficient of 0.755, indicating a respectable level of internal consistency. We then, therefore, publish the

survey officially.

3.2 Data collection

The questionnaire was shared online with professionals in AI supply chain and operations roles,

such as CEOs, Operations Research Scientists, AI Engineers, and Data Architects. We started by carefully

selecting over 300 individuals based on their professional backgrounds to ensure that respondents had

strong expertise in the field. This approach led to 197 people beginning the survey, with 37 completing it

fully. Around 70% of the respondents were in senior positions, providing a solid foundation of experience

and insight for our study. This group of experienced participants supports the analysis of knowledge

patterns, as noted by Guo et al. (2022), Abidi et al. (2005) who emphasize that both practical and technical

knowledge from experts are important for building effective knowledge graphs. By focusing on experts,

this research ensured the data was relevant and kept it consistent, as previous studies suggest that even

smaller sample sizes can be suitable when respondents are highly specialized (Fink, 2002). The similarity

among participants also helps improve the reliability of factor extraction, reducing differences in responses

(Azadegan et al., 2013). CR and AVE metrics are considered reliable even with smaller sample sizes, as

long as certain conditions are met (Peng and Lai, 2012). Specifically, when the collected data maintains

high quality, meaning it reflects clear and consistent responses (Wacker, 1998), and factor loadings meet

the required thresholds for statistical acceptability (Wolf et al., 2013). Meeting the required factor loadings

also helps ensure that even smaller groups can provide dependable results, focusing more on data quality

rather than quantity. (Boateng et al., 2018).

The demographic architecture of the dataset (Table 4) presents a broad spectrum of experts within

the domain of AI in SCM and OM, with a composition that includes AI researchers (10.8%), data scientists

(43.2%), operational specialists/engineers/analysts (29.7%), AI specialists/developers (13.5%), and supply

chain specialists (2.7%). Specifically, respondents spanned from young professionals, aged 20-30 (54%) to

seasoned experts over 61 (5.4%). Educational backgrounds were distinctly skewed towards higher

academic qualifications, with holders of master’s degrees embodying a significant 48.6% of the respondents.

Individuals with 1-3 years of experience formed the largest group (17%), indicating a rapidly expanding

industry attracting new talent. In contrast, those with more than 5 years of experience accounted for 7% of

the sample, emphasizing the nascent nature of the AI field. Regarding organization size, a significant 21%

of participants were from large organizations, underscoring the increasing importance of AI in substantial,

established businesses.

Table 4. Demographic architecture.

Age

Education

Level

Work

Experience

Position

Organization

Size

20-30

Doctorate

degree

<4 months

AI researcher

Micro (0-9

employees)

31-40

Master

degree

4 months-1year

Data scientist

Small (10-49

employees)

41-50

Bachelor

degree

1-3 years

Operational

specialist/enginee

r/analyst

Medium (50-249

employees)

51-60

Secondary

education

3-5 years

specialist/develop

Large (>250

employees)

>61

Associate

degree

>5 years

Supply chain

specialist

3.3 Data analysis

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Knowledge graphs are constructed by presenting entities (e.g., individuals, places, and objects) as

nodes and the relationships between them as edges, thereby creating a network that mirrors the complexity

and interconnectedness of real-world data (Hogan et al., 2021). The formal representation of a knowledge

graph

𝐺

can be denoted as

𝐺

(𝑁,𝐸, 𝐿, 𝑓)

, where

𝑁

is a set of entities,

𝐸

⊆

𝑁

∗

𝑁

is a set of edges,

𝐿

is a

set of labels, and

𝑓:𝐸→𝐿

is an assignment function from edges to labels. An assignment of a label

𝛽

to an

edge E= (

𝛼,𝛾

), can be viewed as a triple G=(

𝛼

𝛽

𝛾

) and visualized as shown in Figure 3.

Figure 3. An example of a triple in a directed labeled graph.

The foundational principles of knowledge graphs are deeply rooted in semantic web technologies,

which strive to make data machine-readable while retaining its meaning (Chen et al., 2020). A critical

aspect of these principles is the utilization of ontologies, defined as a structured framework that categorizes

and delineates the relationships between concepts within a domain (Ehrlinger and Wöß, 2016). This

structure is instrumental in providing context and meaning to the data, enabling sophisticated computational

processes. The RDF is a standard model for data interchange on the web, utilizing triples in the form of

(subject, predicate, object) to represent data, which aligns with the structure of knowledge graphs (Fensel

et al., 2020). In addition to leveraging qualitative relationships within the RDF, such as ‘improve’ and

‘reduced_by’, this research incorporates quantitative measures by assigning the Pearson correlation

coefficient as the label for each edge (Tosi and Dos Reis, 2021). That is, for

𝛽

connecting

𝛼

and

𝛾

, the

function ƒ(

𝛽

)

would assign the Pearson correlation coefficient between the variables represented by

𝛼

and

𝛾

as the label for

𝛽

, as the example shown in Figure 4.

Figure 4. RDF triple structure diagram example.

In constructing a knowledge-graph ontology for our research, entities were identified through a

comprehensive literature review (Table 2 and Table 3), ensuring a robust research foundation drawn from

prior academic findings (Monshizadeh et al., 2023). Relationships between these entities were then

established, grounded in expert judgment that aligns with established theories and empirical evidence (Chen

et al., 2020). Furthermore, the strength of the connections within the knowledge graph was quantitatively

reinforced by correlation analysis, thereby providing a statistically sound basis for the relational intensity

among the identified entities (Tao et al., 2020). The details of entities, edges and strength are shown in the

Appendix A.

4. Findings

We conducted reliability and validity tests, which are essential to establish the consistency and

accuracy of the constructs being measured. Following this foundational step, dimension extraction is

undertaken using EFA through Principal Component Analysis (PCA). EFA is a statistical technique used

to identify underlying factors or components that explain the patterns of correlations within a set of

observed variables (Fabrigar and Wegener, 2011). By grouping variables based on their intercorrelations,

PCA reveals latent structures within the data, simplifying complex relationships by transforming the

original variables into a smaller number of uncorrelated principal components that retain most of the

variation present in the data set (Abdi and Williams, 2010). Once these dimensions are identified, they are

carefully examined through CR and AVE to ensure the dimensions reflect the underlying constructs they

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represent. Subsequently, Pearson correlation coefficients are used to assess the strength and direction of the

relationship between the extracted entities within each dimension.

