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The remarkable growth of ChatGPT, a Generative Artificial Intelligence (Gen-AI), has triggered a significant debate in society. It has the potential to radically transform the business landscape, with consequences for operations and supply chain management (O&SCM). However, empirical evidence on Gen-AI's effects in O&SCM remains limited. This study investigates the benefits, challenges, and trends associated with Gen-AI/ChatGPT in O&SCM. We collected data from O&SCM practitioners in the UK ( N = 154) and the USA ( N = 161). As we used the organizational learning theory for the research, our findings reveal increased efficiency as a significant benefit for both adopters and non-adopters in both countries, while indicating security, risks, and ethical as prominent concerns. In particular, it appeared that the integration of Gen-AI/ChatGPT leads to the enhancement of the overall supply chain performance. Moreover, organizational learning can speed up the results of Gen-AI/ChatGPT in O&SCM. No wonders that adopters express their satisfaction about the post-implementation benefits of the technology, which include reduced perceived challenges for pre-implementation, and greater optimism about future Gen-AI/ChatGPT utilization compared to non-adopters. Adopters also display diverse behavioral patterns toward efficiency, agility, responsiveness, etc. This study provides valuable insights for scholars, practitioners, and policymakers interested in comprehending Gen-AI/ChatGPT's implications in O&SCM for both adopters and non-adopters. Additionally, it underscores the importance of organizational learning processes in facilitating successful Gen-AI/ChatGPT adoption in O&SCM. Introduction The emergence of ChatGPT (Chat Generative Pre-trained Transformer) is transforming business processes and models in virtually all types of industries (Informs, 2023; Kumar et al., 2023; Kothari, 2023; Agrawal et al., 2022). ChatGPT is a cutting-edge artificial intelligence (AI), more specifically, a generative AI chatbot API based on large language models (LLMs) trained by the amount of data to generate new content (Budhwar et al., 2023). In a brief and straightforward manner, Generative AI (Gen-AI) is a powerful artificial intelligence that can generate different types of content, from music and texts to codes and mathematical equations, etc., according to the interactions of prompt queries. Thus, this is part of a new generation of AI with unprecedented interaction with humans (Budhwar et al., 2023). The potential of Gen-AI/ChatGPT for all types of businesses and organizations is causing a bustle in society as a whole (Gordijn and Have, 2023; Larsen and Narayan, 2023). According to emerging literature on Gen-AI/ChatGPT, this technology is already bringing in its wake far-reaching changes in a number of industries, while also bringing about opportunities and challenges (Informs, 2023). However, the technology presents several drawbacks and limitations. From a financial perspective, despite its potential to support research in this domain, it still cannot deal efficiently with data synthesis and privacy issues (Dowling and Lucey, 2023). In the fields of education and business, Gen-AI/ChatGPT is flagging profound positive changes in a considerably short time, but its use reaveals some threats (Agrawal et al., 2022; Heidt, 2023; Larsen and Narayan, 2023). All of this has led scholars to debate the actual role of Gen-AI/ChatGPT and the need to adopt rules for AI-related ethical practices in the education and research domains (Nature Editorial, 2023; Stokel-Walker, 2023). Besides, Gen-AI/ChatGPT is causing similar concerns in several businesses and industries, like healthcare (Vaishya et al., 2023). For example, although it can support the patient's journey and the hospital's efficiency in medicine and healthcare, there are several concerns about its ethical use (King, 2023): the quality of recommendations; limited capacity on specific topics; etc. As they feature among the most (positively/negatively) affected areas by Gen-AI/ChatGPT, education and healthcare are our main focus here. For example, while Gen-AI/ChatGPT can contribute to the support of students with disabilities (Lyerly, 2023), its ethical use remains problematic (Abdulai and Hung, 2023; Bouschery et al., 2023). Overall, there are a lot of concerns about the ethical use of the technology, as there is also a need for urgent standardization, governance, and policies (Budhwar et al., 2023; Chen, 2023; Cotton et al., 2023). For instance, from the finance industry perspective, scholars have already reported the potential for data