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 is developed.
Abstract
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.
With the growing integration of artificial intelligence (AI) into buyer–supplier negotiations, procurement teams must translate efficiency gains into defensible and appropriately calibrated reliance on AI-mediated decision support. This study develops and empirically evaluates a socio-technical trust-by-design model for AI-augmented procurement negotiation systems. It jointly considers perceived transparency/explainability (XAI), ethical governance visibility, and human-in-the-loop (HIL) relational design as conceptually distinct levers of AI system trust—that is, professionals’ willingness to rely on an AI negotiation system and their belief that it behaves competently and responsibly. AI system trust is treated as a focal proximal outcome, not as a proxy for, or a sufficient explanation of, dyadic buyer–supplier trust. Using an exploratory sequential mixed-methods approach—six multi-sector case studies across the EU and ASEAN regions (36 interviews) followed by a purposively recruited cross-sectional survey of 238 AI-exposed professionals analyzed via structural equation modeling (SEM)—we find that AI use is associated with higher perceived negotiation efficiency, but also with lower AI system trust when automation displaces relational cues and explanations are weak. XAI exhibits the strongest positive association with AI system trust, while HIL design and governance show additional positive associations. In an augmented specification, XAI and HIL statistically account for the AI use–trust association. A small inverse-U pattern further suggests that moderate, well-governed AI use is associated with higher trust than very low or very high automation intensity. Multi-group analyses indicate stronger XAI–trust associations in the EU and stronger HIL associations in ASEAN. These findings contribute to trust-in-AI and procurement research by (i) clarifying the boundary between trust in an AI system and trust in a buyer or supplier, (ii) specifying procurement-specific conditions—confidentiality, auditability, and negotiation tacticity—under which AI use can erode confidence in AI support, and (iii) offering a cautiously framed, evidence-consistent roadmap for trustworthy AI negotiation systems. The reported associations should be interpreted within a non-probability sample and do not establish population-representative effects or buyer–supplier relationship outcomes.
Raja Mejri, Sameh Skhiri· Frontiers in Artificial Inte...· 0 citations
This paper examines how Customer Relationship Management (CRM) is being transformed by Artificial Intelligence (AI) into a managerial instrument for agility in periods marked by economic uncertainty and digital disruption. In unstable markets, customers become more selective, less predictable and more exposed to digital alternatives, which makes customer retention and fast managerial interpretation increasingly important. The objective of the paper is to analyse how AI driven CRM supports managers in understanding customer behaviour, identifying customer related risks and responding more rapidly to market changes. The study is designed as a systematic literature review, structured according to the PRISMA logic. The review is based on academic articles selected from major scientific databases and complemented by one official contextual source on the digital economy. The selected studies are examined through thematic coding, with attention to customer intelligence, predictive decision making, personalization, customer retention, organizational agility and the ethical limits of automation. The results suggest that AI driven CRM creates value not only by automating customer management activities, but especially by improving the quality and speed of managerial decisions. However, the paper also argues that agility cannot be produced by technology alone. Data privacy, customer trust, algorithmic bias and excessive automation remain critical limits. The contribution of the paper consists in developing a structured synthesis that connects AI driven CRM with managerial agility and responsible customer management under challenging business conditions.
Erick-Nicolae Furduescu· Business Excellence and Mana...· 0 citations
This study examines the role of Artificial Intelligence (AI) in enhancing supply chain project management and operational performance in a dynamic business environment. As supply chains become increasingly complex, data-intensive, and disruption-prone, organizations are adopting AI-driven tools to improve forecasting accuracy, optimize inventory, streamline logistics, and strengthen decision-making. The purpose of this research is to assess the level of AI adoption, identify key application areas, and examine the relationship between AI familiarity and AI adoption while considering the broader roles of organizational readiness and governance mechanisms. A quantitative research design was employed using a structured questionnaire administered to 42 respondents, including supply chain professionals, project managers, data/AI analysts, students, and other business or technology-related participants. Data were analyzed using descriptive statistics, correlation analysis, and regression techniques. The findings indicate that approximately 57% of respondents reported current AI adoption within their organizations, while the mean AI familiarity score was 3.6 on a five-point scale, reflecting moderate awareness. Correlation analysis revealed a positive relationship between AI familiarity and AI adoption (r = 0.61), suggesting that increased knowledge supports adoption behavior. The results also highlight the perceived importance of AI training, organizational preparedness, and governance frameworks in maximizing implementation benefits. This study contributes to business analytics, operations management, and decision sciences by providing empirical insight into AI-enabled supply chain transformation. The findings offer practical implications for managers, policymakers, and industry stakeholders seeking to strengthen AI readiness, improve operational efficiency, and promote responsible AI adoption for sustainable supply chain excellence.