4.1 Reliability and validity

Reliability and validity are prerequisites for ensuring the accuracy and credibility of the

measurement and, consequently the research findings. Reliability refers to the consistency and stability of

the measurement across time and various contexts, ensuring that the results are repeatable and not due to

random chance. Validity, on the other hand, assesses whether the test measures what it is intended to

measure, confirming that the inferences drawn from the data are sound and meaningful. Specifically,

Cronbach’s Alpha and Kaiser-Meyer-Olkin (KMO) Test and Bartlett’s Test of Sphericity were conducted

prior to testing whether the items are appropriate to conduct the analysis.

In Cronbach’s Alpha test, the value ranges from zero to one, with higher values indicating greater

internal consistency and, thus, more reliable scales. Values above 0.7 are typically considered acceptable.

As it evident from Table 5, Cronbach’s Alpha test revealed a significant value in each lifecycle stage with

a minimum value of 0.860 in post-development stage. With all values well above the threshold, the

instrument demonstrates potential for repeated use while maintaining consistent quality and performance.

Table 5. Cronbach’s Alpha test.

Lifecycle

Cronbach’s Alpha

# of items

Pre-development

0.890

Deployment

0.925

Post-development

0.860

For pre-development, the KMO value is 0.570, which is generally considered mediocre, as KMO

values should be above 0.6 to be deemed adequate. Nonetheless, the significance level of Bartlett’s test is

less than 0.001, which suggests that the variables are related, and the data is likely suitable for factor

analysis. The deployment stage shows a KMO value of 0.837, which is considered meritorious and highly

suitable for factor analysis. The post-development stage has a KMO value of 0.782, which is also above

the acceptable threshold, indicating that the data is appropriate for factor analysis. In all stages (Table 6),

Bartlett’s Test shows statistical significance (p < .001), confirming that the correlation matrix is not an

identity matrix and suggesting that the data does not consist of unrelated variables.

Table 6. KMO test and Bartlett’s test.

Bartlett’s Test of Sphericity

Lifecycle

Kaiser-Meyer-Olkin Measure

of Sampling Adequacy

Approx.

Chi-Square

Sig.

Pre-development

0.570

611.758

276

<.001

Deployment

0.837

291.661

<.001

Post-development

0.782

111.906

<.001

4.2 Dimension extraction

EFA via PCA is a statistical method used to discern the latent structures within a dataset by

identifying clusters of variables that share common variance. PCA simplifies data by transforming it into

principal components, which are ranked by the amount of variance they explain, thus reducing the

complexity of dataset while retaining its essential patterns.

Upon examining the dimension structure within the pre-development stage of the rotating

component matrix (Table 7), each item was considered in the context of its highest loading. This aligned

with the standard approach when cross-loadings occur (Costello and Osborne, 2019). The constituents of

Dimension 1 include F12, F9, F4, F13, and F7, which showcase a predominant association with this factor,

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indicative of a latent variable captured by these items. Dimension 2 is represented by F3, S2, F2, and S3,

suggesting a distinct yet related construct. Subsequent dimensions are delineated by items F8, S5, F11, S4,

and S7 for Dimension 3; S9, S8, S11, and S6 for Dimension 4; F1 and S10 for Dimension 5; F5 and S1 for

Dimension 6; and finally, F10 and F6 for Dimension 7. This strategy, which prioritizes the highest loadings

without omitting cross-loaded items, facilitates a comprehensive interpretation of the data while retaining

the integrity and complexity of the underlying constructs.

Table 7. Pre-development rotating component matrix.

Factors

F12

.733

.457

.724

.698

.542

F13

.696

.363

.636

.426

.879

.784

.376

.452

.703

.435

.406

.339

.734

.727

.355

F11

.660

.305

.356

.505

.652

.367

.578

.486

-.383

.826

.752

S11

.662

.560

-.304

.597

.554

.882

S10

.720

.765

.324

.748

F10

.812

.401

.475

.683

In the deployment rotating component matrix (Table 8), adherence to the principle of maximal factor

loadings allows for the classification of the dataset into two distinct dimensions. Each dimension

encapsulates a unique construct, with the eighth dimension being constituted by the factors F19, F18, S15,

F16, S12, and S13 due to their primary loadings. The ninth dimension is composed of factors F14, S14,

F17, F15, and S16, which exhibit the highest loadings in this separate construct.

Table 8. Deployment rotating component matrix.

Factors

F19

.886

F18

.828

S15

.819

.367

F16

.721

.398

S12

.692

.360

S13

.579

.524

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F14

.912

S14

.361

.865

F17

.691

F15

.588

.590

S16

.429

.513

In post-development stage (Table 9), the tenth dimension is robustly characterized by variables S19,

S18, and S17, manifesting high loadings of 0.904, 0.845, and 0.838 respectively, thereby suggesting a

potent, cohesive construct. The eleventh dimension is predominantly influenced by F20, with an exemplary

loading of 0.932, alongside F21 and F22, which exhibit substantial cross-loadings, but demonstrate a

stronger affiliation with this dimension, evidenced by loadings of 0.751 and 0.671, respectively.

Table 9. Post-development rotating component matrix.

Factors

D10

D11

S19

.904

S18

.845

S17

.838

F20

.932

F21

.469

.751

F22

.401

.671

4.3 Dimension validation

The validation of dimensions in this study evidence robust internal consistency across the lifecycle

stages, reflected in Cronbach Alpha and CR scores exceeding the 0.7 benchmark (Table 10). The AVE for

dimensions D2, D4, and D5 through D11 surpasses the 0.5 threshold, indicating a strong convergent validity.

Even in cases where the AVE does not reach this threshold, such as in dimensions D1 and D3, validity is

maintained when Composite Reliability is above 0.6, adhering to the standards set forth by Fornell and

Larcker (1981).

Table 10. Dimension validation.

