Human–AI resource relations in value cocreation in service ecosystems

Artificial intelligence ^[1] (AI) applications, such as virtual and physical service robots, are claimed to alter frontline service (Wirtz et al., 2018; Paluch et al., 2020). With its ability to learn, adapt, connect and exhibit features of human intelligence, AI can act as a customer interface (Huang and Rust, 2018). Therefore, service robots and chatbots represent an increasing share of frontline service employees. While recent marketing and service research has witnessed an exponential growth of articles discussing different types of AI applications, such as generative AI, large language models (LLMs) and decision support systems (DSSs), the emphasis has been on the division and differences between human and nonhuman actors, and the AI’s potential for value cocreation and automation at the task level (Hottat et al., 2023).

The human vs AI dichotomy can be misleading, as it directs the discussion away from recognizing the combinatorial potential of humans and AI for value cocreation, which takes place in service ecosystems. In service-dominant (S-D) logic, Vargo and Lusch (2016) refer to Maglio et al. (2009) and define a service ecosystem as a configuration of people, technologies and other resources interacting to create mutual value. In information systems literature, the synergistic assemblage of humans and AI is discussed as augmented intelligence (e.g. Jain et al., 2021) and human–AI collaborative environments (Fügener et al., 2022). Nevertheless, despite the recent interest in the topic, the understanding of human–AI augmentation remains limited (Teodorescu et al., 2021). Further, while AI could augment professionals, studies on decision-making indicate that technology is not always utilized (Lebovitz et al., 2022). Recognizing the challenges of studying AI in isolation from humans, Anthony et al. (2023) and Pakarinen and Huising (2023) shift away from the perspective of AI as a standalone tool or medium that merely augments human capabilities. Instead, they embrace a relational perspective, conceptualizing AI as an integral component of the ongoing system of interactions, where value is cocreated and experienced.

Instead of considering humans and AI as separate actors, the purpose of this paper is to extend our metatheoretical understanding of resource integration and value cocreation in service ecosystems by analyzing different human–AI resource relations. Nuanced aspects of coconstituted and co-dependent resource relations are characteristic of service-dominant (S-D) logic (Vargo and Lusch, 2008). As resources only exist in relation to other entities, humans and AI as resources only exist in relation to one another (Barad, 2007; Anthony et al., 2023; Pakarinen and Huising, 2023). Kleinaltenkamp et al. (2023) characterize this as resources becoming entangled within service ecosystems.

Resource integration is driven by intentional and unintentional human behavior (e.g. Findsrud et al., 2018). Traditionally, operant resources were associated with intangible human attributes, such as explicit and tacit knowledge; operand resources with tangible (material) resources (Constantin and Lusch, 1994). In S-D logic, Akaka and Vargo (2014) were forerunners in positioning technology as an operant resource, recognizing that technology, coupled with knowledge and skills, may play a pivotal role in resource integration. Despite the increasing number of articles acknowledging the role of technology in value cocreation, the anthropocentric view that regards humans as the primary knowledge-creating actors and operant resources in value cocreation has remained dominant. We argue that the dichotomy between operant and operand resources lacks the conceptual power to explain technology’s various roles in value cocreation in service ecosystems (see also Kleinaltenkamp et al., 2023).

In our conceptual paper, we employ theory adaptation (Jaakkola, 2020) to contribute to service research, broadening the scope and comprehension of resource integration and value cocreation. We identify various human–AI resource relations in service ecosystems by incorporating insights from S-D logic and postphenomenology (Ihde, 1995), with a notable emphasis on cyborg intentionality (Verbeek, 2008; Aydin et al., 2019), which studies human–technology relations. We employ various forms of cyborg intentionality to answer our research question: What kinds of human–AI resource relations exist in value cocreation in service ecosystems? This question assists us in examining the diverse ways in which the entanglement of humans and AI influences the cocreation and experience of value within service ecosystems.