Denise Nalini, Dr. S.Barathi, Dr. Rubidhadevi· The Journal of Theoretical A...· 0 citations
Artificial intelligence (AI) is increasingly transforming manufacturing supply chains by enabling predictive analytics, intelligent automation, real-time visibility, data-driven decision-making, and adaptive coordination. Yet, the realization of sustained value from AI extends beyond technological adoption and depends on leadership capabilities that strategically align AI with organizational objectives, orchestrate technological and human resources, establish governance mechanisms, and reconfigure organizational capabilities. Despite the growing body of research on AI-enabled supply chains, the literature remains fragmented and largely technology-centric, with insufficient attention to the leadership mechanisms through which AI capabilities are translated into organizational and supply-chain outcomes, particularly in emerging-economy manufacturing contexts. This study addresses this gap through a two-stage design that integrates a systematic literature review of research published between 2011 and 30 May 2026 with exploratory empirical evidence from 60 senior managers across 15 Egyptian manufacturing firms. The review synthesizes research across key supply-chain functions to identify dominant themes, theoretical perspectives, methodological patterns, and unresolved research gaps, while the empirical inquiry examines AI maturity, leadership practices, and barriers to implementation and scaling. The findings reveal a persistent technology–leadership asymmetry, with limited scholarly attention to strategic AI leadership, governance, organizational readiness, resource orchestration, and human–AI collaboration. Empirical evidence further indicates heterogeneous levels of AI maturity, while legacy systems, fragmented data infrastructures, weak governance, organizational inertia, skills shortages, and leadership capability gaps constrain effective implementation. Integrating these insights, the study conceptualizes AI-driven leadership as a higher-order dynamic capability through which leaders sense AI-enabled opportunities and threats, seize them through strategic resource orchestration and governance, and reconfigure organizational and supply-chain capabilities to enhance intelligence, resilience, agility, and adaptability. The study advances AI and supply-chain scholarship by positioning leadership as a critical micro-foundation of AI-enabled transformation and extending the dynamic capabilities perspective to the emerging-economy manufacturing context.
A. Gomaa· Transnational Supply Chain R...· 0 citations
This paper develops a meta-framework for AI-enabled circular supply chain decision-making for sustainability, addressing the lack of an integrated, decision-oriented view across circular supply chain, governance, and AI literature. The study asks which managerial decisions are required across circular supply chain processes and how artificial intelligence can support them. A qualitative systematic literature review was conducted using Scopus, followed by AI-assisted conceptual synthesis. From an initial corpus of 6,718 records, a focused subset of 1,032 studies was derived using circularity, supply chain, and AI-related inclusion criteria. Deductive and inductive coding identified recurring constructs relating to process coverage, decision levels, AI roles, governance, capability prerequisites, and sustainability constraints. The results show that AI-supported circular supply chain decision-making is structured across five interdependent layers: strategic governance, tactical policy design, AI decision support, operational control, and foundational enablers. The framework also identifies a cross-layer governance spine centred on decision rights, auditability, and constraints on triple-bottom-line performance. The study concludes that AI should be positioned as a governed decision-support layer rather than an autonomous technical add-on, and that effective circular supply chain decision-making depends on data quality, interoperability, organisational capability, ecosystem coordination, and sustainability guardrails.
Rudolph Oosthuizen, Hannelie Nel· International Scientific Con...· 1 citation
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.
Gerald Schneikart, Walter Mayrhofer· Engineering review· 0 citations