Lifecycle

Dimensions (D)

Components

Cronbach

Alpha

Composite

Reliability

Average

Variance

Extracted

F12

F13

0.830

0.826

0.498

0.810

0.803

0.518

F11

0.804

0.452

Pre-development

S11

0.769

0.804

0.511

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S10

0.707

0.785

0.650

0.664

0.728

0.572

F10

0.719

0.563

F19

F18

S15

F16

S12

S13

0.908

0.890

0.579

F14

S14

F17

F15

Deployment

S16

0.860

0.845

0.534

S19

S18

D10

S17

0.875

0.897

0.744

F20

F21

Post-

development

D11

F22

0.795

0.832

0.628

4.4 Pearson correlation coefficients

Pearson correlation coefficients were instrumental in mapping the density and valence of

connections between data points, yielding insights into the latent structures within the graph. These

coefficients quantitatively characterized the relationships, providing a robust statistical foundation for the

architecture of network, and underscoring potential areas of concentrated knowledge flow. For the

dimensions from D1 to D7 listed in the pre-development stage (Table 11), the Pearson Correlation analysis

reveals predominantly significant relationships (sig < 0.05) between the majority of factor pairs (except F7-

F12 in D1 and S7-F11 in D3), implying substantial interconnectivity within the graph. Notably, certain

pairs demonstrate a moderate to strong correlation, such as F3 and F2 (0.684), indicating a substantial

positive relationship. In contrast, several pairs exhibit weaker correlations, signifying a less pronounced

linear relationship, as seen between F3 and S3 (0.343).

Table 11. Pearson correlation for pre-development stage (D1-D7).

Pearson Correlation

F12

F13

F12

0.641

0.611

0.653

Sig>0.05

0.563

0.481

0.439

0.432

0.378

F13

0.518

Pearson Correlation

0.592

0.684

0.343

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0.602

0.510

0.357

Pearson Correlation

F11

0.414

0.453

0.576

0.441

0.593

0.588

0.447

F11

0.440

Sig>0.05

0.408

Pearson Correlation

S11

0.597

0.517

0.418

0.388

0.483

S11

0.339

Pearson Correlation

S10

0.554

S10

Pearson Correlation

0.499

Pearson Correlation

F10

0.568

The Pearson correlation coefficients presented in Table 12 indicate strong interrelations among

factors during the development stage, delineated as D8 and D9. For D8, S12 significantly correlates with

S13 (r = 0.738), suggesting a robust link, it also maintains moderate to strong correlations with S15, F16,

F18, and F19, with coefficients ranging from 0.553 to 0.624. Similarly, D9 underscores S14’s significant

correlation with F14 (r = 0.840), implying a potent connection that may have substantial implications for

development dynamics.

Table 12. Pearson correlation for development stage (D8-D9).

Pearson Correlation

S12

S13

S15

F16

F18

F19

S12

0.738

0.624

0.553

0.576

S13

0.600

0.490

0.476

0.584

S15

0.839

0.619

0.772

F16

0.594

0.637

F18

0.707

F19

Pearson Correlation

S14

S16

F14

F15

F17

S14

0.566

0.840

0.703

0.561

S16

0.540

0.481

0.362

F14

0.662

0.524

F15

0.406

F17

The post-development stage correlation analysis presented in Table 13 showcases the relationships

among factors S17, S18, and S19 with significant correlations observed, particularly between S18 and S19

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(r = 0.795), indicating a strong relationship. In a similar vein, factors F20, F21, and F22 also display

meaningful correlations, with the relationship between F20 and F21 (r = 0.646) being notably substantial.

Table 13. Pearson correlation for post-development stage (D10-D11).

Pearson Correlation

S17

S18

S19

S17

0.586

0.723

S18

0.795

D10

S19

Pearson Correlation

F20

F21

F22

F20

0.646

0.496

F21

0.552

D11

F22

5. Discussion and managerial insights

In this study, we employ Cypher, the designated query language for Neo4j (a graph algorithm) to

model the interactions and relationships. Hence, we construct critical ontologies for the AI lifecycle (refer

to Figure 5). Neo4j is acclaimed for its proficient handling of complex networks for the representation of

each factor as a node within a graph. Its edges explicitly illustrates the mutual influences among these nodes

(Liu et al., 2024, Saad et al., 2023). Using Cypher, this research explores the intricate lifecycle network

spanning dimensions D1 through D11 (details provided in Appendices A and B). The focus is specifically

on relationships with a significance level (sig) below 0.05, ensuring that only statistically significant

connections are considered. The utilization of a stringent label threshold ensures that the analysis is

concentrated on the most statistically significant connections.

Figure 5. CSF and CFF knowledge graph (Detailed ontology construction refers to Appendix A and B).

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As shown in Table 13, the 11 clustered dimensions each have a targeted objective, outlining specific points

of resistance along with corresponding forces that can be leveraged to counteract these challenges. This

clustering approach provides a clear objective for each dimension and demonstrates how CFFs can be

strategically transformed into CSFs.

Table 13. Dimensions and transformation strategies.

Dimension

Objective

Resistance

Driving Forces

Transformation

Strategies

D1: Setting Clear

Goals and

Standards

Define clear goals

to secure funding

and ensure AI

reliability.

Vague goals and

standards that

undermine

credibility.

Well-defined

objectives that

align with funding

requirements.

Align project

goals with funder

expectations; set

clear ethical and

technical

benchmarks.

D2: Ensuring

Clear and

Accountable AI

Establish

leadership-driven

accountability to

build trust in AI.

Lack of

transparency and

ethical oversight

in AI systems.

Strong ethical

standards and

clear leadership

accountability.

Promote

transparency

through ethical

guidelines and

assign leadership

roles for oversight.

D3: Activating

Leadership to

Bridge Gaps

Bridge expertise

gaps and enhance

commitment from

stakeholders.

Insufficient

expertise and lack

of stakeholder

engagement.

Committed

leadership focused

on skill-building

and engagement.

Create a

collaborative

environment that

brings together

technical and

business expertise.

D4: Competitive

Edge through

Partnerships

Strengthen

competitive

positioning with

partnerships and

IT infrastructure.

Limited access to

partnerships and

advanced

technology.

Access to skilled

partnerships and

advanced

infrastructure.

Invest in

partnerships and

cutting-edge

infrastructure to

stay competitive.

D5: Data Quality

Driven by

Customer Demand

Improve data

quality in AI

through

responsiveness to

customer needs.

Inconsistent data

quality due to

inadequate

customer insights.

Customer demand

guiding data

quality

improvements.

Use customer

feedback to refine

data inputs and

maintain high data

quality.

D6: Overcoming

AI Resistance

Reduce resistance

to AI adoption

with strategic

communication.

Employee

skepticism and

fear of AI.

Clear

communication of

AI benefits to

mitigate fear.

Educate

employees on AI

benefits and

provide structured

support during

transitions.

