A research framework for analysing the added value of generative AI in logistics organizations, with a focus on “difficult to automate” tasks and processes is presented.
Abstract
The adoption of artificial intelligence (AI) in organizations is often fuelled by promises of improved efficiency and innovation, while its practical value and operational impact remain unclear. This paper presents a research framework for analysing the added value of generative AI (GenAI) in logistics organizations, with a focus on “difficult to automate” tasks and processes. It combines literature-based model development, semi-structured expert interviews, field studies, and user-cantered experiments to examine how GenAI affects task performance, user behaviour, and organizational structures. It integrates qualitative insights with experimental and modelling approaches to support a systematic assessment of value creation, efficiency gains, usability, user acceptance, and organizational impact. Preliminary results from expert interviews indicate that GenAI is primarily used for cognitive support enabling time savings in manual tasks, and less for process automatization. Eventually, the presented framework will provide a structured basis for the development of evidence-based roadmaps for user-centred AI adoption in logistics.
The study evaluates how the implementation of AI-enabled strategies (i.e., augmentation and automation) influences product environmental and operational performance. We also explore if the positive impact of such AI-based strategies amplifies in products whose processes are characterized by high levels of operational flexibility and adaptiveness. To test the proposed hypotheses, we apply multilevel regression models to a unique dataset of 138 product lines from Costa Rican manufacturing and professional service firms in 2024. The core findings show that both augmentation- and automation-based AI strategies contribute to product performance, though through different mechanisms. 'AI-augmented process co-adaptation' improves the connection between process adaptiveness and operational performance by leveraging human-AI collaboration to support rapid and effective product reconfigurations, whereas 'AI-automated process co-adaptation' amplifies the positive effect of process adaptiveness and environmental performance, thus ensuring that eco-efficient routines are executed consistently across product processes. These findings underscore the importance of evaluating the role of AI technologies in product processes, depending on whether the strategic objective of such technologies emphasizes adaptability and responsiveness to customer demands or environmental outcomes. By focusing on the product line as the unit of analysis, this study contributes to both the AI strategy and environmental management literatures by showing how distinct AI-based strategic logics interact with products' adaptive capabilities to generate superior environmental and operational performance. From a practical perspective, the study offers guidance for managers on configuring AI-enabled strategies in ways that align operational flexibility, sustainability objectives, and product-level value creation.
Esteban Lafuente, J. C. Leiva, Ronald Mora-Esquivel et al.· Journal of Environmental Man...· 0 citations
Despite the growing organizational reliance on artificial intelligence (AI) systems, knowledge documentation (KD) practices in AI initiatives remain largely ad hoc, unstandardized, and disconnected from project lifecycle management. This study addressed this gap by proposing and validating the Knowledge Documentation Framework for AI Initiatives (KDF-AI), a twelve-component, five-phase, maturity-tiered framework synthesized through a literature review of peer-reviewed studies on KD practices in organizations implementing AI. Using Design Science Research (DSR) as the methodological paradigm, the study completed two iterative cycles: a literature-based synthesis that produced KDF-AI v1 and an expert evaluation cycle that resulted in the refined KDF-AI v2. Expert content validation was conducted with three domain validators representing academic, governance, and AI practitioner perspectives using a mixed-method approach that combined quantitative Content Validity Index (CVI) assessment with deductive thematic analysis of semi-structured interviews. The results showed that 57 of 66 items (86.4%) achieved universal inter-rater agreement, producing S-CVI/UA = 0.864 and S-CVI/Ave = 0.955, both exceeding the recommended threshold of 0.80. The nine items that did not meet the threshold consistently reflected issues of clarity rather than relevance, indicating strong conceptual acceptance of the framework while highlighting the need for more operationally specific articulation in several Advanced-tier components. Nine targeted revisions resulted in the development of KDF-AI v2. The study contributed: (1) a validated lifecycle-integrated KD framework for AI initiatives; (2) a taxonomy of ten systematically identified gaps in current AI KD practices; and (3) a methodological demonstration of mixed-method CVI validation for framework development in information systems research.
Fitria Handayani, Finannisa Zhafira, D. Sensuse et al.· Jurnal Impresi Indonesia· 0 citations
This paper describes the impact of automation and artificial intelligence (AI) on public procurement performance in Tanzania. Procurement has developed from a simple purchasing activity into a strategic function that enhances organisational performance; therefore, technologies such as AI and automation are increasingly transforming procurement by supporting informed decision-making, reducing operational costs, and improving supplier management. However, sustainable public procurement has received limited attention in relation to broader national policy objectives. The study employed a qualitative approach and a cross-sectional design, with a purposively selected sample of twenty participants. Interviews and focus group discussions were used to collect data, which were analysed thematically with direct quotations. The findings show that automation and AI are used in preparing and managing procurement documentation and simplifying technical language. Robust automation and AI also contribute to minimising human error, enhancing financial control, reducing opportunities for corruption, and improving operational efficiency and capacity building. The study concludes that public and private institutions are better positioned to use digitalisation, including robust automation and AI, in procurement systems to enhance the prudent and efficient use of financial resources in accordance with established regulatory and fiscal standards. It is recommended that institutions prioritise the transition from manual procurement processes to fully digitalised systems and provide comprehensive training for procurement and supply officers on the effective use of Robust Automation and AI systems. Policymakers should also develop clear regulatory frameworks governing automation and AI in procurement to ensure ethical use.