As our contribution, we present two propositions for human–AI research in service based on our theoretical discussion on seven different human–AI resource relations: background, embodiment, hermeneutic, alterity, cyborg, immersion and composite relation. First, humans and AI become entangled value cocreating resources in inherently sociotechnical service ecosystems. Here, we extend the recent advancements in S-D logic (Kleinaltenkamp et al., 2023) by focusing on the particular resource relation between humans and AI. We direct the attention to entangled resources to guide human–AI research toward acknowledging the inseparability of humans and AI. This leads to our second proposition: human and AI entanglements in value cocreation manifest through seven resource relations in inherently sociotechnical service ecosystems. The seven human–AI resource relations give service scholars and practitioners a unique perspective, helping them grasp the intricate entanglement of humans and AI in cocreating value within service ecosystems. Emphasizing human–AI resource relations offers a more comprehensive perspective on value creation and service ecosystem evolution than relying solely on humans as operant resources. We also present future research avenues for service research building on the identified resource relations. Our managerial implications focus on various human–AI resource relations and their potential for improved value cocreation.

The traditional division between humans and technology, and between operant and operand resources, has constrained our conceptual understanding of human–technology interactions, especially regarding human–AI resource relations in service ecosystems. Next, we discuss two main arguments challenging the human vs nonhuman actor and operant vs operand resource divisions in human–AI resource relations and value cocreation.

Firstly, service is increasingly cocreated with AI. As a result, the division between humans and AI has become liminal in value cocreation. For instance, there has been a surge in articles within service research discussing service robots, chatbots and AI in general. Human-type service robots have triggered an active discussion (e.g. Pitardi et al., 2022), and service robots are now viewed as a new employee category (Paluch et al., 2022). In S-D logic, Akaka and Vargo (2014) have applied thinking from science and technology studies, such as actor-networks (Callon, 1986; Latour, 2007), sociotechnical ensembles (Bijker, 1997) or mangles of practice (Pickering, 2010), which all challenge the traditional dichotomy between humans (social) and technology (material). Nevertheless, the conceptual basis of human–technology relations needs to be developed for theory development and practical implications.

Secondly, the rise of sociotechnical ecosystems, which combine social (people) and technical components in goal-oriented behaviors (Sony and Naik, 2020), also challenges the established divisions. We contend that service ecosystems are inherently sociotechnical, and agency emerges from the interaction between various entities, whether members of the biotic community, inanimate objects, hybrids or their configurations (Helkkula and Arnould, 2022; Kleinaltenkamp et al., 2023; Arnould and Helkkula, 2024). Customers sometimes might not differentiate between interacting with a human or an AI. For example, a chatbot might handle the initial customer service interaction when booking an airline ticket online. However, if the query becomes more complex, the chatbot might redirect the customer to a human “colleague.” The transition from traditional service to a service that uses AI technology could be so smooth and unnoticeable that the customer may not even realize or understand to what degree AI is being utilized by the service provider. Moreover, generative AI, capable of producing new content (e.g. audio, text, images) based on prompts from humans or software, has emerged as a cocreator of knowledge in service. This challenges the traditional division between humans and AI and the practice of attributing agency solely to humans. As a result, AI should be considered a part of entangled resources in a service ecosystem (Kleinaltenkamp et al., 2023). As AI does not preexist as an independent actor but emerges from computing power, algorithms, training data sets and human inquiries, focusing on human–AI resource relations in value cocreation becomes more meaningful.

Our study offers a fresh perspective on human–AI resource relations. We draw on the postphenomenological research tradition (Ihde, 1995; Verbeek, 2008), which explores how technology shapes our perception of the world and influences our behavior. As value cocreation emerges from entangled human and nonhuman resources, grasping how we study human experience toward and with technology, here AI is pivotal.

Postphenomenology argues that technologies coconstitute both the subject (actor) and the world (context). As a result, technology acts as a mediator within this context. For service research, technology is not simply a tool humans use in the physical world; instead, it significantly impacts how they interact with the world (Aydin et al., 2019).

