Key Success Factors for Organizational AI Project Success

Leveraging studies on artificial intelligence (AI) stakeholders and success factors, this article sets out to embed an AI perspective in a project management standard and center it around avoiding moral issues—harms, losses, and damages—in AI projects. The study provides an AI Project Framework that identifies the significant differences between AI projects and other information technology (IT) projects, including the AI development lifecycle, risks, issues, and challenges. The study creates a conceptual structure that combines aspects from the International Organization for Standardization (ISO) 21502:2020-12 Project Management standard and the AI project lifecycle. Finally, it weaves a practical framework of interdependencies and success factors for managing AI projects. The study uses an integrative literature review methodology that follows a hermeneutic framework. The study results should offer practical benefits to sponsoring organizations, project sponsors, and project managers in planning and governing AI projects.

Purpose Artificial intelligence (AI) is expected to disrupt international businesses processes. However, given the complexity and inherent risks associated with AI investments, it remains debatable whether such projects offer tangible benefits to firms. In the global discussion, this paper aims to add an European perspective for international businesses by analyzing the value effects of AI project announcements by European companies. Design/methodology/approach This study is based on an event study methodology to test for abnormal market returns around the announcement of AI projects by European companies. The market perception is further controlled for differences in AI projects like additional strategic partnerships, industry-fixed effects and success in implementation. Findings Contrary to expectations, this event study finds no significant abnormal market reactions to AI project announcements by European companies on general. Project-specific characteristics also do not appear to meaningfully influence investor perception. However, the authors observe clear industry-specific effects: the automotive sector exhibits a comparatively positive market response, while pharmaceutical and telecommunications firms also show above-average abnormal returns around the announcement date. These findings suggest that investors evaluate AI initiatives in Europe through a sector-specific lens, resulting in diverging market reactions. Overall, the results further challenge assumptions about the uniformly US-centric valuation of AI announcements. Practical implications The findings suggest that European managers should align AI announcements with clear, sector-specific innovation strategies to enhance credibility and investor confidence. However, to ensure broader societal benefit, AI strategies must be inclusive, avoiding the concentration of value in select sectors. These insights highlight the need for context-sensitive approaches to AI communication and implementation across Europe independent from the US role model. Social implications The findings highlight the heterogeneous interpretation of AI projects across sectors in Europe. The sector-specific nature of investor reactions suggests that firms must adopt tailored AI communication strategies that resonate with the expectations and realities of their respective industries. This underscores the broader societal importance of fostering public trust in AI, not just as a technological innovation, but as a tool for meaningful and inclusive social and economic progress. Originality/value This study contradicts the common understanding of market perception on AI projects from the USA and adds a European perspective, which indicates that the value of AI projects differentiates geographically.

Outline for a German Strategy for Artificial Intelligence

Artificial Intelligence (AI) has grown increasingly in the past decade. The growth and development bring up several issues for a successful AI project. The AI project requires communication across different domains, like specialists, engineers, data scientists, stakeholders, and ecosystem partners (analytic, storage, labeling, and open-source platforms). It offers numerous vital qualities to give deeper insights into user behavior and give recommendations based on the data. The AI project is hard to define, it requires more than mastery of data, and every enterprise needs guidance and a simple plan on how to use AI. This research creates a wide-view approach of different types of AI Model Canvas for companies that do projects, produce, promote and provide AI technology to organizations. We selected three canvases that represented AI, Machine Learning (ML), and Deep Learning (DL) method. We illustrate and interpret those canvas along with some case studies. We conclude our research by writing the final case report for each use case from the AI model canvas. By filling the one-page Canvas, it will help us explain what AI will provide, how it will interact with humans judgment, and how it will be used to influence decisions, how you will measure success & outcome, and the type of data needed to train, operate, and improve AI. The AI Model Canvas purposed a clear description and differentiation of the roles of stakeholders, customers, and AI strategy. This canvas also can be used in analytical and assembly projects in making new product lines.

