Resilient by Design: Exploring the Social Abilities and Actor‐Network Roles of Artificial Intelligence in Supply Chain Management
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Condé, Lansiné; Münch, Christopher Article — Published Version Resilient by Design: Exploring the Social Abilities and Actor‐Network Roles of Artificial Intelligence in Supply Chain Management Journal of Business Logistics Provided in Cooperation with: John Wiley & Sons Suggested Citation: Condé, Lansiné; Münch, Christopher (2025) : Resilient by Design: Exploring the Social Abilities and Actor‐Network Roles of Artificial Intelligence in Supply Chain Management, Journal of Business Logistics, ISSN 2158-1592, Wiley, Hoboken, NJ, Vol. 46, Iss. 4, https://doi.org/10.1111/jbl.70032 This Version is available at: https://hdl.handle.net/10419/329777 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/
1 of 27 Journal of Business Logistics, 2025; 46:e70032 https://doi.org/10.1111/jbl.70032 Journal of Business Logistics ORIGINAL ARTICLE OPEN ACCESS Resilient by Design: Exploring the Social Abilities and ActorNetwork Roles of Artificial Intelligence in Supply Chain Management LansinéCondé | ChristopherMünch FriedrichAlexanderUniversität ErlangenNürnberg, Nuremberg,Germany Correspondence: Lansiné Condé ([email protected]) Received: 21 November 2024 | Revised: 20 May 2025 | Accepted: 30 June 2025 Funding: The authors received no specific funding for this work. Keywords: actor–network theory| artificial intelligence| autonomous actor| social abilities| supply chain resilience ABSTRACT Despite its substantial potential to enhance supply chain resilience (SCRes), artificial intelligence (AI) remains underexplored as an autonomous entity within supply chains (SCs), particularly in terms of its social capabilities and interactions within sociotechnical systems. Current literature has yet to address how AI can transform SC collaboration and network stability from a forwardlooking perspective, creating a critical research gap. This study aims to address this gap by examining the social abilities introduced by AI and their implications for SCRes. The exploratory research design of this study is grounded in the actornetwork theory and the Gioia method and includes 23 semistructured interviews with experts. The findings reveal that AI actively influences decisionmaking processes, power dynamics, and trust among SC actors. By interacting with both human and nonhuman actors, AI emerges as a critical tool for redefining collaboration and network stability. While AIdriven SCs can enhance organizations' efficiency and adaptability, they also introduce challenges, including ethical considerations, responsibility allocation, and unintended consequences of algorithmic decisionmaking. This study contributes to the discourse on the embeddedness of AI in SCs by offering theoretical insights into its sociotechnical integration and managerial implications for its governance. Understanding AI as an active tool is crucial for designing resilient and futureproof SCs. The findings emphasize the need for organizations to develop strategies that balance the benefits and risks of AI, ensuring its responsible and effective deployment across SC networks. 1 | Introduction Throughout the 21st century, disruptive digital technologies such as the Internet of Things, big data, blockchain, and artificial intelligence (AI) have emerged and gained significant attention as tools to enhance efficiency and performance in logistics and supply chain (SC) management (Wang et al. 2025). Min etal.(2019) highlight the potential of these technologies to optimize economic and environmental performance by supporting strategic decisionmaking to improve organizations' longterm responsiveness (Richey etal.2022). Despite these technological advancements, many organizations continue to face a knowledge gap regarding disruptive technologies, particularly AI (Birkstedt etal.2023; Martin and Parmar2022), which has only recently garnered significant attention. While large corporations have begun to explore AI's potential, its adoption in small and mediumsized enterprises This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2025 The Author(s). Journal of Business Logistics published by Wiley Periodicals LLC.
2 of 27 Journal of Business Logistics, 2025 remains scarce (Benbya etal.2020). This knowledge deficit has led to a lack of motivation and limited investment from management, hindering the broader adoption of AI to enhance resilience in logistics and SC management (Klumpp and Zijm2019) despite its recognized potential to positively impact the industry (Sanders etal.2019). Recent SC disruptions—such as the COVID19 pandemic, blockages in critical shipping lanes, and geopolitical events like Russia's invasion of Ukraine—underscore the urgent need for more resilient SCs (Pujawan and Bah2022; Swanson and Suzuki2020; Wieland2021). Due to the strong global integration of SCs, deglobalization and associated riskmitigation strategies remain economically unfeasible (Attinasi etal.2023). AI holds significant potential to mitigate SC risks and enhance resilience; however, research on its specific applications and implementations remains limited. Much of the literature focuses on past and current efforts, with a noticeable lack of forwardlooking perspectives (Zamani etal.2023). Despite the theoretical potential of AI to enhance SC resilience (SCRes; Belhadi etal.2022; Gupta etal.2024; Modgil, Gupta, etal.2022; Modgil, Singh, etal.2022; Naz etal.2022; Riahi etal.2021; Zamani etal.2023), actual implementation rates remain modest (Bui Trong and Bui Thi Kim2020; Deiva Ganesh and Kalpana 2022; Riahi et al. 2021), and organizations face challenges in deploying AIenabled SCs. AI is a relatively opaque concept for some of these organizations, which perceive it as a black box that entails operational complexities (Franzoni2023; Schlögl etal.2019). To address this issue, recent academic literature has explored AI from various perspectives and across diverse domains, offering strategic guidance for effective AI integration within SCs to foster resilience (Bechtsis etal.2022; Dey etal.2024; Moosavi etal.2022; Thürer etal.2020; Zamani etal.2023). To drive forwardlooking SC strategies, it is essential to make AI more accessible and easier to understand. This process begins with enhancing stakeholder familiarity with AI and articulating the transformative benefits of this disruptive technology (Hangl etal.2022; Hasija and Esper2022). The positive relationship between AI utilization in SC management and performance outcomes has been well established (Dash et al. 2019; Hallikas et al. 2021; Mohsen 2023; Riahi et al. 2021). Prior research has highlighted opportunities for AI to act as a support mechanism in logistics and SC management. This includes tasks such as demand forecasting, inventory management, logistics optimization, and risk assessment (Atwani et al. 2022; Brintrup 2020; Dash et al. 2019; Riahi etal.2021; R. Sharma, Shishodia, etal.2022), as well as enhancing decisionmaking processes (Niranjan etal.2021), facilitating realtime monitoring, and accelerating innovation cycles (Dash etal.2019). Baldea etal.