S-D logic has a tradition of employing phenomenology to study value cocreation and service experience (Vargo and Lusch, 2008; Helkkula et al., 2012). Phenomenology focuses on first-person subjective experiences in a social context. Therefore, phenomenology does not consider technology to mediate our experiences. Instead, postphenomenology considers that technology shapes the world, enabling and constraining humans’ ability to know and act in the world (Goeminne, 2011). Technology is characterized by nonneutrality as it displays a particular intentionality. For instance, AI may affect value cocreation through “smart nudging” (Mele et al., 2021), resulting in widened resource accessibility, extended engagement and augmented agency of human actors. By combining social and technological elements (Aydin et al., 2019), postphenomenology emphasizes technology’s importance in cocreating distinctive resource relations and contexts of entangled systems for value cocreation. Thus, postphenomenology enables service researchers and managers to understand how technology as a nonneutral, entangled resource affects how value is cocreated and experienced.

We use postphenomenology as a method theory (Jaakkola, 2020) to analyze human–AI resource relations in value cocreation. In a conceptual paper, a method theory provides the conceptual framework for studying the domain theory. We use postphenomenology’s recent conceptualization of intentionality to identify human–AI resource relations in value cocreation in service ecosystems (Aydin et al., 2019). In earlier postphenomenological research, intentionality referred to the experiences that arise from an individual’s attention directed at an event, another agent or artifacts (van Manen, 2016). For example, Verbeek (2008)—one of the founders of postphenomenology—ascribed intentionality only to humans. In more recent research, Aydin et al. (2019) deploy cyborg intentionality as a meta-term for technology-mediated, hybrid, augmented and composite intentionality. Our paper draws on Aydin et al.’s (2019) four types of cyborg intentionality to rethink human–AI value cocreation in service ecosystems. Within Aydin et al.’s (2019) framework of the four types of cyborg intentionality, human and technological intentionalities intertwine. When human and technological intentionalities intertwine, technology seamlessly integrates with human intentionalities and becomes a novel contextual “world”. We note that while cyborg intentionality is the meta-term in Aydin et al.'s (2019) framework to define human technology value cocreation relationships, Aydin et al. (2019) also use the term cyborg to describe one of the seven types of human–AI cyborg relations they define, referring specifically to the case of human and AI as a hybrid, bionic system.

Aydin et al.’s (2019) cyborg-intentionality framework encourages re-examining human–AI resource relations, encompassing mediated, hybrid, augmented and composite intentionality. Next, we elaborate on these four types of intentionality and the seven human–AI resource relations: background, embodiment, hermeneutic, alterity, cyborg, immersion and composite resource relation.

In mediated intentionality, human intentionality occurs “through” AI (Ihde, 1995; Verbeek, 2008). The four types encompass human–AI background, embodiment, hermeneutic and alterity relations:

In the human–AI background resource relation, AI technologies operate subtly, enhancing human activities without dominating attention or consciousness. In this relation, AI is a tool or extension of human abilities, allowing humans to perform tasks more efficiently or effectively. For example, speech recognition software transcribes speech in real-time, or AI-powered personal assistants schedule appointments and perform tasks on their users’ behalf. In the human–AI background relation, people focus on the task or goal accomplished rather than the technology as long as the AI functions as expected.

The human–AI embodiment resource relation refers to integrating AI technologies with the human body, enhancing, and influencing human experience and capabilities. This resource relation integrates technologies with the user’s body, forging a connection between humans and their environment. For example, when wearing glasses, the glasses are not explicitly noticed but are “incorporated” into the body, becoming an extension of the human form (Ihde, 1995). This resource relation involves closely integrating technology and the body, resulting in seamless and constant communication. Examples of the human–AI embodiment resource relation include wearable smart devices, like sleep and activity trackers people wear to improve physical abilities.