Aim/purpose – This research presents a conceptual stakeholder accountability model for mapping the project actors to the conduct for which they should be held accountable in artificial intelligence (AI) projects. AI projects differ from other projects in important ways, including in their capacity to inflict harm and impact human and civil rights on a global scale. The in-project decisions are high stakes, and it is critical who decides the system’s features. Even well-designed AI systems can be deployed in ways that harm individuals, local communities, and society. Design/methodology/approach – The present study uses a systematic literature review, accountability theory, and AI success factors to elaborate on the relationships between AI project actors and stakeholders. The literature review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement process. Bovens’ accountability model and AI success factors are employed as a basis for the coding framework in the thematic analysis. The study uses a web-based survey to collect data from respondents in the United States and Germany employing statistical analysis to assess public opinion on AI fairness, sustainability, and accountability. Findings – The AI stakeholder accountability model specifies the complex relationships between 16 actors and 22 stakeholder forums using 78 AI success factors to define the conduct and the obligations and consequences that characterize those relationships. The survey analysis suggests that more than 80% of the public thinks AI development should be fair and sustainable, and it sees the government and development organizations as most accountable in this regard. There are some differences between the United States and Germany regarding fairness, sustainability, and accountability. Research implications/limitations – The results should benefit project managers and project sponsors in stakeholder identification and resource assignment. The definitions offer policy advisors insights for updating AI governance practices. The model presented here is conceptual and has not been validated using real-world projects. Originality/value/contribution – The study adds context-specific information on AI to the project management literature. It defines project actors as moral agents and provides a model for mapping the accountability of project actors to stakeholder expectations and system impacts. Keywords: accountability, artificial intelligence, algorithms, project management, ethics. JEL Classification: C33, M15, O3, O32, O33, Q55.

Recently, Python is the most-widely used language in artificial intelligence (AI) projects requiring huge amount of CPU and memory resources, and long execution time for training. For saving the project duration and making AI software systems more reliable, it is inevitable to handle exceptions appropriately at the code level. However, handling exceptions highly relies on developer’s experience. This is because, as an interpreter-based programming language, it does not force a developer to catch exceptions during development. In order to resolve this issue, we propose an approach to suggesting appropriate exceptions for the AI code segments during development after training exceptions from the existing handling statements in the AI projects. This approach learns the appropriate token units for the exception code and pretrains the embedding model to capture the semantic features of the code. Additionally, the attention mechanism learns to catch the salient features of the exception code. For evaluating our approach, we collected 32,771 AI projects using two popular AI frameworks (i.e. Pytorch and Tensorflow) and we obtained the 0.94 of Area under the Precision-Recall Curve (AUPRC) on average. Experimental results show that the proposed method can support the developer’s exception handling with better exception proposal performance than the compared models.

Assessing perspectives on artificial intelligence applications to gastroenterology

Recently, the government of United Arab of Emirates (UAE) is focusing on Artificial Intelligence (AI) strategy for future projects that will serve various sectors. Health care sector is one of the significant sectors they are focusing on and the planned (AI) projects of it is aiming to minimize chronic and early prediction of dangerous diseases affecting human beings. Nevertheless, project success depends on the adoption and acceptance by the physicians, nurses, decision makers and patients. The main purpose of this paper is to explore out the critical success factors assist in implementing artificial intelligence projects in the health sector. Besides, the founded gap for this topic was explored as there is no enough sharing of multiple success factors that assist in implementing artificial intelligence projects in the health sector precisely. A modified proposed model for this research was developed by using the extended TAM model and the most widely used factors. Data of this study was collected through survey from employees working in the health and IT sectors in UAE and total number of participants is 53 employees. The outcome of this questionnaire illustrated that managerial, organizational, operational and IT infrastructure factors have a positive impact on (AI) projects perceived ease of use and perceived usefulness.