(2025) propose an alternative research approach to extend this perspective by adopting an automationbased framework for autonomous SC behavior supported by AI. The potential applications of this framework include optimized datadriven decisionmaking, realtime access to relevant information, and enhanced risk mitigation throughout the SC (Calatayud etal.2019; Xu, Mak, Minaricova, etal.2024; Xu, Mak, Proselkov, etal.2024). However, the widespread implementation of these applications may be hindered by several challenges, including limited trust in the technology, insufficient infrastructure, a shortage of skilled professionals, and the absence of a welldefined social dimension within AI systems (Kar and Kushwaha2023; Kar etal.2021). To support the effective and sustainable integration of AI into SC management, the social dimensions introduced by this disruptive technology must be analyzed to anticipate and assess its potential impacts on the surrounding environment (Khakurel etal.2018; Rakowski and Kowaliková2024). This study contributes to the literature by addressing the research gap regarding the autonomous role of AI in enhancing SCRes, specifically examining the extent to which AI can use social abilities to bolster SC robustness. Additionally, the study aims to provide organizations with an informed outlook on anticipated changes associated with AI implementation across SC processes, including shifts in operational dynamics, interdependencies among stakeholders, and overall performance outcomes. It aims to support organizations' decisionmaking processes regarding the feasibility and potential return on AI adoption. Specifically, this study investigates the following research question: What social capabilities can AI introduce to enhance SCRes? An exploratory methodology was adopted to address this research question. Semistructured interviews were conducted with 23 experts specializing in SC management and AI. The actornetwork theory (ANT; Callon and Latour1981), supported by the Gioia method (GM; Gioia etal.2013), was employed to systematically examine the potential applications of AI within the SC. The application of ANT as a theoretical foundation for analyzing technological implementation in corporate contexts has been validated across numerous studies and fields (Hald and Spring2023). For example, it has supported the inclusion of information technology (IT) adoption in healthcare settings (Cresswell etal.2010; Greenhalgh and Stones2010), ITenabled sustainability initiatives (Bengtsson and Ågerfalk2011; Yurui etal.2021), geographic information systems in local administration (Walsham and Sahay 1999), financial technology developments (Lee etal.2015; Shim and Shin2016), smart city frameworks (Söderström etal.2014), and futureoriented innovations in agriculture (Berthet etal.2018). This study offers a dual contribution to academic research. First, it builds on prior research on AIdriven resilience by examining the role of AI within SCs, specifically focusing on six social dimensions introduced by AI during implementation. It bridges the existing research gap by presenting an exploratory approach to assessing the potential applications of AI across SC functions. Second, the study employs ANT as a methodological framework to investigate the dynamic relationship between AI and its organizational environment. This theoretical lens builds on the existing literature (e.g., Gams etal.2019; Horvitz2017) by examining humantechnology interactions and the integration of AI systems within broader environmental contexts. This approach is used to address the challenges commonly encountered
3 of 27 by managers in AI implementation, underscoring the benefits of AI for enhancing SCRes (Dietzmann and Duan2022; Sharma, Luthra, etal.2022). 2 | Theoretical Background 2.1 | Supply Chain Resilience The literature on SCRes dates back to the early 2000s (Blackhurst et al. 2011; Bode et al. 2011; Jüttner and Maklan 2011; Pettit et al. 2010; Rice and Caniato 2003; Sheffi2007). Subsequent research has expanded on this foundational understanding by incorporating concepts such as agility, collaboration, and adaptability (Ambulkar etal.2015; Chowdhury and Quaddus 2017; Melnyk et al. 2014; Safari etal.2024; Scholten and Schilder2015; Umar and Wilson2024; Zhao etal.2024). SCs should be designed to exhibit low susceptibility to disruptions and recover quickly and costeffectively. A lack of resilience in SCs can lead to significant financial losses, diminished stakeholder confidence, and imbalances between supply and demand, ultimately destabilizing production processes (Gupta etal.2021; Ivanov etal.2016; Pavlov etal.2019; Pettit etal.2019; Yoon et al. 2020). All SC disruptions share certain common characteristics: they arise from specific incidents, necessitate a rapid return to the initial state, and occur within a defined observation period (Wieland and Durach2021). Ivanov(2021a, 2021b) distinguishes between design for efficiency and design for resilience. The former approach emphasizes that SCs should prioritize responsiveness and efficiency, aiming to maximize profit and minimize waste through optimal resource utilization. In contrast, the latter approach emphasizes the importance of resilient SCs that can effectively mitigate unexpected and substantial disruptions, enabling a rapid return to the initial state or, ideally, an improved state. The literature identifies four distinct phases of SCRes: readiness, response, recovery, and growth strategies (Hohenstein etal.2015). The readiness phase involves preparing for disruptive events, primarily by identifying and monitoring changes within organizational boundaries (Fahimnia and Jabbarzadeh 2016; Maitlis and Sonenshein2010). The response phase focuses on executing preestablished strategies to mitigate disruptions (Stone and Rahimifard 2018). The recovery phase includes efforts to either rectify losses and return to a businessasusual state (BrandonJones etal.2014) or achieve the quickest possible transition to a desired future state (Fahimnia and Jabbarzadeh2016; Ivanov 2021a, 2021b). The final phase, growth strategies, involves the implementation, adoption, and refinement of lessons learned by organizations to enhance preparedness for future disruptions (Dennehy etal.2021; Hohenstein etal.2015; Yan etal.2023). 2.2 | Artificial Intelligence and Supply Chain Resilience in Supply Chain Management The term AI was first introduced by McCarthy et al. (1955) during the Dartmouth Summer Research Project. They envisioned machines capable of using language and solving problems traditionally associated with human intelligence (McCarthy etal.1955). Haenlein and Kaplan(2019) define AI as the ability of a system to acquire knowledge by analyzing data from the external environment and applying it to achieve specific goals and tasks. Marvin Minsky, founder of the MIT Artificial Intelligence Lab, has described AI as “the science of making machines do things that would require intelligence if done by men” (Minsky1968, v). These definitions encapsulate the core aspects of AI found in many other definitions in the literature (e.g., Barredo Arrieta etal.2020; Leake2001; Samoili etal.2020; Wang2019) and will guide the discussion of AI in this paper. Notably, there is no universally agreedupon definition of AI yet (Legg and Hutter2007; Nilsson2009). For the purpose of this study, the taxonomy of AI is adopted from Pournader etal.