The human–AI hermeneutic resource relation underscores the importance of interpreting and understanding the output of AI systems. These technologies provide representations of reality necessitating interpretation to form a “perception”—like a thermometer merely providing a value that must be “read” to determine something about temperature rather than producing an experience of hot or cold (Ihde, 1995). AI helps humans interpret complex datasets and information by applying AI algorithms and models to analyze data and create predictions. For example, financial analysis and medical diagnosis tools analyze massive amounts of data to help experts understand the situation. The human–AI hermeneutic resource relation emphasizes interpretation and comprehension while acknowledging the influence of human judgment and decision-making on the interaction.

The human–AI alterity resource relation characterizes the interaction between humans and AI systems, such as service robots. Humans engage with robots as if they are “quasi-other,” possessing human-like intentions (Benjamin, 2023). AI appears capable of exerting agency over its environment on behalf of humans interacting with it. In the human–AI background resource relations, humans typically notice the AI only when it malfunctions or ceases to function. In alterity resource relations, technological artifacts become opaque figures of human experience. Examples of human–AI alterity resource relations include robots in frontline service.

The human–AI cyborg resource relation pertains to the fusion of human and AI systems, culminating in a bionic entity or cyborg. This integration can lead to an increase in human abilities. Technology and a human become a single, hybrid entity. The lines between humans and technology are blurred in this relation, unlike in the embodiment relation, where one can easily (physically) separate human and nonhuman elements. Rather than merely altering the human perception of the world, technology in human–AI cyborg relations transforms humans into a novel human–technology hybrid. Examples of human–AI cyborg resource relations include brain–computer interfaces, AI-powered prosthetics and implantable medical devices using AI algorithms to regulate the function of internal organs.

In the human–AI immersion resource relation, technology makes an artificially expanded form of human intentionality accessible. This resource relation describes scenarios where AI technologies integrate seamlessly with the human environment, influencing human experiences and perceptions. In contrast to the human–AI background relation, immersion can be viewed as a more dynamic version where the environment not only senses humans but actively engages with them (Vindenes and Wasson, 2021). AI is not just a background but an integral part of the human experience, influencing and nudging behavior and creating a highly interactive and responsive environment. For instance, AI-based augmented reality (AR) can produce alternative representations of the world to humans, such as showing the impact of their intended purchase on the natural environment or spotlighting a product the customer is most likely to buy in a walk-in store based on the AI’s real-time data analysis. Given AI’s capability to discern emotions and relay information to other technologies, human–AI immersion resource relations add new layers of meaning and depth to the human experience and value cocreation processes.

In the human–AI composite resource relation, humans and AI collaborate to form a new hybrid entity, further blurring the line between humans and machines (nonhumans). Composite intentionality pertains to scenarios where both human intentionality and technological intentionalities intersect. It entails a dual intentionality: one from technology directed toward its environment and another from humans toward the results of technological intentionality. Technological intentionality refers to the deliberate use of certain technologies to engage with and shape particular facets of the world, such as detecting sounds inaudible to humans. Here, unlike in cyborg relations, the boundary is solely based on intentionality, not the physical. The two components coexist in a continuous dynamic loop, where one’s output becomes the other’s input. As a unique perspective, technological intentionality can reveal realities only accessible via technology (Verbeek, 2008). In simple terms, humans are attuned to how technology is directed at the world. This type of relation is common in areas such as human–robot interaction, where service robots work with humans to accomplish tasks. Through this fusion, humans and AI create a new reality that can only be experienced in this unique resource relation.

Table 1 summarizes the different forms of cyborg intentionality and gives examples of human–AI resource relations and cyborg intentionality in value cocreation.

Our discussion centers on the sociotechnical entangled perspective of human–AI resource relations and their potential to cocreate value in service ecosystems. The core of the sociotechnical perspective encompasses both the social and technical aspects of value cocreation. We reference Bijker’s (1997) assertion that in today’s sociotechnical ecosystems, human and technological resources can no longer be compartmentalized into separate silos.

Our study introduces a fresh metatheoretical viewpoint on human–AI resource relations, underlining AI’s pivotal function in cocreating value with humans and other entangled sociomaterial resources. Our contribution is to characterize sociotechnical value cocreation. In this context, the focus shifts from the human vs AI dichotomy to the value cocreation emerging from entangled human–AI resources. We present two foundational propositions capturing the entangled human–AI resource relations in service ecosystems.

