Artificial Intelligence (AI) refers to systems that can perform tasks that typically require human intelligence, such as learning from data, understanding natural language, recognizing patterns and making decisions (Russell and Norvig, 2009). Unlike traditional computer programs that follow explicit instructions, AI systems are featured in digesting big data, adapting to new inputs and improving performance over time. At its core, AI is powered by algorithms that are sets of mathematical and logical instructions specifying how computers analyze information and make decisions. Modern AI relies on deep learning techniques, particularly massive neural networks, to process complex and high-dimensional inputs such as images, speech and natural language and extract patterns from relevant big data (Mienye et al., 2024; Razavi, 2021). The rapid-evolving generative AI represents a signature move of contemporary AI paradigms (Sengar et al., 2024). Tremendous industrial and academic resources have been investing to advance AI alignment, interpretability and efficiency and seek to balance innovation with safety, transparency and responsible deployment across global industries.In the context of the sport business, AI is not just a technological add-on but a transformative force that provides the tools to analyze complex data, automate operations and create new value for fans and stakeholders. This deep connection extends to sport business research, where AI has been reshaping how sport business knowledge is created, validated and applied. The impact of AI on sport business research can be understood from forging novel research agendas, advancing methodological paradigms and improving research efficiency.The most profound impact of AI is its role as a catalyst for novel research agendas. As AI technologies become deeply embedded in the everyday lives of sport consumers and rapidly reshape the operations of sport organizations, they create emergent phenomena that existing theories may not adequately explain. This presents fertile ground for sport business researchers to explore, potentially leading to conceptual refinements and the development of entirely new theoretical frameworks. Below we outline several research agendas for sport business scholars engaging with AI.How Fans Respond to Sport Products Integrating AI. Sport products are increasingly AI-supported, where AI serves as a supplemental component to enhance existing functions or even AI-powered, where AI acts as the core engine of the product itself (Naeem et al., 2024). This AI transformation ranges from the integration of AI for enhancing traditional game-day experience to the full adoption of AI in delivering personalized content recommendation, powering emerging sport-betting platforms and revolutionizing chatbot-based interactions. A new field of research is emerging to explore the psychological and sociological dimensions of how sport consumers interact with AI. This line of inquiry could further refine the conceptual models in the field or even lead to new theoretical frameworks explaining human–AI dynamics.How sport businesses utilize AI. The deployment of AI is reshaping sport organizations by transforming both day-to-day operations and high-level strategic roles, including but not limited to public relations, marketing planning, resource allocation, talent management and organizational design. As AI assumes a more central role in shaping strategy and governance, sport organizations may need to adapt their culture, capabilities and decision structures to remain competitive. A growing body of research aims to assess the effectiveness of adopting AI technologies in organizational operations and strategic management and explore organizational structures needed to address the challenges of AI transformation.How governing bodies and society adapt. The prevalence of AI raises significant questions for governance and ethics. Critical frontiers for research include investigating algorithmic bias in fan profiling, ensuring data privacy and establishing fair regulations for AI in sports betting. This line of inquiry is essential for developing new governance models and ethical guidelines for the sport industry.Sport business research has progressed from employing AI primarily as an analytical tool or methodological enhancement to developing AI-centered research agendas that examine how key stakeholders respond to various applications of AI in the sport industry. At present, consumer responses have received greater scholarly attention, whereas the perspectives of business entities and the implications for governance and policy remain comparatively underexplored.Beyond creating new topics, AI is reshaping the methodology of sport business research. It provides powerful new ways to advance existing research agendas by advancing data variety, data volume, data collection, analytics and experiment simulation, which benefits both correlational and experimental studies.Augmented data features. With the assistance of natural language processing (NLP) and computer vision, researchers are no longer limited to structured numerical data such as surveys and official statistics. We now can tap into vast and varied unstructured data sources like natural language from social media, images from fans and video feeds from games. The data with high breadth, granularity and contextual richness enable extraction of sentiment, emotion, contextual meaning and relationships that traditional datasets could not capture (Mao, 2025). The volume and velocity of available data have expanded exponentially, offering