D7: Linking

Domain

Knowledge to

Robustness

Leverage domain

knowledge for

robust AI

infrastructure.

Weak

infrastructure due

to disconnected

domain insights.

Strong domain

knowledge

reinforcing

infrastructure

needs.

Integrate domain

knowledge into

infrastructure

planning for

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robust

deployment.

D8: Enhancing

Stakeholder

Engagement

Increase

engagement and

foster

collaborative

innovation.

Poor

communication

channels limiting

stakeholder input.

Active

communication

channels to

promote

engagement.

Enhance

stakeholder

communication

channels to boost

involvement.

D9: Empowering

Robustness

through Training

Enhance AI

robustness with

targeted training

and governance.

Limited training

on AI adaptability

and change

management.

Training programs

focused on

resilience and

adaptability.

Provide targeted

training and

governance

policies to support

adaptability.

D10: Cyclical

Performance

Management

Use KPIs for

ongoing

performance

assessment post-

development.

Lack of structured

review and

continuous

improvement.

KPI-driven

assessments

fostering

continuous

improvement.

Establish a KPI-

based review

process to assess

AI performance

over time.

D11:

Strengthening

Governance and

Integrity

Ensure ethical

governance and

intellectual

property

protection.

Weak governance

leading to

accountability

issues.

Robust

governance

frameworks for

ethical AI

management.

Strengthen ethical

and governance

frameworks to

ensure

accountability.

Dimension #1: Setting Clear Goals and Standards to Secure Funding and Build Reliable AI

The dynamics of the pre-development stage of AI are critically shaped by factors such as

inconsistent standards (F12), funding constraints (F9), and unclear business objectives (C7). Inconsistent

standards can hinder funding opportunities as investors typically look for stability and predictability in

regulatory guidelines. Additionally, the lack of uniform standards can create conditions conducive to bias

and prejudice within AI systems (F4), which is exacerbated by the absence of a comprehensive ethical

framework (Cheng et al., 2021). This can undermine trust and lead to resistance, which further complicates

the establishment of clear business goals. Additionally, the lack of benchmark cases (F13) make these

challenges worse by depriving the development process of critical reference points necessary for both

technical calibration and ethical scrutiny (Palladino, 2023).

Dimension #2: Ensuring Clear and Accountable AI with Leadership-Driven Strategies to Prevent Unethical

Practices and Build Trust

The pathway weighted at 0.68 from uninterpretable and irresponsible AI (F2) to unethical AI and

mistrust (F3) suggests a consequential relationship wherein the opacity and lack of accountability in AI

systems lead to ethical concerns and a loss of trust among stakeholders. This connection highlights the

criticality of ensuring AI systems are designed and deployed with clarity and responsibility to foster ethical

use and enhance trust (Patel, 2024). Concurrently, top management support (S3) is depicted as a

foundational element that directly impacts both effective strategy formulation (S2) with a weight of 0.59

and the twin concerns of AI interpretability and ethics (F2 and F3) with weights of 0.36 and 0.34,

respectively. This suggests that top management engagement is crucial not only for setting strategic

direction (Shafique et al., 2024) but also for prioritizing and addressing issues related to AI’s ethical and

operational transparency. The model further shows that effective strategy formulation (S2) exerts an

influence back onto the concerns of AI interpretability (F2) and ethics (F3), as evidenced by the weights of

0.60 and 0.59. This cyclical influence proposes that the strategies developed by organizations must

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inherently include considerations for addressing the challenges posed by AI systems, fostering a feedback

loop that continually refines both the strategy and the ethical governance of AI (Du and Xie, 2021).

Dimension #3: Activating Leadership to Bridge Expertise Gaps and Secure Stakeholder Commitment in AI

Advancements

Top management support (F8) serves as the keystone, with its absence negatively impacting in-

house expertise (S5, weight 0.41), understanding of AI (S4, weight 0.58), and stakeholder buy-in (S7,

weight 0.44). Leadership failure to back AI initiatives can stifle the development of necessary skills (F11),

compounding the expertise deficit (S5 to F11, weight 0.59). However, the presence of in-house expertise

can foster a broader organizational understanding of AI (S5 to S4, weight 0.59) and strengthen stakeholder

confidence (S5 to S7, weight 0.45). This expertise, when supported by leadership, can create a positive

feedback loop, enhancing AI literacy and buy-in across the board. Concurrently, the lack of expertise (F11)

directly inhibits AI understanding (weight 0.44). Both expertise and understanding are interdependent and

essential for gaining stakeholder trust for AI (Chowdhury et al., 2022, Habbal et al., 2024). The role of

leadership is thus twofold: to nurture and leverage in-house expertise (Dey et al., 2023) and to understand

AI sufficiently to support its integration and champion its cause to stakeholders (Merhi, 2023). Effective

management can address the expertise gap, which is crucial for the successful adoption of AI, and can turn

these potential failure factors into pillars of success.

Dimension #4: Competitive AI Edge through Expert Partnerships and Advanced IT Infrastructure

Competition (S8) is an engine that drives innovation, and in technology-centric business

environment, it often prompts organizations to reach out for external expertise (S11) to develop unique

competencies and differentiate themselves (weight 0.39). A symbiotic relationship emerges as competition

inspires companies to engage with experts (Belhadi et al., 2024), while these experts empower businesses

to navigate and shape competitive landscapes effectively. Additionally, government incentives (S9) can

serve to improve competitive behaviors, encouraging firms to adopt novel technologies and engage with

experts to exploit these benefits fully (weight 0.52). IT infrastructure (S6) is the backbone that supports all

technological endeavors in an organization and plays a critical role in how businesses respond to

competitive pressures (S8) and leverage government incentives (S9). Advanced IT infrastructure enables

rapid adaptation to market changes and competitive dynamics (weight 0.49), fostering agility and

innovation. Such agility is often bolstered by government incentives (S9), which can provide the financial

impetus for IT enhancements, positioning companies to capitalize on cutting-edge technologies and

improve cybersecurity (weight 0.42), effectively translating policy support into competitive advantage.

Furthermore, IT infrastructure often necessitates external expertise (S11) to harness sophisticated

technologies that underpin competitive strategies (weight 0.34). This expertise, whether in the form of

consulting or technical partnerships, is critical for the continuous evolution of IT capabilities within an

organization, promoting a culture of innovation and enabling a competitive stance in the market (Sahoo et

al., 2024).