• 1. Humans and AI become entangled value cocreating resources in inherently sociotechnical service ecosystems.

So far, S-D logic has yet to emphasize the pivotal role of AI in value cocreation. With this proposition, we argue that AI not only has a mediating role in value cocreation (Vargo, 2018). Assessing humans and AI as entangled resources in inherently sociotechnical service ecosystems is meaningful. When AI is present with its ability to display intelligent behavior, the dynamics of human interactions with resources change compared to interactions with non-AI technology. This has important implications for managing service ecosystems, emphasizing the need to recognize the increased role of human–AI relations alongside human efforts. What we add to existing cocreation research is that AI can act on humans just like humans act on other humans and technologies in service ecosystems. Furthermore, we question the human-centered understanding of only human beings as knowledge-creating, resource-integrating actors and claim that human–AI resources are increasingly taking this role. This is imperative as value increasingly emerges from human–AI resource relations. Even when we think about a human-centered, manual service, such as a haircut, AI often mediates the experience along the customer journey. For instance, when booking the service, a customer may ask the iPhone Siri (human–AI alterity relation): “Where is a barber store open nearby?”.

Postphenomenology offers a framework for understanding human–AI resource relations from various aspects. To continue the barber example, the customer whose hair was manually cut pays the bill using a payment system mediated by the AI (human–AI background resource relation). Thus, from the service provider’s side, either a person, an AI application, or their combination can take care of the payment. Even these simple examples indicate that the distinction between entangled human–AI resource relations is never clear-cut. Our theoretical discussion and examples show how postphenomenology, particularly cyborg intentionality, can advance a service ecosystems approach to replace a human vs AI and the operant vs operand dichotomies in service research. We indicate how intentionality is not conceptually meaningful to solely assign to humans or AI but to the entangled resources (Barad, 2007; Orlikowski, 2007; Kleinaltenkamp et al., 2023). In our managerial implications, we will further elaborate on the specific postphenomenological resource relations for value cocreation.

• 2. Human and AI entanglements in value cocreation manifest through seven resource relations in inherently sociotechnical service ecosystems.

Resource integration in sociotechnical service ecosystems is not human vs AI but includes human–AI resource relations. Service ecosystem elements are connected to a shared purpose, where physical and AI elements are architecturally designed (Mele et al., 2010; Mele and Russo-Spena, 2022). Therefore, designing value cocreation focuses on facilitating intended value cocreation for service providers and customers. Resourcing the intended value cocreation can be designed using different human–AI resource relations. For example, a fast-food takeaway restaurant offers two kinds of food ordering and pick-up options: a customer can stand in line and order food at the counter (human–human) or via a voice app in their wristwatch (human–embodied AI), get home delivery by a food delivery firm (human–background AI), or a service robot delivery (human–alterity AI).

These examples are positive human–AI resource relations. The two sides of the sociotechnical coin include the tremendous potential for new types of service innovations on one side and risks and threats on the other. For example, AI-powered autonomous vehicles can be safe and comfortable but raise people’s fears, which may cause reluctance to adopt the service (Meyer-Waarden and Cloarec, 2022). Moreover, certain AI-based innovations, like autonomous military drones (Dyndal et al., 2017), are perceived as threats to humanity. This underscores the need to recognize human–AI resource relations and revisit ethical guidelines and legislation. Ethical considerations extend beyond concerns about threats to humanity in human–AI interactions. They also encompass issues such as accountability and transparency regarding the utilization of AI within service ecosystems. We anticipate our work will illuminate the intricate dynamics of these entangled human–AI resource relations, aiding in addressing these challenges. Next, we discuss some possible future research topics that could advance human–AI resource relations in value cocreation.