This research presents a detailed case analysis of BGL Group, a leading, international, distributor of insurance and household financial services. The AI strategy is described by analysing and evaluating a set of AI applications covering a variety of business areas: 1. Machine learning for pricing; 2. Chatbot AI technology to improve the customer experience in e-service; 3. Customer experience design thinking and a/b testing in new product development; 4. Voice recognition and Natural Language Processing (NLP) in call centre operations; 5. AI techniques for market segmentation. Each application is described in detail, and the concept of value creation in service markets is illustrated using data flow diagrams of customer interactions for different stages of the customer journey. A benefits matrix model is proposed that captures the principal AI benefits to both the supplier and the customer. The case discussion uses a new model, an AI systems map, to describe and explain the overall landscape of current AI applications, traditional Management Information Systems (MIS) and possible future application areas based on broad AI strategies and cognitive AI/thinking machines. Some concluding remarks are made on the importance of a digital first culture, up-to-date digital infrastructure and technology partnerships for successful implementation of AI systems, the crucial role of big data in AI strategies, and the growing importance of AI ethics in business applications. Finally, some propositions are offered regarding the future direction of AI in insurance markets.

While organisations are increasingly interested in artificial intelligence (AI), many AI projects encounter significant issues or even fail. To gain a deeper understanding of the issues that arise during these projects and the practices that contribute to addressing them, we study the case of Consult, a North American AI consulting firm that helps organisations leverage the power of AI by providing custom solutions. The management of AI projects at Consult is a multi‐method approach that draws on elements from traditional project management, agile practices, and AI workflow practices. While the combination of these elements enables Consult to be effective in delivering AI projects to their customers, our analysis reveals that managing AI projects in this way draw upon three core logics, that is, commonly shared norms, values, and prescribed behaviours which influence actors' understanding of how work should be done. We identify that the simultaneous presence of these three logics—a traditional project management logic, an agile logic, and an AI workflow logic—gives rise to conflicts and issues in managing AI projects at Consult, and successfully managing these AI projects involves resolving conflicts that arise between them. From our case findings, we derive four strategies to help organisations better manage their AI projects.

PurposeThis concise review aims to explore the potential for the clinical implementation of artificial intelligence (AI) strategies for detecting glaucoma and monitoring glaucoma progression.MethodsNonsystematic literature review using the search combinations “Artificial Intelligence,” “Deep Learning,” “Machine Learning,” “Neural Networks,” “Bayesian Networks,” “Glaucoma Diagnosis,” and “Glaucoma Progression.” Information on sensitivity and specificity regarding glaucoma diagnosis and progression analysis as well as methodological details were extracted.ResultsNumerous AI strategies provide promising levels of specificity and sensitivity for structural (e.g. optical coherence tomography [OCT] imaging, fundus photography) and functional (visual field [VF] testing) test modalities used for the detection of glaucoma. Area under receiver operating curve (AROC) values of > 0.90 were achieved with every modality. Combining structural and functional inputs has been shown to even more improve the diagnostic ability. Regarding glaucoma progression, AI strategies can detect progression earlier than conventional methods or potentially from one single VF test.ConclusionsAI algorithms applied to fundus photographs for screening purposes may provide good results using a simple and widely accessible test. However, for patients who are likely to have glaucoma more sophisticated methods should be used including data from OCT and perimetry. Outputs may serve as an adjunct to assist clinical decision making, whereas also enhancing the efficiency, productivity, and quality of the delivery of glaucoma care. Patients with diagnosed glaucoma may benefit from future algorithms to evaluate their risk of progression. Challenges are yet to be overcome, including the external validity of AI strategies, a move from a “black box” toward “explainable AI,” and likely regulatory hurdles. However, it is clear that AI can enhance the role of specialist clinicians and will inevitably shape the future of the delivery of glaucoma care to the next generation.Translational RelevanceThe promising levels of diagnostic accuracy reported by AI strategies across the modalities used in clinical practice for glaucoma detection can pave the way for the development of reliable models appropriate for their translation into clinical practice. Future incorporation of AI into healthcare models may help address the current limitations of access and timely management of patients with glaucoma across the world.