Dimension #5: Driving Data Quality Improvement through Customer Demand in AI Applications

The edge between the customer demand-driven approach (S10) and insufficient or low-quality data

(F1), with a weight of 0.55, suggests that customer demands significantly influence the recognition and

resolution of data issues in AI adoption. As customer demands guide business objectives, the necessity for

high-quality data becomes clear, because satisfying customer needs effectively hinges on the accuracy and

integrity of data. In scenarios like demand forecasting and warehouse optimization, when data is insufficient

or of low quality, organizations may struggle to align their products or services with customer expectations,

leading to missed opportunities and potential loss of market share (Hangl et al., 2023). Hence, a customer

demand-driven approach highlights deficiencies in data and acts as an enabler for organizations to prioritize

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data enhancement efforts, ensuring that their decision-making processes are informed, and customer

satisfaction is optimized.

Dimension #6: Overcoming AI Resistance Through Awareness of Benefits and Strategic Change

Management

The relationship between the perceived benefits of AI (S1) and human resistance to AI (F5), as

indicated by the strength of the edge is 0.50. This denotes a significant influence, suggesting that as

individuals and organizations become aware of the tangible advantages AI offers, such as enhanced

efficiency, improved accuracy, and superior outcomes, the likelihood of resistance may decrease (Cannas

et al., 2023). Clear communication regarding the value and practical enhancements AI provides can serve

as a prerequisite for acceptance and adoption, overcoming reluctance born from fear, misunderstanding,

mistrust or uncertainty surrounding AI technologies (Merhi and Harfouche, 2023). In this context, the

benefits of AI function as facilitators for adoption but also as essential tools for change management,

directly targeting the mitigation of resistance (Abadie et al., 2023).

Dimension #7: Linking Domain Knowledge to Infrastructure Robustness in AI Development

A significant link between poor infrastructure (F10) and the lack of domain knowledge (F6), with a

weight of 0.57, suggests that an insufficient understanding of specific domain requirements is a key

contributor to the development of poor infrastructure. When those tasked with constructing and upholding

an organization’s infrastructure lack essential domain knowledge, they may be unable to choose suitable

models, apply best practices, or formulate effective maintenance protocols, leading to an ill-equipped

infrastructure to support the needs of AI (Habbal et al., 2024). Consequently, this can impede operational

efficiency and its capacity to innovate, highlighting the critical nature of domain knowledge in establishing

a robust and capable infrastructure (Richey Jr et al., 2023).

Dimension #8: Bridging Communication for Enhanced Stakeholder Engagement and Collaborative AI

Innovation

Stakeholder engagement (S13) lies at the heart of this interplay, with significant weights illustrating

its reliance on both Communication gaps (S12) and Effective communication (S15), implying the bridging

of communication gaps can potentiate stakeholder engagement. These connections are underscored by

Belhadi et al. (2024), where communication serves as a medium for mutual understanding and AI

consensus-building, a prerequisite for cohesive stakeholder engagement. The relational weight of 0.48

between Effective cross-sector collaboration (F18) and Stakeholder engagement (S13) offers quantitative

evidence to their qualitative synergy, reflecting where the depth and breadth of stakeholder relationships

catalyze collaborative innovation. Meanwhile, Gaps in communication (S12) and Limited cross-

departmental collaboration (F16) often create a cycle of problems in organizations that work in separate

silos. These challenges tend to feed into each other, leading to a fragmented organization, with departments

that struggle to sync up and work cohesively towards AI system integration. In parallel, the weights attached

to the Lack of stakeholder consensus (F19) indicate its pressuring impact on collaboration and engagement,

challenging organizations to navigate these gaps to unify varying stakeholder viewpoints and foster

collective AI strategy (Wang et al., 2023b).

Dimension #9: Empowering AI Robustness and Change Management through Targeted Employee Training

and Governance

Employee training (S14) and Effective AI governance (S16) serve as crucial nodes, which, through

their interconnections, imply that the proficiency and knowledge provided by robust training programs are

instrumental in fostering governance structures capable of navigating the challenges posed by Non-

robust/adaptive systems (F14). The weight of 0.54 between S16 and F14 suggests a correlation between

informed oversight and system adaptability, where effective governance is likely to mitigate risks

associated with non-robust systems (Díaz-Rodríguez et al., 2023). Similarly, Poor change management

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(F15) is connected to Lack of adequate employee training (F17), with a weight of 0.41 indicating a

relationship of moderate strength. This connection presents training gaps in change management, echoing

sentiments that adequate training is a determinant in successfully managing and adapting to technological

change within organizations (Bag et al., 2023). Moreover, the weighted edges connecting S14 with F14 and

F15 (0.84 and 0.70, respectively) reflect the paramount role that employee training plays in reinforcing

system robustness and in navigating the pitfalls of AI change management (Cadden et al., 2022). This aligns

with the discourse on the strategic alignment of human capital development with technological and

organizational change processes (Hao et al., 2024).

Dimension #10: Cyclical Performance Management: KPI-Driven Reviews Enhancing AI Post-

Development

In the post-development stage, the necessity for regular AI reviews (S17) has emerged as a crucial

facet of maintaining operational integrity and strategic alignment. The role of KPIs reviews (S18) in this

process is underscored by their capacity to quantitatively assess the impact of AI systems on organizational

goals, with a particular weight of 0.59 indicating a moderately strong influence on the continuous feedback

loop (S19) that is central to AI system refinement and performance optimization (Pournader et al., 2021).

Furthermore, the relationship between KPI reviews and continuous feedback (with a substantial weight of

0.72) reveals a bidirectional synergy where the metric-driven analysis shapes and is also shaped by the

dynamic input received from the AI’s operational output and user interactions. This synergy highlights the

significance of KPIs as both a reflective and formative mechanism in guiding AI evolution within

organizations (Singh, 2023). In analyzing directionality of these interconnections, it becomes evident that

the flow of influence is cyclical, with each element reinforcing and being reinforced by the others. Regular

AI reviews (S17) necessitate a critical examination of KPIs (S18), which, in turn, informs the continuous

feedback (S19) that shapes the iterative improvements in AI systems. This feedback then loops back to

influence subsequent AI reviews, establishing an ongoing, iterative cycle of performance management and

enhancement (Richey Jr et al., 2023).