Postphenomenology, particularly cyborg intentionality, provides a unique foundation for researching human–AI resource relations in value cocreation. We delineate future research for advancing service research within three interconnected areas: (1) studying human–AI resource relations in service, (2) advancing understanding of human–technology resource relations and (3) conceptualizing agency and intentionality in service ecosystems. Table 2 lists potential research questions for service research encompassing all three areas. In sociotechnical ecosystems, individual and collective social factors are relevant to all human–AI resource relations. When designing for human–AI value cocreation, it is vital to incorporate networks of actors, rules, social norms and institutions into the process (Vargo and Akaka, 2012; Vink et al., 2021).

Our study initiates a new approach to studying the roles of humans and AI in resource integration and value cocreation in service ecosystems. We need more research that does not simply draw a line between humans and AI but explores the consequences when these boundaries become indistinct. We call for studies to uncover how various human–AI resource relations impact the service frontline, which can be approached from the perspective of customers and frontline employees. The implications of this integration extend beyond the frontline. Further studies are needed to explore the influence of human–AI resource relations on areas ranging from actor engagement to market shaping and transformative service research. In empirical research, the effect may be contextually sensitive; hence, we need more studies to uncover the role of the cultural context in shaping human–AI resource relations. Further, there is a need to understand the ethical and social implications of the blurring boundaries between humans and AI in service ecosystems.

This study purposefully focuses on AI as a technology. We recognize that our decision limits the direct applicability of our work beyond human–AI resource relations. Therefore, we invite other scholars to employ postphenomenology as a method theory to further our general understanding of human–technology resource relations. More studies are needed to understand how AI stands apart from other technologies in value cocreation. For example, while AI is capable of autonomous decision-making, other technologies do not possess this skill. Thus, service scholars might explore how other human–technology resource relations impact value cocreation. These questions could influence not just service research but also other disciplines. For example, Hoffman and Novak (2018) observed that the recognized emergence of consumer-object assemblages prompts new inquiries and suggests broadening the scope of consumer behavior.

As service researchers move from a human-centric approach to a sociotechnical perspective on value cocreation, they will encounter new questions about resources and actors. For example, we need to deepen our understanding of agency and intentionality within service ecosystems and evaluate the significance of these conceptual terms. The merging of humans and technology into bionic beings or cyborgs further obscures the distinction between humans and technology. As these boundaries blur, it becomes crucial to comprehend how institutions shape human–AI relations and the consequent impacts. This understanding could aid programmers and service designers enhance AI’s contextual sensitivity within service ecosystems. Given that AI’s self-awareness in its autonomous decisions contrasts with human reflexivity, there is a pressing need for more research on agentic technology (Murray et al., 2021). The challenges posed by AI technology bring forth ethical and legal issues, especially when the line between human and nonhuman agency becomes blurred (Kleinaltenkamp et al., 2023). Further, the dark side of AI opens up intriguing research avenues.

Our two propositions adopt a metatheoretical perspective on human–technology resource relations, explicitly focusing on human–AI resource relations. We neither build models for specific human types or roles, such as customer or employee, nor particular AI applications or methods, such as image recognition or recommendation systems. Using the S-D logic lexicon: it’s all human–AI.

As the cocreation environment evolves with new technology, service providers need to remain agile and responsive to customers' needs. The perception of people and AI as entangled resources in value cocreation necessitates reconfiguring resource integration. Thus, understanding what kind of human–AI resource relations exist and what type of value cocreation processes can be designed is essential. We introduce a postphenomenological approach to human–AI value cocreation, outlining managerial responsibilities across four main types and their sub-types, as detailed in Table 3. Managers can use this approach to have a better understanding of alternative human–AI resource relations and rethink the roles of humans and AI in value cocreation. When service experience is understood as AI-mediated, managers can start experimenting, how various AI technologies can contribute to service provision as well as how they impact the perceived service experience in various contexts. Through this experimentation, managers can find human–AI resource relations that cocreate value in service ecosystems.