(2021), who categorize AI into three key domains: sensing and interacting (i.e., vision, speech recognition, and natural language processing), learning (i.e., machine learning), and decisionmaking (i.e., simulation and modeling, optimization, and planning and scheduling). This taxonomy aligns with the widely accepted definition of AI proposed by Kaplan and Haenlein(2019). The specific application of AI in SC management, particularly in enhancing SCRes, remains an underexplored area of research. This research gap is increasingly highlighted in the literature, with several authors calling for concrete insights into the role of AI in strengthening resilience (Akter etal.2022; Baryannis etal.2019; Kassa etal.2023; Pournader etal.2021; RodríguezEspíndola etal.2020). Prior research underscores the transformative role of AIdriven technologies, such as machine learning and predictive analytics, in improving the accuracy of forecasting demand, optimizing inventory management, and enhancing realtime SC visibility (Choi etal.2018; Riad etal.2024). These advancements enable organizations to anticipate, mitigate, and recover from disruptions with increased efficiency (Modgil, Singh, et al. 2022). Further, AI has been used to foster transparency, facilitate tailored solutions, and refine procurement strategies to minimize the impact of SC disturbances (Singh etal.2024). In recent years, applications in SC management have increasingly transitioned from automated systems to fully autonomous systems (Baldea etal.2025), reflecting advancements in AIdriven decisionmaking and operational capabilities (Xu etal.2021). Various terms have been used to describe independent operating systems within SCs, including “agents” (Boyko etal.2017; Cao etal.2009; Ch. Meyer2008), “autonomous SCs” (Xu, Mak, Minaricova, et al. 2024), and “selfthinking SCs” (Calatayud etal.2019). Despite differences in terminology, these concepts converge on the idea of intelligent technologies (e.g., AI) that operate autonomously within dynamic environments (e.g., SCs) to achieve specific objectives (e.g., SCRes). Autonomy has become increasingly significant due to the widespread adoption of Internet of Things technologies (Kumar etal.2023; Madushanki etal.2019; Tu2018). Nitsche etal.(2023) explain that while automation and autonomy share the fundamental characteristic of executing processes without
4 of 27 Journal of Business Logistics, 2025 direct human intervention, they differ fundamentally in their operational paradigms. The authors discuss how automated systems follow predefined rules established by human programmers, whereas autonomous systems exhibit the ability to act independently, adapt to novel situations, and deviate from their original programming when necessary (Wooldridge2002; Wooldridge and Jennings1995). This adaptive capability grants autonomous systems greater flexibility and selfmanagement potential than automated systems. To establish a unified theoretical foundation, this study employs the term “actor” to encapsulate these various concepts of autonomy. Actorbased models are widely utilized in domains such as financial technology (Pal etal.2023) and data mining (Cao etal.2009) to enhance efficiency and support decisionmaking processes. Such systems are particularly effective at managing complex, distributed structures under conditions of uncertainty (Boyko etal.2017). They often integrate AI, machine learning, and big data analytics to optimize performance. Current research continues to refine actorbased model intelligence, improve the ability of these models to interpret user needs, and enhance their capacity for social interaction (Pal etal.2023). Although AI has traditionally been examined from an autonomous and technical standpoint, every technical system inherently possesses a social dimension (Channell and Volti 1991; Gieryn etal.1994). This social aspect remains underexplored in the literature on autonomous SCs. This study examines AI from a social perspective, emphasizing its integration into existing networks as an independent actor that can enhance both societal impact and functionality. ANT provides a theoretical framework for analyzing the interactions between human and nonhuman actors. A detailed discussion of this approach is presented in Section2.3. 2.3 | Overview of the ActorNetwork Theory Organizations are intricate systems that require the alignment and engagement of all stakeholders to ensure the successful implementation of new technologies (Gallivan2001; Luo etal.2006; MateosGarcia2018). This study applies ANT as a theoretical framework to examine the multifaceted interactions between human and nonhuman actors throughout the technology implementation process (Latour 2005; Law and Hassard 1999). Grounded in a radical extension of the social constructivist approach typically used within science and technology studies, ANT emphasizes the influence of nonhuman actors in shaping social processes while challenging conventional distinctions between nature and society (Cresswell etal.2010). Highlighting the distributed nature of agency, it asserts that both human and nonhuman entities operate within interconnected networks (Callon1990). ANT functions as both a theoretical framework and a methodological tool and is built upon three core principles (Callon1984; Callon and Latour1981, 1992). The first principle is agnosticism. ANT encourages the adoption of a neutral stance toward sociological questions about the actors involved. It avoids favoring any one perspective over another or excluding potential interpretations. Observers are advised to refrain from fixing actors' identities until those identities are clarified definitively within the network. The second principle, free association, calls for observers to avoid imposing predefined categories or assumptions about the types and roles of actors. This allows for a flexible analysis free from traditional distinctions between natural and social phenomena. The third principle, generalized symmetry, requires the use of uniform language for describing and analyzing all processes, whether natural, social, or technical. This ensures consistency in the analysis of each type of actor or process within the network. Together, these three principles call for a redefinition of theoretical terminology, which is further discussed in the following paragraphs. 2.3.1 | Actors ANT adopts a broad definition of the term “actor,” extending it beyond human entities. While any entity can be considered an actor, ANT acknowledges that not all actors possess the same capacity to act (Callon and Latour1992). Consequently, this framework neither elevates nonhuman entities (e.g., objects, plants, animals) to the same status as humans nor reduces humans to the level of nonhuman entities (Callon and Latour1992; Latour1999). Instead, it challenges the anthropocentric assumption that humans are the sole initiators of action and highlights the active role that nonhuman entities play in shaping outcomes. Within the ANT framework, action is not the product of a single agent but the result of interactions among multiple entities. In the ANT framework, an actor is defined as an entity driven to act by numerous others (Latour 2005). This perspective highlights that an actor's ability to act is contingent upon the involvement of other entities, which may constrain, structure, influence, or modify one another; no single actor functions as the sole cause or autonomous subject of an action. Rather than categorizing entities into natural, technical, or social domains, the ANT framework treats all variables as actors, adhering to the principles of agnosticism, free association, and generalized symmetry. Since ANT does not prescribe guidelines regarding the number, identity, or configuration of actors, the relationships between actors must be derived by analyzing relevant literature, empirical descriptions, or case reports. Latour(2005) also introduces a distinction between actors and actants. Actors are protagonists explicitly defined by the researcher through descriptive figuration and possess specific characteristics, such as identity, form, and consistency. In contrast, actants are simpler, prefigured entities defined by their capacity to act, often excluding inert entities as uninteresting. The author rejects both objectivism and purely subjective interpretative approaches, instead advocating for an analysis that captures the complexity of actants, which include both human and nonhuman entities. This terminological distinction suggests that the same entity may be described differently in varying contexts, resulting in divergent interpretations of its role as an actor. This nuanced approach to defining actors and actants underscores the ANT framework's emphasis on capturing the complexity and plurality of interactions within a network, eschewing rigid hierarchies or predetermined classifications.