Dimension #11: Strengthening AI Governance to Ensure Accountability and Copyright Integrity

The significant weight of 0.65 from inadequate AI governance (F21) to concerns of AI

accountability (F20) underscores the substantial influence that deficits in governance exert on the

amplification of accountability issues. The lack of robust governance frameworks has been identified as a

critical factor contributing to the emergence of opaque algorithmic decision-making, which in turn fuels

accountability concerns as it obscures the traceability of AI actions and decision paths (Oppioli et al., 2023).

This is further complicated when AI systems, in the absence of clear governance structures, act in ways that

challenge stakeholders’ abilities to attribute responsibility for unexpected outcomes. Parallelly, the weight

of 0.50 from inadequate AI governance (F21) directly to copyright issues (F22) describes a significant

direct relationship. Governance deficiencies affect accountability and translate into legal challenges as AI

systems especially those with generative models, ungoverned and unchecked, may engage in the creation

or manipulation of content that breaches copyright law (Lucchi, 2023). This raises the pressing need for

governance that can proactively define the ethical and legal boundaries for AI, thereby minimizing the risk

of such infringements.

Cross-Dimensional Framework (#1-11) for AI in SCM and OM: Spanning Pre-Development, Development,

and Post-Development Phases

In the pre-development phase of AI integration in SCM and operations OM, setting a strong

foundation is essential to ensure that the AI initiatives meet industry standards, regulatory requirements,

and stakeholder expectations. Dimension #1: Setting Clear Goals and Standards is the starting point, where

clear objectives must be defined to secure funding, build reliable systems, and gain stakeholder confidence.

Without structured goals, AI projects in SCM and OM may struggle with regulatory compliance and risk

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losing support from investors and other stakeholders who demand transparency, reliability, and ethical

assurances in AI-driven processes. For example, inconsistencies in AI standards can lead to biased or

unreliable decisions, especially in sensitive areas like supplier selection and route optimization, potentially

harming supplier relationships and raising ethical concerns (Guida et al., 2023). This is why benchmarking

against established cases is crucial; it provides a reference for both technical calibration and ethical

compliance, helping to align AI with industry standards (De Bock et al., 2024). Building on this, Dimension

#2: Ensuring Clear and Accountable AI emphasizes the role of leadership in establishing transparency and

accountability. In SCM and OM, where AI decisions affect various stakeholders, leaders must ensure AI

aligns with ethical principles and corporate values. Leadership that fosters accountability can create a

culture of responsible AI use, turning potential resistance into support from stakeholders (Dey et al., 2024).

This leadership-driven approach helps in managing risks, as it ensures that AI systems are auditable and

interpretable, critical factors for regulatory compliance in these tightly regulated fields (Chatterjee et al.,

2022). Additionally, Dimension #3: Activating Leadership to Bridge Expertise Gaps and Secure

Stakeholder Commitment addresses the need for specialized knowledge. AI applications in real-time

tracking, predictive maintenance, and demand forecasting require both technical AI skills and a deep

understanding of SCM/OM functions. However, many organizations may lack in-house expertise. Leaders

play a critical role in bridging this gap by fostering an environment that supports continuous learning,

helping employees to build the necessary AI skills to engage with and leverage AI tools effectively

(Shahzadi et al., 2024). Dimension #4: Competitive AI Edge through Expert Partnerships and Advanced IT

Infrastructure highlights the importance of external partnerships and robust IT systems. Collaborations with

AI experts, universities, and technology providers can bring advanced capabilities that improve AI’s

effectiveness in predictive analytics, allowing for accurate demand forecasts, optimized distribution, and

enhanced resilience to market changes (Helo and Hao, 2022). Robust IT infrastructure is vital for supporting

these capabilities, enabling rapid data processing and real-time decision-making, which is crucial in the

fast-paced SCM environment. Dimension #5: Driving Data Quality Improvement through Customer

Demand ensures that the data feeding AI models is both accurate and relevant. In demand forecasting and

customer service, aligning data quality with customer needs enhances the precision of AI-driven predictions

and boosts customer satisfaction (Aldunate et al., 2022). Finally, Dimension #6: Overcoming AI Resistance

Through Awareness of Benefits and Strategic Change Management and Dimension #7: Linking Domain

Knowledge to Infrastructure Robustness in AI Development address human and technical challenges.

Employee resistance, often stemming from concerns over job security, can be managed through clear

communication about AI’s role in enhancing, not replacing, human tasks (Jarrahi et al., 2023). Embedding

domain-specific knowledge into AI design further strengthens infrastructure robustness, ensuring AI

models are resilient and adaptable to real-world needs (De Bock et al., 2024).

In the deployment phase, effective engagement and management practices are critical to ensure that

AI is implemented smoothly and achieves the desired impact. Dimension #8: Bridging Communication for

Enhanced Stakeholder Engagement and Collaborative AI Innovation focuses on the importance of clear

communication channels across departments and with external stakeholders. In SCM, where interconnected

functions such as procurement, logistics, and warehousing must work closely, poor communication can

hinder the effectiveness of AI initiatives. Establishing open communication channels allows for goal

alignment, ensuring all stakeholders understand the objectives of AI, its role in improving operational

efficiency, and the specific contributions it brings to decision-making processes (Fosso Wamba et al., 2024).

This clarity and alignment encourage collaborative innovation, allowing stakeholders to contribute insights

that refine AI applications and make them more effective. Dimension #9: Empowering AI Robustness and

Change Management through Targeted Employee Training and Governance reinforces the importance of

preparing employees to work effectively with AI tools. SCM and OM rely on skilled employees who

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understand both the technological and operational aspects of AI. Training programs focused on AI literacy

help employees maximize the potential of AI and enhance the quality of their decisions. Additionally,

implementing governance policies provides clear guidelines for data handling, accountability, and ethical

AI use, ensuring AI aligns with both regulatory standards and the organization’s values (Adekugbe and

Ibeh, 2024). These governance frameworks also reduce risks by ensuring consistent, responsible AI

practices across the organization, establishing a foundation for reliable AI operations.