handling with this technology, but at the same time, it fails in data synthesis and amplifies the ethical concerns of organizations (Dowling and Lucey, 2023). In fact, there is no area that is not impacted both positively and negatively Gen-AI/ChatGPT. Even the academic community has expressed huge worries about the use of Gen-AI/ChatGPT in research and teaching (Susnjak, 2022; Bommarito II & Katz, 2022; Qadir, 2023). For example, Gen-AI/ChatGPT can help write convincing abstracts without plagiarism, but fortunately for academics, the writing style can be detected by other AI tools (Gao et al., 2022). Other benefitis of Gen-AI/ChatGPT in education include offering opportunities to learn in a personalized manner with robust feedback (Baidoo-Anu & Owusu Ansah, 2023). In the field of journalism, there is a debate about how Gen-AI/ChatGPT will transform jobs, collaborations between AIs and humans, the risks, and ethical concerns (Pavlik, 2023). In the healthcare field, Gen-AI/ChatGPT promises to support the creation of unprecedented levels of operations efficiency. For example, Gen-AI/ChatGPT can improve the efficiency of the overall processes, including accuracy, in writing patients’ clinic letters, thus bringing customer satisfaction (Ali et al., 2023). Concerning global warming and other climate problems, the same technology is well fitted to minimize their impacts. In particular, it can well improve accuracy in climate projections, model parametrization, interpretation, scenario modeling, and environmental assessments (Biswas, 2023). In the automotive industry, Gen-AI/ChatGPT is well taken advantage of by smart vehicles, as reported by Gao et al. (2023), notably in order to leverage critical features like safety and the user experience. While the potential and future of Gen-AI/ChatGPT (Paul et al., 2023; Ray, 2023) are being investigated by a number of studies, barriers to the technology are also at the centerstage of debates. Regarding the potential of Gen-AI/ChatGPT in operations and supply chain management (O&SCM), studies achieved so far are virtually from grey literature [e.g., Gartner and Forbes (Pukkila, 2023; Raveendran, 2023)]. To date, a few studies have been published in academic outlets to approach the interplay between Gen-AI/ChatGPT and O&SCM (Hendriksen, 2023; Wang et al., 2023; Cribben & Zeinali, 2023). In spite of the potential of Gen-AI coupled with ChatGPT in the field of O&SCM to reshape the business models, the field seems not ready yet to explore the benefits of this union (Hendriksen, 2023). For instance, Gen-AI/ChatGPT can bring some benefits to the O&SCM, such as the improvement of the processes efficiency, forecast enhancement, order fulfillment, as well as quick analysis of a large amount of data to support quick and better decisions, and more strong support and training for their employees (Hendriksen, 2023). To date, there is no doubt that Gen-AI/ChatGPT can transform the way of collaboration and communication between members in the supply chain (Hendriksen, 2023; Siotia, 2023). In addition, Wang et al. (2023), in the context of manufacturing, proposed an industrial GPT in order to enhance efficiency and flexibility in services. The existing literature about the Gen-AI/ChatGPT in O&SCM shows that it is still at the infancy stage, but the empirical evidence on how Gen-AI/ChatGPT may affect the O&SCM area is roughly absent, as well as the theorization of the technology's exploration in that domain (Hendriksen, 2023; Cribben & Zeinali, 2023). Thus, the literature about Gen-AI/ChatGPT is emerging and growing fast in traditional fields like information systems (Dwivedi et al., 2023a), healthcare (Vaishya et al., 2023), manufacturing (Badini et al., 2023), marketing (Peres et al., 2023), entrepreneurship (Short and Short, 2023), tourism (Nautiyal et al., 2023), government (Kreps and Jakesch, 2023), among others. However, in the O&SCM fields, there is a scarcity of papers published in reliable outlets (Hendriksen, 2023) exploring the dynamics of this relationship, the threats, and the learning process. Because of this, this study aims to provide an overall perspective of this new AI paradigm to O&SCM and the role of organizational learning to support its adoption. Accordingly, our study is guided by the following research questions (RQ): RQ1 How can the O&SCM field benefit from the Gen-AI/ChatGPT integration? RQ2 What kind of threat in this relationship could lure the attention of supply chain managers? RQ3 What role do organizations and supply chains play in integrating Gen-AI/ChatGPT in their operations? Our study is supported by the organizational learning theory background, which focuses