Godfrey Fabia Mbondo· Asian Journal of Education a...· 0 citations
The advent of Generative Artificial Intelligence (GenAI) is reshaping the landscape of project documentation and governance, offering benefits in automation, knowledge management, and decision support. But, the success of organizational adoption is not just about technology, it is also about collaboration between humans and AI, organizational governance, and socio-technical integration. This paper offers an exploratory literature review of the socio-technical issues involved in the adoption of GenAI tools in project documentation and governance. The relevant, peer-reviewed studies were identified in a structured search of the Scopus database, then screened with predetermined inclusion and exclusion criteria and assessed by means of a structured quality assessment framework. The selected studies were then analysed inductively to synthesise common challenges in implementation, prevailing research themes and conceptual links.
The review synthesizes the literature into three interdependent pillars of responsible use of GenAI: technological trust and transparency, Human-AI collaboration and organizational governance. GenAI has great promise in enhancing the efficiency of documentation processes, organizational knowledge management, and decision-making support, but, there are significant challenges related to the reliability of output, explainability, data quality, accountability, ethical governance, regulatory adherence, cybersecurity, and user trust. The study builds on these insights and suggests a socio-technical conceptual framework that integrates these aspects into a unified view to inform people's understanding of responsible Human-AI collaboration in project settings.
This work adds to the growing amount of literature by offering an interdisciplinary synthesis, bringing together technological, organizational, governance and human perspectives in a single conceptual framework. The results provide practical advice for project managers, organizational leaders, and policymakers aiming for the responsible implementation of GenAI and give direction for further empirical studies. Although the review was limited to peer-reviewed literature available in Scopus, the proposed framework offers a theoretically based grounding for empirical testing and refinement in a variety of organizational and industrial settings
Bela Lestari Dwireja, F. Abdalla, Yuhang Liu et al.· International journal of res...· 0 citations
Background: Artificial intelligence (AI) is increasingly discussed as a means of improving procurement efficiency and supply chain agility, yet its role in supplier evaluation remains insufficiently understood, particularly when decisions depend on fragmented information, cross-functional coordination, explainability, and managerial accountability. This study examines how AI may augment decision-making agility in supplier evaluation. Methods: An exploratory qualitative single-case study was conducted in a large multinational manufacturing company. Data were collected through 18 semi-structured interviews with procurement, logistics, quality, operations, and ERP/process actors, and analyzed through a Gioia-inspired thematic analysis, complemented by a descriptive assessment of theme recurrence. Results: The findings show that supplier evaluation is constrained by informational fragmentation, weak organizational memory, limited explainability, and the need to preserve contextual human judgement. AI was not perceived as a substitute for procurement professionals but as a decision-support infrastructure that may reconnect dispersed supplier knowledge, detect recurring problems earlier, and support transparent recommendations. Conclusions: The study develops a preliminary conceptualization of AI-augmented procurement agility as a bounded, process-level capability composed of AI-enabled supplier sensing, AI-supported interpretive integration, explainable decision support, and human-supervised responsiveness. The findings remain context-dependent and require further validation through comparative and longitudinal research.
The adoption of artificial intelligence (AI) offers significant opportunities to improve organizational performance. However, organizations must leverage its potential to enhance productivity without compromising employees' long-term well-being. This study addresses this challenge by developing a multi-criteria decision-making framework for evaluating six AI tools against eight sustainability-oriented criteria. Expert judgments provided by academic researchers from the Republic of Croatia were used to assess both the importance of the evaluation criteria and the performance of the AI tools in supporting sustainable organizational efficiency. Given the uncertainty and imprecision inherent in expert evaluations, an intuitionistic fuzzy framework was employed. The SWARA (Stepwise Weight Assessment Ratio Analysis) method was applied to determine the criteria weights, while the MABAC (Multi-Attributive Border Approximation Area Comparison) method was used to rank the AI tools. The results indicate that improving individual productivity and enhancing employee engagement and motivation are the most influential evaluation criteria. The findings further show that AI tools supporting employee learning and development, together with decision-support capabilities, represent the most suitable solutions for sustainable organizational efficiency. The robustness of the proposed framework was confirmed through comparative and sensitivity analyses. The study provides practical guidance for managers seeking to balance technological advancement with the long-term development and well-being of human resources.