In the post-development phase, ongoing evaluation and governance are crucial to maintain AI’s

effectiveness, alignment with business goals, and compliance with evolving standards. Dimension #10:

Cyclical Performance Management establishes the need for KPI-driven reviews to continually assess AI’s

impact on key SCM metrics, such as delivery accuracy, inventory turnover, and cost efficiency. These

regular reviews create a feedback loop that enables managers to make timely adjustments to AI systems,

responding to seasonal trends, supply chain disruptions, or regulatory changes. For example, when

customer demand shifts, AI-driven inventory management can dynamically adjust stock levels, preventing

issues like overstock or stockouts and allowing for quick responses to real-time market needs (Sjödin et al.,

2023). This adaptability ensures that AI remains aligned with organizational objectives and continues to

provide value in a constantly changing environment. Lastly, Dimension #11: Strengthening AI Governance

emphasizes the importance of ethical and legal standards in AI decision-making, particularly in sensitive

areas such as supplier selection, where transparency and fairness are critical. Strong governance

frameworks prevent issues like algorithmic opacity by setting guidelines for clear, interpretable AI

decisions, which is essential for maintaining accountability (Novelli et al., 2024). Additionally, these

frameworks address legal concerns such as data privacy and intellectual property, especially in SCM

applications that handle large datasets, some of which may include proprietary or sensitive information. By

implementing structured policies on data usage, intellectual property rights, and accountability,

organizations can protect stakeholder trust and ensure that AI systems are compliant with industry standards

(Rezaei et al., 2024). This commitment to governance, coupled with ongoing training and evaluation, helps

create a resilient AI infrastructure that supports sustainable and responsible AI practices across SCM and

OM.

6. Conclusions

In AI integration for SCM and OM, certain factors can initially act as constraints but hold the

potential to be transformed into powerful enablers for success. These factors, while posing challenges, can

be strategically addressed to support the seamless adoption and operation of AI systems. For instance,

challenges such as unclear goals, ethical concerns, and lack of stakeholder buy-in are not merely obstacles;

they can become guiding forces when approached with a proactive strategy. Through clear goal setting,

robust governance frameworks, and targeted engagement, these constraints can evolve into strengths that

foster a sustainable AI environment in SCM and OM. This dynamic approach underscores the importance

of not just identifying barriers but actively working to transform them into support systems for AI success.

In the pre-development phase, it is crucial to define quantifiable objectives that align AI efforts with

specific business goals, such as reducing error rates or improving forecast accuracy. This clarity in goal

setting forms the foundation for targeted AI initiatives that directly address SCM and OM needs. Ethical

accountability, often perceived as a regulatory burden, can be reframed as a mechanism for trust-building

through internal and third-party audits that protect data privacy and mitigate bias. Leadership engagement

also plays a transformative role, as leaders who actively support AI initiatives help bridge the knowledge

gap within their organizations, fostering an innovation culture that aligns with AI objectives. Moreover,

strategic partnerships with academic institutions and tech experts introduce cutting-edge research and

practices into real-world applications, enhancing AI’s effectiveness. Technological infrastructure, essential

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for handling large datasets, should be robust enough to accommodate scalability and maintain data accuracy

and reliability. This phase also emphasizes data quality, which can initially seem like a daunting task due

to the volume of data involved in SCM and OM. However, by instituting thorough data validation processes

that check for consistency and accuracy, organizations can ensure that their AI systems make reliable and

informed decisions. Change management, supported by comprehensive training programs, equips

employees with the skills they need to adapt to AI integration, easing the transition and reducing resistance.

Lastly, domain expertise is critical for tailoring AI solutions to the unique challenges of SCM and OM.

Whether sourced internally or externally, professionals with deep industry knowledge ensure that AI

applications are responsive and aligned with the specific operational needs of their environment.

In the deployment phase, effective stakeholder engagement and robust change management are

pivotal. These elements, while often seen as complex, are essential for fostering a collaborative environment

that supports AI innovation. Effective communication across departments and with external partners helps

bridge gaps that can hinder AI deployment. This approach aligns different functional areas within SCM and

OM, such as procurement, logistics, and inventory management, fostering a unified strategy that maximizes

AI’s impact. Real-world examples from tech leaders like Google and IBM demonstrate the importance of

structured training programs that build employee proficiency in AI, equipping them to navigate new

technologies and maintain robust AI systems. Lessons from industry pioneers like Siemens also show that

when employees are proficient in AI, it strengthens governance structures, reducing the risk of non-robust

systems. The deployment phase requires a comprehensive approach that integrates communication, training,

and governance, creating a resilient foundation for AI adoption and fostering an environment where AI can

drive continuous improvement in operational performance.

The post-development phase focuses on continuous evaluation and adaptation to ensure AI systems

remain effective and relevant in a changing business landscape. In SCM and OM, where customer demands,

regulatory requirements, and market conditions evolve frequently, KPI-driven reviews are crucial for

tracking AI performance and identifying areas for improvement. By regularly assessing metrics like lead

times, order accuracy, and inventory turnover, organizations can ensure that AI systems adapt to real-world

changes and continue to meet strategic goals. For example, companies in the automotive industry, like Tesla

and Toyota, use ongoing evaluations to fine-tune their AI systems, ensuring they keep pace with production

demands and optimize supply chain efficiency. This iterative approach prevents AI from becoming obsolete

or misaligned with business needs, which was a common issue in earlier AI applications in inventory

management that struggled with fluctuating market trends. Furthermore, effective governance structures

play a critical role in this phase by ensuring data integrity and regulatory compliance. As AI applications

in SCM and OM often involve sensitive information, strong governance protects against potential risks

related to data privacy and intellectual property. Governance frameworks also enhance transparency, which

is essential for building trust in AI-driven decisions among stakeholders.

Finally, looking ahead, this research highlights the value of iterative enhancement of the knowledge

graph ontology, integrating real-time data analytics and question-answering capabilities. This ongoing

refinement would enable SCM and OM organizations to quickly adapt to technological advancements and

market shifts, making AI systems more agile and responsive to industry demands. The proposed knowledge

graph ontology provides a structured, practical framework for AI integration in SCM and OM, bridging the

gap between theoretical potential and real-world application. This approach ultimately fosters sustainable

AI adoption, allowing organizations to leverage AI as a dynamic, evolving tool that continuously aligns

with strategic goals, supports innovation, and strengthens competitiveness in a complex, rapidly changing

industry. By transforming initial constraints into support pillars, this framework ensures that AI becomes a

long-term asset that drives operational resilience and success in SCM and OM. Future directions for this

research advocate for an iterative enhancement of the knowledge graph ontology, utilizing real-time data

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analytics and question-answering systems. Such advancements are pivotal for organizations to maintain

relevance in the ever-evolving AI landscape. This iterative approach would enable a more agile adaptation

of organizational strategies to the demands of AI integration. Furthermore, this updated ontology promises

to be instrumental for subsequent research, providing a framework that narrows the divide between the

theoretical potential of AI and its practical, promoting successful AI journeys on SCM and OM.