a richer, more holistic view of the sport ecosystem (Mamo et al., 2022).New quantitative solutions. Machine learning and language processing models provide powerful additions to the researcher's arsenal, which largely enhance our capacity for analyzing complex unstructured data, modeling non-linear relationships, uncovering latent structures and elevating prediction power that are challenging to achieve with the traditional analytics paradigm. AI can also assist in cleaning and pre-processing large datasets, detecting anomalies and suggesting data transformations. The analytical advancement empowers researchers to develop conceptual models or test established theories with a level of rigor and predictive accuracy that was previously impossible (Chen and Chen, 2024).AI-enhanced qualitative approaches. AI and NLP have also transformed traditional qualitative research methodologies, significantly expanding researchers' ability to process large-scale textual data and automate time-intensive coding processes (Hitch, 2024). They help researchers identify thematic patterns across extensive textual corpora, facilitate rapid comparison across multiple data sources and enhance reproducibility of interpretive analysis in ways that manual approaches often fall short of (Nelson, 2020). This paradigm shift allows researchers to engage with both the breadth and depth of consumer experience simultaneously (Mao et al., 2024).Innovative computational simulation. AI facilitates the creation of sophisticated simulations that explore and examine the behavior of key stakeholders (e.g. fans, athletes, sport organizations and general businesses) within sport business ecosystems. Notably, generative AI gives researchers unprecedented power to design and tailor realistic experimental stimuli such as synthetic commentary, virtual sport environments or tailored promotional messages, enhancing the rigor and ecological validity of experimental designs.AI also benefits sport business research at a broad level by reshaping and streamlining fundamental early-stage tasks, thereby improving research workflow efficiency and enabling scholars to devote more time to higher-level analysis and interpretation. For example, AI-powered platforms such as Semantic Scholar and Sourcely have significantly improved the efficiency of literature review and information search, sorting and synthesis. These tools interpret the context of queries rather than relying solely on keywords, automatically identify related papers, summarize key findings and generate conceptual maps of research areas, enabling scholars to more quickly evaluate existing literature and identify gaps. By automating these foundational tasks, AI not only improves efficiency but also enhances reproducibility in the iterative research cycle where new knowledge builds upon prior work.Five studies in this special issue explore consumer response to AI transformation in various sport consumption settings, ranging from the deeply personal (AI-supported wearable devices) and the interactive (AI chatbots) to the persuasive (generative AI in ads) and the high-stakes (AI-driven sports betting). This collection of work provides crucial managerial implications for navigating the AI transition and enriches theoretical frameworks by illuminating the complex factors, such as emotion, technology anxiety, perceived trust and subjective norms that ultimately determine consumer adoption.Lee et al. (2025) investigated how consumer evaluations of AI-generated sports ads are affected by AI awareness timing, advertisement model type and source-message incongruence. The results show that AI awareness generally have a positive impact, particularly when consumers are aware of the AI's role after viewing the ads. Virtual Human models are rated the lowest compared to Digital Twin and Human models and source-message incongruence negatively influenced evaluations. The study offers insights for practitioners on optimizing AI ads by strategically timing disclosures and selecting appropriate models and provides references for effective AI integration in sport advertising practices.Gerke et al. (2025) empirically examined consumer responses to the AI-supported wearable devices based on the Artificially Intelligent Device Use Acceptance Model (AIDUA). Their findings highlight a significant intention–behavior gap, as emotions were found to predict the intention to use but not actual consumption. The study also nullified a common assumption that consumers' appraisal of AI anthropomorphism influences their performance or effort expectancies. These insights are critical for sports managers and marketers aiming to improve the adoption of AI-supported sports services.Grounded in the parasocial interaction and the social exchange theories, Choi and Lee (2025) investigated how anthropomorphized AI chatbots in sports enhance social presence to boost consumer loyalty and reduce technology anxiety. Key findings indicate that anthropomorphism successfully increases social presence, which in turn positively influences loyalty while negatively affecting technology anxiety. This study also identifies technology anxiety as a partial mediator, showing that a heightened social presence can mitigate anxiety's negative impact on user loyalty. This research effort extends theory by demonstrating that social presence is a key mechanism for reducing user anxiety, offering practical insights for sports marketers using AI to enhance consumer engagement.Buechner et al. (2025) scrutinized whether the source of a sports betting recommendation (AI or