5 of 27 2.3.2 | Networks Actors in the ANT framework do not function autonomously but operate within actornetworks, which consist of interconnected actors. In this context, the term “network” is a theoretical construct representing the diverse linkages, connections, and relationships among actors. ANT employs a distinct definition of networks that diverges from traditional interpretations. Latour(2005) and Callon(1990) critique three traditional definitions, arguing that networks are not (a) merely technical connections (e.g., wires, trails, tubes); (b) purely intrasocial relationships; or (c) postmodern metaphors for sociotechnical relationships. Instead, ANT defines networks as dynamic assemblages of human and nonhuman entities that emerge through interactions. These interactions define the competencies, characteristics, functions, and roles of all participants. Thus, network formation involves two interrelated analytical processes: (a) the creation or modification of connections between actors and (b) the formation or reconfiguration of the actors themselves. This interplay underscores the mutual dependency between actors and networks. Actors are not independent of networks, nor do they hold inherent precedence over them. Rather than possessing fixed characteristics, intrinsic qualities, or autonomous capabilities, actors derive their identities and actions from their relationships with other actors. As Law(2008) notes, without networks, actors lack both identity and the potential for action. Conversely, networks cannot exist without actors, as their purpose is to reveal and allocate potential for actions to specific actors. Within the ANT framework, every actor is also an actornetwork, and every network can function as an actor within other networks. The concept of translation can be used to describe the process of network formation in ANT. Through the process of translation, the identities, programs, and competencies of actors are negotiated, transformed, and assigned (Callon1980; Law2008). Callon (1984) identifies four key interdependent steps in the translation process: (a) “problematization”, where the researcher identifies the problem and defines the potentially affected actors (206); (b) “interessement”, where various strategies are employed to align the actors' interests with the problem and proposed solutions (207); (c) “enrolment”, where actors decide whether to accept the proposed roles and instructions for action (211), with efforts being focused on increasing actor acceptance and minimizing resistance; and (d) “mobilization”, which involves actors' agreement and role acceptance if translation is successful, culminating in a stable network that defines the identities, capabilities, and roles of the actors in a binding manner (216). However, this stability is not permanent and can be dissolved or reconfigured at any time in response to new interaction or disruptions. 2.4 | ANT in Technological Research Technology occupies a pivotal role in ANT due to its potential to reorient the framework through analysis or practical engagement. ANT redefines technology as an actor with distinct capacities for action rather than as a tool wielded by humans (Callon 1980). A fundamental assumption of the ANT framework is that technological tools act in conjunction with other actors, both human and nonhuman. Thus, actions within an actornetwork are not confined to specific locations but are distributed, displaced, and often dislocated (Latour2005). The agency of technology within networks is often overlooked, as it tends to become invisible when operating smoothly. The ANT framework describes this process of stabilizing, fixing, and integrating technology into routine practices as black boxing. A black box is conceptualized as an entity that is assigned explicit role specifications and predefined processes, encompassing all elements that have become objects of indifference due to their successful integration. The more elements—such as objects, methodologies, or conceptual frameworks—that are consolidated within a black box, the greater its utility as a foundation for further development (Callon and Latour 1981). However, black boxes are not immutable; their roles and functions can change, and technological tools can fail or break. The process of black boxing establishes a binding relationship among actors, thereby stabilizing these connections and facilitating translation. By closing black boxes, networks achieve a degree of consistency, resulting in the mechanization—i.e., the stabilization and institutionalization—of the network (Callon 1990). This mechanization enhances the predictability and calculability of interactions within the network, strengthening its overall resilience and coherence (Callon1990). 3 | Research Methodology 3.1 | Rationale for the Exploratory Research Design Given the nascent nature of AI in leveraging SCRes, the relatively underexplored research domain, and the limited number of experts with knowledge of both SC management and AI, this study adopts a qualitative, exploratory research design (HladyRispal and JouisonLaffitte2014). The methodological approach is based on the GM, proposed by Gioia et al. (2013), which extends grounded theory and is particularly wellsuited for inductive research. GM provides a systematic framework for developing new concepts while ensuring methodological rigor and preserving the creative potential inherent in qualitative inquiry. Its multistep structure supports transparent data collection and analysis while facilitating theoretical development, making it wellaligned with the objectives of this study. This methodological approach has been previously applied in research on AI and SCRes (e.g., Aarland 2024; Koponen etal.2023; Musick etal.2021; Russo2024) and offers a framework that bridges macroand microanalyses (von Soest2023). However, our systematic review of Scopus, Google Scholar, and ScienceDirect reveals the lack of studies examining the application of AI to enhance SCRes, particularly within the GM and ANT frameworks. Data for this study were primarily collected through semistructured expert interviews. This method provides a structured yet flexible framework that enables deeper exploration of critical areas of inquiry through followup and clarification
6 of 27 Journal of Business Logistics, 2025 questions as needed (Adams 2015). To ensure rigor in data collection and analysis, the interviews were developed and conducted in accordance with established guidelines (Kallio etal.2016), while integrating an analytical framework based on the GM (Gioia etal.2013). This methodological alignment ensured a systematic and iterative research process, facilitating the identification of meaningful insights while maintaining transparency and reliability. The research design also allowed for a focused examination of key themes while remaining flexible enough to prioritize areas requiring further depth (Yin2009). 3.2 | Preparation Phase and Data Collection This study employed a systematic approach to ensure the precise sampling and collection of data relevant to the research focus (Eisenhardt and Graebner 2007), as illustrated in Figure 1. It adhered to a systematic approach following the methodological framework proposed by Kallio etal.(2016), who delineate a multistep process for developing guidelines for the semistructured interviews. The process began with the identification of prerequisites to ensure the methodological appropriateness of the interviews. The second phase focused on acquiring the requisite expertise to conduct interviews with subjectmatter experts. This was achieved through consultations with experts in the field and an extensive review of relevant literature. Building on this foundation, the third phase involved developing a wellstructured and clearly formulated interview guide designed to elicit comprehensive and insightful responses from experts. The development of the interview questions drew on openended formats commonly used in the literature. Flexibility in the sequence of questions was maintained to allow interviewers to adapt to the natural flow of conversation while ensuring coherence throughout the interview process. The fourth phase comprised two pilot tests of the interview guide to assess its clarity and comprehensibility from an external perspective. Based on feedback, adjustments were made to two questions to improve their phrasing and precision and reduce ambiguity. Expert selection was guided by clearly defined criteria. Participants were required to hold leadership roles in SC FIGURE 1 | Visualization of the research methodology procedure.