Acknowledgements

The authors gratefully acknowledge the insightful feedback and valuable contributions of the two reviewers,

associate editor, and editor, which have significantly enhanced the clarity and quality of this work.

Funding information

This research did not receive any specific grant from funding agencies in the public, commercial, or not-

for-profit sectors.

Declaration of Competing Interest

No potential conflict of interest was reported by the author(s).

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Appendix A: AI lifecycle knowledge graph

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Appendix B: AI lifecycle ontology construction

Pre-

develop

ment

Entities/Edge/

Strength

(Dimension 1)

Inconsistent

standards

Funding

constraint

Unfairness and prejudice of AI

Lack of benchmark

cases

Unclear business goal

Inconsistent

standards

0.641

(results_fro

m/leads_to)

0.611

(leads_to/caused_by)

0.653

(leads_to/caused_by)

Sig>0.05

Funding

constraint

0.563

(exacerbates/ exacerbates_by)

0.481

(causes/caused_by)

0.439

(causes/caused_by)

Unfairness and

prejudice of AI

0.432

(exacerbates/

exacerbates_by)

0.378

(contributes_to/contri

buted_by)

Lack of

benchmark

cases

0.518

(results_from/leads_to

)

Unclear

business goal

Entities/Edge/

Strength

(Dimension 2)

Unethical and

mistrust of AI

Effective

strategy

formulation

Uninterpretable and irresponsible

Top management

support

Unethical and

mistrust of AI

0.592

(reduces/red

uced_by)

0.684

(exacerbates/ exacerbates_by)

0.343

(reduces/reduced_by)

Effective

strategy

formulation

0.602

(reduces/reduced_by)

0.510

(requires/required_for

Uninterpretable

and

irresponsible

0.357

(reduces/reduced_by)

Top

management

support

Entities/Edge/

Strength

(Dimension 3)

Lack of top

management

support

In-house

expertise

Lack of expertise

AI understanding

Stakeholders buy-in

Lack of top

management

support

0.414

(helps_gain/

helped_by)

0.453

(contributes_to/contributed_by)

0.576

(reduces/reduced_by)

0.441

(hinders/hindered_by)

In-house

expertise

0.593

(resolves/resolved_by)

0.588

(deepens/deepened_by)

0.447

(facilitates/

facilitated_by)

Lack of

expertise

0.440

(limits/limited_by)

Sig>0.05

understanding

0.408

(facilitates/

facilitated_by)

Page 53 of 56 Journal of Modelling in Management

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Stakeholders

buy-in

Entities/Edge/

Strength

(Dimension 4)

Government

incentives

Competition

-driven

External expertise

IT infrastructure

Government

incentives

0.597

(encourages

/encouraged

_by)

0.517

(encourages_utilization_of/encou

raged_by)

0.418

(promotes_upgrade_of/p

romoted_by)

Competition-

driven

0.388

(supports/supported_by)

0.483

(promotes_upgrade_of/p

romoted_by)

External

expertise

0.339

(optimizes/optimized_b

infrastructure

Entities/Edge/

Strength

(Dimension 5)

Insufficient or

low-quality data

Customer

demand-

driven

Insufficient or

low-quality

data

0.554

(improves/h

inders）

Customer

demand-driven

Entities/Edge/

Strength

(Dimension 6)

Human

resistance

Benefits of

Human

resistance

0.499

(improves/h

inders）

Benefits of AI

Entities/Edge/

Strength

(Dimension 7)

Poor

infrastructure

Lack of

domain

knowledge

Poor

infrastructure

0.568

(exacerbates

exacerbates

_by)

Lack of domain

knowledge

Deploy

ment

Entities/Edge/

Strength

(Dimension 8)

Effective cross-

sector

collaboration

Effective

communicat

ion

Stakeholder engagement

Communication gaps

Limited cross-

department

collaboration

Lack of

stakeholder

consensus

Page 54 of 56Journal of Modelling in Management

Journal of Modelling in Management

Effective cross-

sector

collaboration

0.738

(facilitates/

facilitated_b

0.624

(enhances/enhanced_by)

0.553

(hinders/hindered_by)

0.576

(mitigates/mitigated_b

0.576

(reduces/reduced_

by)

Effective

communication

0.600

(facilitates/ facilitated_by)

0.490

(hinders/hindered_by)

0.476

(mitigates/mitigated_b

0.584

(mitigates/mitigate

d_by)

Stakeholder

engagement

0.839

(hinders/hindered_by)

0.619

(reduces/reduced_by)

0.772

(reduces/reduced_

by)

Communicatio

n gaps

0.594

(leads_to/caused_by)

0.637

(leads_to/caused_b

Limited cross-

department

collaboration

0.707

(contributes_to/con

tributed_by)

Lack of

stakeholder

consensus

Entities/Edge/

Strength

(Dimension 9)

Employee

training

Effective AI

governance

Non-robust/adaptive systems

Poor change

management

Lack of adequate

employee training

Employee

training

0.566

(supports/su

pported_by)

0.840

(enhances/enhanced_by)

0.703

(mitigates/mitigated_by)

0.561

(resolves/resolved_by

)

Effective AI

governance

0.540

(identifies_and_improves/identifi

ed_and_improved))

0.481

(hinders/hindered_by)

0.362

(hinders/hindered_by)

Non-

robust/adaptive

systems

0.662

(results_from/leads_to)

0.524

(contributes_to/contri

buted_by)

Poor change

management

0.406

(results_from/leads_to

)

Lack of

adequate

employee

training

Entities/Edge/

Strength

(Dimension

10)

Regular AI

reviews

KPIs

reviews

Continuous feedback

Regular AI

reviews

0.586

(aligns_with

aligned_by_

0.723

(enhances/enhanced_by)

Post-

develop

ment

KPIs reviews

0.795

(informs/informed by)

Page 55 of 56 Journal of Modelling in Management

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Continuous

feedback

Entities/Edge/

Strength

(Dimension

11)

Concerns of AI

accountability

Inadequate

governance

Concerns of AI

accountability

0.646

(leads_to/ca

used_by)

0.496

(triggers/triggered_by)

Inadequate AI

governance

0.552

(exacerbates/ exacerbates_by)

issues

Page 56 of 56Journal of Modelling in Management

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