human) affects consumer perceptions of expertise and their likelihood to follow the advice. Through three lab experiments, this research consistently found that compared to human resources, AI recommendations significantly decreased consumers' perceptions of expertise. This lower perceived expertise in turn reduced participants' likelihood of following the betting recommendation. As one of the pioneer studies in this area, the findings suggest that despite technological advances, consumers currently exhibit lower trust in AI for sports betting advice, perceiving human sources as more credible.Dinç et al. (2025) assessed ChatGPT adoption's impact on soccer bettors' behavioral intention and word of mouth. Survey results show that attitude and subjective norms are strong predictors of behavioral intention. Specifically, perceived ease of use and usefulness positively shaped attitude. The effect of usefulness on intention was indirect, mediated entirely by attitude. Social influence significantly drove word of mouth via subjective norms and behavioral intention. This research broadens the applicability of existing theoretical frameworks by examining AI adoption among soccer bettors, while simultaneously offering AI developers actionable strategies to improve user acceptance within this evolving market.Perspectives on how sport organizations utilize AI show organizations' capacity to digest AI techniques to compete, profit and grow in today's market. This research body is essential for understanding the strategic, operational and economic implications of AI in the sport ecosystem. The current special issue highlights two studies by five scholars.Du et al. (2025) examined whether AI can assist in training sports salespeople by evaluating their interactions with prospective ticket buyers. Using topic modeling and sentiment analysis on transcribed National Basketball Association (NBA) sales calls, the research identified several key predictors of success. Findings show that agents with greater lexical diversity and a moderate speaking pace generated more positive customer sentiment and achieved higher sales success. Additionally, a positive association was found between asking more open-ended questions and effective information gathering. Guided by the Technology-Task-Fit theory, this study provides evidence that AI can effectively analyze sales conversations to deliver applicable, data-driven feedback, supporting its integration into modern salesperson training programs.Fortunato and Kosterich (2025) examined how Amazon Web Services (AWS) uses its functionally congruent sponsorship with the National Football League (NFL) to demonstrate its performance capabilities. AWS provides both on-field (e.g. player health and safety) and off-field (e.g. game scheduling) services to the NFL and promotes this deep integration via major marketing communications (e.g. AWS websites, in-game elements and TV commercials) to showcase its brand reliability, which is a key factor in business-to-business marketing. The core message of sponsorship implies that if AWS can handle complex tasks for the NFL, it can certainly do the same for a potential client's business. This study provides a timely, practical example of how AI brands leverage sports sponsorship to communicate and position their advanced technical services.As previously discussed, AI is largely reshaping the sport data frontier, altering data attributes (variety, volume and velocity), collection methods, analytical routines and experimental simulation. The ripple effects of this data revolution on sport business research are profound and pervasive. This impact is comprehensively exemplified by four articles featured in the current special issue.Anagnostopoulos et al. (2025) utilized natural language processing (GPT-4) to analyze how companies on the Qatar Stock Exchange reported their “corporate social responsibility (CSR) through sport” initiatives within annual reports from 2006 to 2022. By automating information retrieval from all 46 listed companies, the analysis identified 672 distinct CSRs through sport initiatives, revealing a significant upward trend over the period. The primary contribution lies in its human–AI framework, which provides a novel and efficient method for systematically analyzing how publicly listed companies communicate their CSR activities in the sport sector. This offers a new perspective on corporate philanthropy in the Middle East.Ryu et al. (2025) used AI to analyze how player performance in professional women's volleyball affects fan emotions, measured via sentiment analysis of Instagram comments. It also examined the moderating roles of superstar status and facial attractiveness. The results confirmed fan emotions are tied to game outcomes but revealed a complex beauty bias. Specifically, attractive players received a beauty premium (less negativity) for errors, yet faced a “beauty penalty” for scoring points, a dynamic not observed with non-all-star players. This research advances the use of AI to demonstrate how attractiveness moderates fan reactions to on-court performance, expanding prior work that focused primarily on off-court factors like salary.Bian and Cork (2024) developed a machine learning model capable of predicting the of and identified the key these machine learning for fan offers a data-driven to traditional The model identify sponsorship as the most by and fan The research provides a novel for understanding how fan are offering an actionable for practitioners to enhance sponsorship et al. 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