7 of 27 operations or related fields and possess extensive knowledge of AI applications in the context of SC management. A minimum of five years of seniority, either in research or industry, was also a prerequisite. To ensure a consistent regional perspective and minimize potential biases arising from regional differences in technological maturity, cultural factors, or economic conditions, only experts from Europe were interviewed (Richey etal.2016). A total of 190 potential experts were contacted via LinkedIn, yielding 41 responses (21.6%) and resulting in 23 completed interviews (12.1%) conducted between July and September 2024. Interviews continued until theoretical saturation was reached, implying that no new significant insights emerged from additional interviews (Corbin and Strauss1990). Table1 presents an overview of the interviewees, including their professional backgrounds and experience levels. The interview guideline was organized into two sections. The first section focused on general background information, such as the participants' professional roles and a selfassessment of their expertise in AI. The second section explored AI implementation in SCs, including perceived challenges, barriers, and crisis management strategies. The guideline was iteratively refined based on feedback from two pilot interviews, leading to adjustments in question phrasing to enhance clarity and precision. TABLE 1 | Overview of the interviewees. Expert Academic degreeaCurrent role Function Years of experience Knowledge of AIb E01 Master's degree Consultant, Supply Chain External consultant 20 7 E02 Master's degree Head of Purchasing Head of department 19 7 E03 MBA CEO —19 8 E04 N/A Head of Customer Order Management Head of department 27 8.5 E05 Bachelor's degree Head of Procurement and Logistics Head of department 19 6.5 E06 Master's degree Managing Director Data scientist 20 10 E07 Master's degree Senior Consultant External consultant 6 7 E08 Ph.D. Expert Supply Chain Innovation Internal consultant 8 7 E09 Ph.D. Business Analytics and Optimization Consultant External consultant 25 10 E10 Ph.D. Business Unit Manager Data scientist 21 6.5 E11 Master's degree Consultant, AI & Supply Chain External consultant 24 9 E12 Master's degree CEO —22 8.5 E13 Ph.D. Professor Supply Chain Management —21 7.5 E14 Ph.D. Business Development Manager Sales 24 7 E15 Apprenticeship Head of Purchasing Head of department 14 6.5 E16 Master's degree Managing Director Project manager 29 6.5 E17 Master's degree Director, Logistics Strategy and Transformation Head of department 28 8 E18 Ph.D. Head of Purchasing Head of department 26 3.5 E19 Ph.D. Group Leader, Data Science Head of department 86.5 E20 Master's degree Director, Innovation, Digitalization, & AI Research and development 24 5 E21 Ph.D. Head of Sales Head of department 16 8.5 E22 Apprenticeship Consultant, Procurement Strategy External consultant 38 8 E23 Ph.D. Customer Success Lead Production process 11 5 aMaster's degree includes the German university degrees “DiplomIngenieur” and “DiplomBetriebswirt.” bThe scale ranged from 1 (basic knowledge only) to 10 (expert). The category was based on the subjective perception of the interviewee. If the answer fell between two numbers, a halfstep was used.
8 of 27 Journal of Business Logistics, 2025 A total of 1016 min of interview data were recorded, with an average interview duration of 44 min, resulting in 380 pages of transcription. One interview was conducted via telephone, while the remaining 22 were held on Zoom or Microsoft Teams. All interviews were audiorecorded and transcribed verbatim to ensure data integrity and accuracy. Participants were assured complete anonymity throughout the research process. 3.3 | Data Analysis Data analysis followed a structured coding process inspired by the GM (Gioia etal.2013) and qualitative content analysis, supported by the MAXQDA software. In the first phase, firstorder concepts were identified directly from the data (Van Maanen1998). Each response was initially treated as a distinct category, preserving the respondents' original language to ensure empirical grounding. This process was independently conducted by two researchers, who then collaboratively refined the coding scheme through discussion and iterative adjustments until consensus was reached (Beth Harry et al. 2005). Given the emphasis on interpretative alignment rather than statistical agreement, intercoder reliability was not quantified (Saldana2015), which is a common practice in qualitative studies (e.g., Münch etal.2022; Pauli etal.2025). In the second phase, the firstorder concepts were consolidated into secondorder themes by abstracting and synthesizing key insights. The researchers adopted the perspective of knowledgeable agents, linking expert statements with theoretical constructs and broader conceptual frameworks. This iterative approach involved continuous interaction between the empirical data and relevant literature to ensure that emerging themes were theoretically informed. Theoretical sampling was applied to constantly refine categories and concepts as new data became available (Glaser and Strauss1970). Once established, the secondorder themes were further aggregated into higherlevel dimensions—referred to in this study as social abilities—which summarize the key theoretical insights derived from the data (Gioia etal.2013). Tables2 and 3 present exemplary results of the data analysis process, including firstorder concepts, secondorder themes, and aggregate dimensions. The final phase of analysis involved the construction of a data structure to visually map the transformation of raw interview data into abstract theoretical dimensions. This structured visualization enhanced the transparency and rigor of the analytic process (Pratt 2008; Tracy 2010). Conceptual diagrams were developed to illustrate the relationships among categories using boxes and arrows, which facilitated an intuitive understanding of the findings (Nag etal.2007). To enhance the credibility of the findings, data triangulation was performed by integrating multiple sources and verification methods. All experts were asked a consistent set of questions, aligning with the principles of synchronic primary data source triangulation (Pauwels and Matthyssens 2004). Additionally, secondary data—including industry reports, company records, and archival documents—were analyzed to crossvalidate the insights derived from the interviews (Denzin 2017). This approach ensured that the identified TABLE 2 | Exemplary results of data analysis. Interview excerpt Firstorder concepts ID of the concept “And you just have to have the right people, the right employees, who, let's say, trust the whole thing to start with, who are open to new ideas, and who say, ‘Okay, we'll test the whole thing and see what comes of it’” (E16) Openmindedness c2 “That is one of those basic attitudes. I don't think it has much to do with age but simply with the question: Am I openminded and open to things or not? And I wouldn't see that in demographic or age terms, but simply as a personal attitude” (E10) “But it is a fact that openness is not a question of age. No, it isn't. And it is more a question of what you have experienced […]. And then it is the case that you can very quickly enjoy rediscovering things for yourself” (E14) “Capturing the texts, feeding them back, analyzing them in terms of content, applying machine learning, utilizing data from other sources, then reconnecting them, and finally delivering the results on time” (E12) Integration into/exchange with existing systems c38 “I would say, primarily, integration. […]. All the operational processes, meaning planning within the supply chain, are embedded in these systems, and then planning has to somehow integrate with them, pulling and feeding data back into the system” (E11) “It definitely needs to be integrated into the existing system landscape. It cannot be a standalone solution. Everything I generate from my eprocurement, from the SRM tool, or from any other system must be accessible through the tool, regardless of the purpose behind it. But fundamentally, it should be open to importing data via interfaces” (E05)