not only on knowledge creation and sharing by people and organizations, but also on how to apply it to gain efficiency and add value (Qian et al., 2023; Cangelosi and Dill, 1965). By exploring Gen-AI/ChatGPT through the organizational learning theory, our study can contribute to advancing the traditional theory by revealing how organizations are dealing with this cutting-edge technology in their network to gain more knowledge. Additionally, the organizational learning theory enables this study to disclose the differences between adopters and non-adopters of Gen-AI/ChatGPT in O&SCM. Due to the novelty of the topic, the aforementioned questions need to be answered by focusing on an empirical approach based on primary data about Gen-AI/ChatGPT on O&SCM. We collected data from two representative countries, the UK and the USA supply chains practitioners. In this regard, our paper could well help scholars, practitioners, and policymakers to better grasp the main benefits, challenges, and trends in the interplay between Gen-AI/ChatGPT and O&SCM. The rest of this paper is structured as follows. Section 2 presents the latest advances in the literature about Gen-AI/ChatGPT. In sequence, in Section 3, we provide the details of the methodology design. Then, Section 4 presents the analysis of results, followed by the discussion and implications in Section 5. Finally, Section 6 points out the limitations and valuable insights for future research studies, and Section 7 draws the concluding remarks. Section snippets Organizational learning theory The organization learning theory is a well known and traditional theory on management and organizational fields (Qian et al., 2023; Cohen and Levinthal, 1990; Cangelosi and Dill, 1965). It concentrates on the knowledge creation as well on its use by the people from the organization. In this regard, organization learning theory approaches show how organizations build a learning culture, which in turn can support knowledge sharing, thus affecting processes and efficiency in production/processes. Methodology approach We used primary data due to the novelty of the topic, which requires an exploratory approach. In addition, “Primary data collection has the advantage of being specific to the study question, minimizing missingness in key information, and providing an opportunity for data correction in real time” (Dhudasia et al., 2023, p. 2). We developed a questionnaire adapted from Queiroz et al. (2023), which investigated the metaverse benefits, challenges, and trends (see Appendix A). To assess the benefits, Analysis of the results We analyzed the results by three approaches. The first one is a pooled analysis, that is, a full sample ( N = 315). In the second approach, we compared the UK and the USA. Finally, in the third, we compared the groups in the country. Thus, in all approaches, we established a comparison between Gen-AI/ChatGPT adopters and non-adopters. Discussion Regarding our first question (“ How can the O&SCM field benefit from the Gen-AI/ChatGPT integration? “), in the pooled analysis ( N = 315), our findings suggest important convergence between adopters and non-adopters). These findings are in line with the scarce literature on Gen-AI in O&SCM and related fields (Hendriksen, 2023). And with the real use cases found, this thought is reinforced. For instance, four of the Top-5 benefits were the same between the adopters and non-adopters (EFFI, RESP, Limitations of this study, and future research directions In relation to the limitations, the major is related to the lack of empirical studies about Gen-AI/ChatGPT, mainly in the O&SCM field, to compare our results. Another limitation is that our study used two mature and leading G-7 economies (the UK and the USA). Future studies could test the same research framework considering other countries from other geographic regions, like emerging and low- and middle-income markets. Besides, the three interesting propositions of our study can be empirically Conclusion Our study empirically investigated the role of Gen-AI/ChatGPT in O&SCM, focusing on adopters and non-adopters from the UK and the USA. We based our argumentation on the organizational learning theory. The findings suggest that efficiency is a major benefit for the companies that have adopted it and that they have not adopted it yet. Similarly, security was a major concern and the expectation (trends) about the future increased supply chain performance. We found interesting convergences and References (72) S.R. Ali et al. Using ChatGPT to write patient clinic letters The Lancet Digital Health (2023) M. Dowling et al. 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