Human–AI resource relations do not always yield positive outcomes. Emphasizing the relationality of humans and AI in service ecosystems, we argue that the negative impact does not depend solely on the quality of the AI component. Value cocreation is also dependent on the capabilities and other characteristics of a human in the human–AI resource relation, as well as institutional arrangements of service ecosystems. Sometimes, institutions may need to be altered to embrace certain human–AI resource relations, such as brain-computer interfaces and implantable medical devices. This kind of institutional work requires managers to be aware of existing institutional arrangements, such as legislation and norms in medical practice, and capable of changing them through service ecosystem design (Vink et al., 2021).

In the first AI-mediated value cocreation type, AI is an entangled resource that mediates human experience. Table 3 further inventories four sub-types: background, embodied, hermeneutic (knowledge process), and alterity (a new type of “other”). Embodied wearables and “other” frontline employees, such as robots (Odekerken-Schröder et al., 2022) and chatbots (Mozafari et al., 2022), can cocreate positive and negative customer experiences, depending on the service frontline setting. Therefore, a new type of value cocreation requires strategic reorientation to retain customers in changing conditions.

In the second type, hybrid human–AI value cocreation, a customer and AI merge physically. For instance, this type offers cocreation possibilities for healthcare service providers when a chip is implanted into the body for monitoring purposes. Here, the AI’s role is physically different from the first type, which can lead to new legal and ethical challenges that managers need to consider.

In the third type, AI-augmented value cocreation, the human and AI merge virtually. AR and virtual reality (VR) are also used in health care, such as in surgeries and physical therapy, but their usage could be, and most probably will be, expanded to various other use cases soon. While AR and VR enhance reality, AR provides physical world enhancements, e.g. projecting an anatomy chart on a patient; VR detaches the user from the physical world. AR, VR, and the combination of mixed reality (MR) enable a new type of service cocreation reality (De Keyser et al., 2019). Here, managers can consider how human–AI resources can work together in a virtual world to cocreate value.

In the fourth type, the AI composition resource relation refers to value cocreation, which would be outside human experience without AI. For instance, AI-powered computational imaging may generate a picture of an ancestor by combining various data points. This resource relation provides opportunities for value cocreation that often extend beyond our current human imagination, enabling unique opportunities for those who can build on these relations.

Table 3 presents the different entangled human–AI resource relations and their potential for resource integration and value cocreation. Understanding the possible human–AI value cocreation types enables service providers to make conscious choices in their specific service context. We advise a strategic assessment of which types or mixtures offer fresh perspectives on resource integration and value cocreation, considering the possible impacts on customer experiences.

We claim that when designing resource integration, extending operant human resources to entangled combinations of humans and AI is imperative. The human–AI value cocreation types can assist managers to design different human–AI combinations and understand their potential for improved value cocreation.

In this metatheoretical study, we adapt a novel framework from postphenomenology, specifically cyborg intentionality, and introduce seven entangled human–AI resource relations. While the dominant discussion in service research has focused on how humans use and experience AI in service, we take a broader sociotechnical view of AI. This enables us to realize different human–AI value cocreation potentials in service ecosystems.

We claim the conventional division of human and nonhuman actors, such as AI in value cocreation, must be questioned and repositioned within the relational sociotechnical understanding of technology. Given the increasingly blurred resource relations between people and AI, research and practice should avoid overly celebrating or dramatizing risks in using AI in service.

By presenting various cyborg intentionality and resource relation types, we help scholars in service research and beyond to study value cocreation by entangled human–AI resource relations in service ecosystems (e.g. Fügener et al., 2022; Lebovitz et al., 2022; de Vericourt and Gurkan, 2023). We argue that to understand AI’s full potential and recognize the challenges it creates for ethical principles and legislation, it is imperative to consider AI as a pivotal part of resource integrating and value cocreating service ecosystems. We encourage future research to examine the specific potentials and challenges of the seven different human–AI resource relations in sociotechnical value cocreation in service ecosystems.

We would like to thank the Editor, Guest Editors and reviewers for their valuable comments that helped us improve the paper. ChatGPT was used for initial ideation and for parts of language editing.