15 of 27 The social ability of neutrality directly influences vision, particularly by mitigating siloed thinking across departments and promoting a holistic, datadriven view of the SC. This broad perspective supports methodological rigor and enhances flexibility within the SC while minimizing bias, thereby enabling more agile responses to evolving challenges. Additionally, a neutral stance enhances system reliability by fostering independent decisionmaking, which ensures consistency and objectivity. The ability of vision requires a fundamental degree of flexibility to accommodate potential future developments. Effective scenario planning depends on the capacity of AI to integrate and respond to new information in real time. Therefore, longterm adaptability requires continuous knowledge acquisition and skill enhancement. Advanced AI facilitates this by enabling the development of strategic alternatives and refining them in response to changing conditions. Through predictive decisionmaking, AI can preemptively identify potential risks, allowing the system to prepare accordingly and implement proactive measures to counter them. This approach strengthens trust in the system and enhances its overall reliability. However, the interplay between the social skills of flexibility and reliability has inherent constraints. While rapid responsiveness is an essential skill for AI, it must align with compliance standards and maintain appropriate communicative integrity to safeguard the system's credibility. Further, flexibility is intrinsically linked with adaptability, as continuous system evolution and iterative knowledge exchange are necessary to sustain the SC's responsiveness to environmental fluctuations. Conversely, adaptability reinforces flexibility because an AIdriven system that is capable of continuous learning and realtime data integration is inherently better equipped to navigate complex and unpredictable scenarios than other systems. The system's high level of intelligence facilitates the efficient acquisition and evaluation of information, as well as effective responses to novel situations. From a reliability perspective, newly assimilated information and subsequent system adjustments must consistently adhere to established regulatory and operational frameworks. Stability and trust must remain uncompromised even amid continuous change. Ultimately, a reliable AI system is one that meets initial user expectations and is grounded in trust and regulatory compliance. Neutral decisionmaking is paramount, as a system devoid of internal biases and corporate conflicts enhances its credibility. Reliability is further reinforced through predictive planning, which includes identifying potential vulnerabilities of the system and implementing proactive measures. The symbiotic relationship between flexibility, adaptability, and reliability enables swift and effective responses to changing circumstances while preserving confidence in the system's integrity. 5 | Discussion and Conclusions Organizations often perceive the integration of autonomous systems into SC operations as a complex challenge. This is primarily due to the limited research on social implications, with attention typically focused on technical implementation. This study addresses this gap by examining the potential of AI in SCs from a social perspective, with a specific focus on its role in enhancing resilience and capacity for independent operation. Based on an exploratory research design, this study uses semistructured interviews with 23 subjectmatter experts specializing in SC management and AI. This qualitative methodological approach enabled an indepth exploration of the research gap, allowing for a nuanced understanding of AI's role through comprehensive yet flexible discussions that incorporated individual insights and expertise. The analysis is grounded in ANT, which provides a robust framework for analyzing the dynamic interactions between technology and its operational environment. ANT facilitates an examination of the interconnected and reciprocal roles of both internal and external components of an SC. Within this framework, AI is conceptualized as an integrated and autonomous actor capable of reshaping organizational networks. This theoretical perspective underscores AI's systemic influence when embedded as a functional actor within corporate structures, reinforcing its potential to enhance SCRes. 5.1 | Theoretical Contributions The contributions of this study to the academic discourse on the application of AI in SC management are twofold. The first contribution is the identification of six social abilities inherent to AI that emerge when it is integrated into an established network, such as an SC. These abilities were derived from expert insights and mapped against the four phases traditionally associated with SCRes—readiness, response, recovery, and growth. The findings reveal a significant alignment between the identified attributes of AI and these phases. Specifically, the ability referred to as vision in our study corresponds to the readiness phase, while flexibility and adaptability align with the response and growth phases, respectively. Figure3 illustrates this alignment by comparing the established phases of resilient SCs (Hohenstein etal.2015) with the six social abilities identified in this study as pivotal to enhancing SCRes. The first ability, vision, reflects AI's forwardlooking capacity. While this does not imply that AI can predict the future with absolute precision, it does enable the identification of the most probable scenarios and the preparation of multiple contingencies in advance. This proactive approach aligns with prior research on foresight in SC management (Dubey et al. 2021; Gruchmann etal.2024). By anticipating a range of possibilities, AI significantly reduces response times to unforeseen disruptions, thereby enhancing SCRes. Even when AI operates in a supporting capacity rather than as a fully autonomous actor, its predictive capabilities equip SC managers with a spectrum of decisionmaking options, ultimately bolstering their ability to effectively address unexpected challenges. The second ability identified in this study, flexibility, closely corresponds to the response phase of SCRes, as frequently emphasized in the literature. This ability underscores the critical need for swift and effective reactions to unforeseen events based on
16 of 27 Journal of Business Logistics, 2025 the most uptodate information available (Gaudenzi etal.2023). AI plays a key role in achieving this responsiveness and optimal flexibility, surpassing traditional decisionsupport systems in terms of both processing power and speed. For instance, during the COVID19 pandemic, AI was instrumental in coordinating stakeholders, enhancing realtime information processing, and enabling the rapid reconfiguration of distribution channels despite material shortages and logistical delays (Guida etal.2023; Modgil, Gupta, etal.2022; Modgil, Singh, etal.2022). This case underscores the vital role of AI in supporting SCRes by enabling realtime decisionmaking and effective crisis response. Third, the adaptability skill underscores AI's ability to engage in continuous learning through ongoing information exchange, aligning with Bhattacharya's(2021) findings. This concept resonates with Megginson's (Megginson 1963) interpretation of Charles Darwin's Origin of Species: […] it is not the most intellectual of the species that survives; it is not the strongest that survives; but the species that survives is the one that is able best to adapt and adjust to the changing environment in which it finds itself. (4) For AI to effectively adapt to evolving external conditions, it must constantly assimilate new data and remain regularly updated with current information. These continual updates reinforce the foundational structure of AI systems and enable the generation of increasingly tailored and effective solutions. The majority of experts interviewed in this study highlighted the critical importance of this continuous feedback loop, noting that AI's adaptability significantly enhances organizational capabilities by learning from external conditions, reducing system complexity, and strengthening resilience. These findings are consistent with those of Belhadi etal.(2022), who emphasize the transformative potential of AI adaptability in promoting organizational robustness and agility. Building on this foundation, this study extends the literature by introducing the additional abilities of inevitability, neutrality, and reliability, offering novel perspectives on the application of AI within SCs. The ability of reliability, in particular, focuses on the interaction between AI and internal corporate systems, as well as its compliance with broader legal frameworks. A major development in this area occurred in July 2024, when the European Union adopted the first transnational AI legislative package (Regulation (EU) 2024/1689 of the European Parliament and of the Council2024). This legislation introduces a riskbased approach to AI governance and mandates transparency measures, such as the explicit labeling of AIgenerated content (Gasser2023). While the EU has taken the lead in AI regulation, many countries still lack comprehensive legal frameworks (Sofinska2024). However, globally harmonized legislation is essential to mitigate the risks of AI misuse, as fragmented governance may lead to operational and security challenges (Robles Carrillo2020). Although the G7 countries have agreed upon a shared code of conduct for AI governance, comprehensive international legislation remains in development (Organisation for Economic Cooperation and Development2024). In the absence of such regulation, AI systems remain vulnerable to risks such as security breaches, cyberattacks, and unsafe operational behaviors (Jazairy etal.2024; Puşcã2020). The identified skill of reliability also involves the seamless integration of AI into an organization's existing IT infrastructure, a critical factor that influences AI's impact on organizational performance (Themistocleous etal.2001). While the integration of AI offers significant potential benefits, it also requires careful attention to both technical and economic considerations, including compliance with legal and jurisdictional FIGURE 3 | Comparison of the four phases of SCRes, based on Hohenstein etal.(2015), and the social abilities identified in this study.