This paper examines Artificial Intelligence (AI) implementation and evolution in the Brazilian judicial system from 2021 to 2023, focusing on institutional framework, governance, and applications. Analyzing data from the National Justice Council's (CNJ) AI project dashboards, we identified patterns in project development, survival rates, and categorical shifts in AI applications. Our findings reveal a 238% increase in AI projects, yet with a high mortality rate; over 65% were deprecated by 2023.The research indicates an evolution from basic data classification applications towards more sophisticated uses like procedural intelligence and user-focused services. While initial projects targeted efficiency, newer ones demonstrate transformative potential, including novel mediation and fraud detection. The study also assesses the regulatory framework (CNJ Resolution n. 615/2025) and its adaptation to emerging technologies like generative AI. Despite progress, challenges persist in the coordinated development and strategic implementation of AI systems. The paper concludes with recommendations for enhancing cross-court collaboration, establishing impact-focused metrics, and monitoring the new regulatory framework to ensure AI improves efficiency, transparency, and access to Justice, while considering risks like algorithmic bias, data quality, and accountability. These findings also provide valuable insights for other judicial systems undertaking similar transformations, highlighting the need for strong governance and strategic coordination for a successful AI integration.

Purpose This study aims to explore how generative AI enhances employee creativity and performance in international hotel marketing. It applies an integrated technology–organization–environment (TOE) and antecedents–behavior–consequences (ABC) framework to examine the role of technological competence, organizational support, government support and artificial intelligence (AI) strategy in fostering employee innovation and performance. Design/methodology/approach A mixed-method approach was adopted, combining survey data from 206 international hotel marketers with semi-structured interviews. The study uses partial least squares structural equation modeling to test relationships and fuzzy-set qualitative comparative analysis (fsQCA) to identify configurations leading to high employee performance. Findings Technological competence, organizational support and government support significantly influence AI-driven innovative behavior. Innovative behavior, in turn, enhances employee creativity and performance, with creativity acting as a mediator. AI strategy amplifies the impact of organizational support on employee innovation. The fsQCA results reveal multiple pathways to achieving high employee performance, demonstrating the multifaceted nature of AI-enabled outcomes. Practical implications Hotels can enhance employee innovation and performance by investing in AI training and aligning AI strategies with organizational support. Policymakers should promote AI-friendly policies and partnerships to foster adoption. Organizations can further benefit from integrating generative AI tools with workflows to boost creativity and service quality, enhancing competitive advantage in the hospitality sector. Originality/value This study contributes by integrating the TOE and ABC frameworks to explore the cognitive and behavioral mechanisms underpinning AI-driven performance. It introduces AI strategy as a boundary condition and offers new insights into the nuanced ways AI influences creativity and productivity in hospitality management.

Projects play a vital role in achieving organizational success, where artificial intelligence (AI) has a transforming impact in project management (PM). The integration of AI techniques into PM practices has the potential to significantly improve project success rates and enable more effective project management. This article adopted a systematic literature review (SLR) methodology, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and employing a content analysis strategy to review 61 peer-reviewed academic journal articles published between 2015 and 2025 in the Web of Science and Scopus. This study investigates the key project success dimensions influenced by AI throughout the project lifecycle, and identifies the AI sub-fields and algorithms employed in relation to project success, where time and cost are found to be the most significantly affected factors in project success. Machine learning (ML), along with its corresponding algorithms, emerged as the most frequently applied AI subfield. This study overviews key AI-influenced project success factors and the main AI subfields and algorithms in recent literature, providing actionable insights for diverse project stakeholders aiming to enhance outcomes through AI. Limitations, including the lack of industry or regional focus, exclusion of project management process groups, and omission of gray literature, were also acknowledged, which suggest valuable directions for future research.

This systematic review focused on evaluating the impact of the machine learning operations (MLOps) methodology on anomaly detection and the integration of artificial intelligence (AI) projects in computer auditing. Data collection was carried out by searching for articles in databases, such as Scopus and PubMed, covering the period from 2018 to 2024. The rigorous application of the preferred reporting items for systematic reviews and metaanalyses (PRISMA) methodology allowed 88 significant records to be selected from an initial set of 1,389, highlighting the completeness of the selection phase. Both quantitative and qualitative analysis of the data obtained revealed emerging trends in the research and provided key insights into the implementation of MLOps in AI projects, especially in response to increasing complexity, whereby the adoption of the MLOps methodology stands out as a crucial component to optimize anomaly detection and improve integration in the context of information technology auditing. This systematic approach not only consolidates current knowledge but also stands as an essential guide for researchers and practitioners, and the information derived from this systematic review provides valuable guidance for future practices and decisions at the intersection of AI and information technology auditing.