17 of 27 frameworks. By adhering to robust legal frameworks, organizations can minimize the risk of disruptions caused by noncompliance, thereby strengthening the resilience of their SCs. This ability underscores the importance of reliable integration and legal safeguards as prerequisites for AI's sustainable and effective contribution to SCRes (Abousaber and Abdalla2023). The ability of neutrality reflects the absence of human sensitivities in AIdriven decisionmaking. Unlike human actors, AI systems operate independently of interpersonal dynamics or departmental biases, maintaining a consistent focus on the overarching goal of achieving a resilient SC. Groupbased decisions are often more impartial than those made individually (Keck etal.2012). In this context, AIdriven decisions can be conceptualized as an individual group decision, given AI's capacity to evaluate information across multiple SC domains from a holistic perspective. Recent research raises concerns about AI not being entirely neutral in decisionmaking and possibly perpetuating discriminatory outcomes or existing biases; however, such issues generally arise when AI algorithms are trained on biased data (Stinson2022). This limitation is less relevant in SC applications, as the data used are typically specific, objective, and operationally focused, minimizing the potential for bias. The ability of inevitability underscores the unavoidable necessity of integrating AI into SC operations. As mentioned by multiple interviewees, successful implementation requires substantial educational efforts. These efforts ensure that managers and decisionmakers are informed and equipped to advocate for AI integration, while also preparing employees and end users to understand its capabilities and limitations. The absence of such educational initiatives could hinder the adoption of AI and reduce organizational readiness. According to the experts, organizations that fail to comprehensively incorporate AI into their SCs may risk falling behind competitors, thus reinforcing the imperative of this technological transition. The second contribution of this study to the literature lies in its conceptualization of AI as an autonomous actor within the SC. Drawing upon ANT as the guiding theoretical framework, this study highlights AI's ability to act and influence network dynamics. Building on the six social abilities of AI identified in this research, it is evident that AI can be understood as an autonomous actor (Calatayud etal.2019; Xu, Mak, Minaricova, etal.2024; Xu, Mak, Proselkov, etal.2024) that integrates social abilities into the surrounding network. This integration underscores AI's ability to operate independently, adapting its functions to specific operational needs within an organization. Although AI is conceptualized as an independent actor, it simultaneously assumes multiple roles by functioning as an actor, protagonist, and actant within the SC ecosystem. However, in contrast to Latour's (2005) original conception of actors, AI lacks a tangible physical form, a stable identity, or consistent attributes, challenging traditional definitions of agency. This perspective extends beyond traditional understanding and emphasizes the interpersonal dynamics between AI and human counterparts, raising the key question: To what extent will humans adapt to AI as an increasingly familiar presence in their daily professional environments? As Latour (1992, 254) observes, “It is too full of humans to look like the technology of old, but it is too full of nonhumans to look like the social theory of the past.” This statement conveys the view that humans and nonhumans operate as interconnected actors within a network, rejecting a strict distinction between entities such as technology and humans. The increasing integration of AI as an actor within SCs necessitates targeted efforts in education and technological training (Brockhaus et al. 2023) to prepare individuals for seamless collaboration. Addressing concerns such as job displacement (Cropley et al. 2022; McClure 2018) and the opacity of AIdriven decisionmaking processes (e.g., black box systems) is essential (Ajunwa2020). Prior research demonstrates that social attributes are often ascribed to technological systems (Nass etal.1994; Takeuchi and Naito1995), a phenomenon equally applicable to AI (Nass etal.1994). As Norman(1994, 71) states, it is important to understand that “once unleashed, technologies do not disappear,” a statement reaffirmed by contemporary research (Jöhnk etal.2021; Nokelainen etal.2012). This observation indicates that disruptive technologies persist beyond their initial breakthrough, as they are progressively integrated into society by an increasing number of users. This argument underscores the need for proactive preparation to ensure that AI is perceived as an accessible and integrated element of human– machine interaction rather than a disruptive force. As Klien etal.(2004) note, collaboration between humans and AI presents challenges, yet many of these obstacles can be mitigated by reducing human mediation and enabling direct communication between AI systems. Operational efficiency within SCs can be significantly improved by minimizing the need for AI to interpret human intentions (Abbass2019). The findings of this study suggest that AI should assume a greater share of decisionmaking and operational tasks within SCs, thereby streamlining processes and reducing errors at the human–technology interface. Within the ANT perspective, AI is not only an actor but also a network. By deconstructing AI and analyzing the network of relationships surrounding its implementation, it emerges as a complex, hybrid association that encompasses and integrates natural, technical, and human elements within specific applications. Each implementation of AI requires thorough investigation to fully understand its impact on SC performance. In addition to acting as an autonomous actor within the broader theoretical framework, AI also operates as part of an evolving, interconnected network shaped by both internal and external influences. The dual conceptualization of AI as both an independent agent and an integrated network illustrates its profound and multifaceted impact on modern SC management. The second foundational concept of our theory is the notion of the network. Based on the identified ability of reliability— which encompasses attributes such as system trust, compliance with surrounding systems, and etiquette—we propose that AI can be conceptualized not only as an individual actor but also as a network within the SC. Specifically, AI can be conceptualized as an autonomous network comprising social abilities, software, hardware, IT infrastructure, enterprise resource planning systems, and other interconnected elements.
18 of 27 Journal of Business Logistics, 2025 Together, these components form a selfcontained microcosm in which various individual actors collaborate toward the overarching goal of creating and sustaining a stable network (Couldry2008). As demonstrated in Section4.7 of this paper, all identified social abilities and their interdependencies contribute to maximizing reliability. These contributions foster the formation of a stable network, creating a selfreinforcing cycle that ultimately results in a continuously selfoptimizing network. In the corporate context, this AI network functions as one actor among many within a broader organizational ecosystem. This perspective aligns with the view that human and nonhuman actors should be treated as equally significant. To ensure the stability of this actornetwork, both human and nonhuman agents should receive equitable development opportunities and financial investments. Further, both must be integrated into the strategic objectives of the organization (Samarghandi etal.2023). 5.2 | Practical Implications This study introduces six social abilities AI brings to SCs, which enhance SCRes when extensively implemented. The proposed framework integrates various areas in which organizations must develop capabilities to effectively strengthen SCRes. It provides managers with a structured approach to assess their SC operations and departments, facilitating preparation for upcoming digital transformations. By identifying gaps or underdeveloped capabilities, these organizations can strategically build the competencies necessary for effective AI integration across all relevant areas of the SC. An ANT perspective is used in this study to emphasize the importance of addressing social factors in tandem with technological advancements when implementing AI in SCs. Given the widespread uncertainty and apprehension surrounding AI, the study highlights the necessity for managers and users to deeply engage with this disruptive technology. It is essential that individuals are provided with the time and resources to fully comprehend and integrate AI into their workflows, rather than merely treating it as an addon to their existing responsibilities. Such intensive engagement is crucial to foster effective collaboration between AI systems and human workers, thereby optimizing performance within the organization. Further, the study explores the interdependencies among the six social AI abilities to underscore their interconnectedness and the holistic nature of AI's contribution to SCRes. The findings of the study demonstrate the advantages of AI as an autonomous actor and examine the extent to which it can stabilize SCs. Ultimately, the degree of autonomy granted to AI is determined by the user, who may initially restrict its decisionmaking authority. As AI demonstrates its effectiveness, it can be entrusted with increasing autonomy, potentially achieving full independence in executing SC processes. The successful implementation of AI within an organization integrates the six identified abilities into the SC, enabling managers to address the black box concept introduced by Callon and Latour(1981) and ultimately enhance the resilience of the organizational SC network. This framework sets the stage for a new era of SC digitalization in which AI plays a pivotal role in ensuring operational stability and resilience. 5.3 | Future Research Directions Building on the findings of this study, several avenues for future research can be pursued to further advance the understanding and application of AI in SC management, particularly in the context of SCRes. A crucial area of exploration is the contextual adaptation and scalability of AI integration across varying organizational sizes and sectors. While our research acknowledges the universal applicability of AI, it remains essential to investigate whether variations exist in its effectiveness or the necessary abilities across different scales of enterprises, from sole proprietorships to large multinational corporations. Future research could examine constructs such as digital maturity and resource availability to better tailor AI solutions for small and mediumsized enterprises. Additionally, longitudinal studies focusing on the temporal evolution of AI's social abilities and their impact on SCRes could yield valuable insights. Understanding how these capabilities develop and mature over time, and whether all abilities need to be concurrently activated or can be staggered, may refine implementation strategies across different stages of SC growth. Considering the dynamic nature of AI, exploring scenarios where its predictive, flexible, and adaptive skills are nurtured incrementally could optimize resilience efforts. Further exploration of AI's role within specific SC sectors may offer nuanced contributions. Case studies embedded within individual segments, such as procurement, distribution, or logistics, could elucidate sectorspecific benefits and challenges. Such detailed analyses could enhance generalizability and lead to specialized strategies optimized for particular operational contexts. Another promising direction lies in integrating ANT with other theoretical frameworks to enrich the understanding of AI's role as both an actor and a network. Incorporating theories that address sociotechnical interactions or organizational behavior could provide a more comprehensive lens for evaluating AI's integration into SC ecosystems. This interdisciplinary approach may clarify AI's network interactions and amplify its strategic impact. Finally, quantitative assessments of the interdependencies among the identified social abilities of AI could establish a robust framework for understanding their interactions. Mathematical models or simulations may offer objective insights into the synergistic effects of these abilities on SCRes, potentially leading to more precise and predictive strategies for AI implementation. 5.4 | Limitations of the Study This study's limitations largely pertain to its scope and methodological approach, which constrain an exhaustive analysis of AI's role and potential within SC ecosystems. First, our inquiry
19 of 27 does not delve into the differential impact of AI across diverse organizational scales. Although AI's universal potential is recognized, its practical implementation may not be uniformly advantageous for all enterprises, particularly when accounting for disparities in resource availability and digital maturity. Future empirical work should segment enterprises by size to explore AI's differential benefits and constraints. Second, the research does not address the temporal dynamics of AI's social abilities comprehensively. While these abilities are pivotal to SCRes, the study lacks a temporal lens to observe their evolution or the sequential activation required for optimal outcomes. This gap necessitates longitudinal research to chronicle the development trajectory of AI capabilities and their sustained impact on resilience. Third, the study adopts ANT without concurrent theoretical frameworks that might offer complementary insights into AI's actor–network roles and interactions. Despite ANT's initial application as a theoretical backdrop, its limitations, such as vague actor definitions, call for integrative approaches with other theories to fortify interpretations and analyses of AI's role as both an actor and a network within SC contexts. Finally, the subjective representation of the interdependencies among AI's social abilities introduces another limitation. The qualitative assessments are grounded in expert interviews and author perspectives, which may lack mathematical precision. Incorporating quantitative methods to explore these relationships systematically could provide objectivity and a deeper understanding, offering more structured strategies for implementing AI to enhance SCRes. 5.5 | Concluding Remarks The increasing integration of AI into SC management presents both opportunities and challenges, necessitating a deeper understanding of its role within established networks. This study addresses a research gap by identifying six social abilities inherent to AI and analyzing AI's agency within the SC through the lens of ANT. These contributions provide a nuanced perspective of AI's impact on SCRes and its function as an autonomous actor in dynamic SCs. From a theoretical standpoint, the findings of this study contribute to the ongoing discourse on digital transformation in SC management by reframing AI not merely as a technological tool but as an active participant shaping SCs. By aligning the identified AI abilities with the phases of resilient SCs—readiness, response, recovery, and growth—this study offers a novel framework that contextualizes AI's role within established resilience paradigms. The conceptualization of AI as an actor, rather than a passive enabler, advances theoretical understandings of human–machine collaboration, highlighting the necessity of considering AI's decisionmaking capabilities, adaptability, and systemic interactions when integrating these tools into SCs. The application of the ANT framework to AI in SCs extends existing research on sociotechnical transitions by recognizing AI as both an individual actor and a network entity. This dual conceptualization emphasizes AI's transformative potential to reshape SC structures and operational processes. By acknowledging AI's social abilities—inevitability, neutrality, flexibility, vision, adaptability, and reliability—this study underscores the importance of governance mechanisms, ethical considerations, and regulatory frameworks to ensure AI's effective and sustainable integration into SCs. Finally, this research offers direction for future inquiries into the evolving relationship between AI and SC management, advocating for a holistic perspective that integrates technological capabilities with organizational strategy and human–AI interaction. As AI continues to permeate SCs, developing a deeper understanding of its agency and networked influence will be essential for designing resilient, adaptive, and ethically sound SCs. Acknowledgments Open Access funding enabled and organized by Projekt DEAL. Conflicts of Interest The authors declare no conflicts of interest. 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