Abstract
The infusion of generative AI (GenAI) is already disrupting established services. This technology’s generative and agentic nature challenges the design and management of service routines, which have been previously handled primarily by frontline service employees. Guided by organizational routines theory, our longitudinal study (2020–2024) examines how the infusion of GenAI changes routines in customer support services. We gathered interview data from 41 employees, managers, and AI experts in two phases,
Introduction
Generative artificial intelligence (GenAI) is disrupting the ways service providers deliver services to their customers (Ferraro et al. 2024; Liu et al. 2024). At its core, GenAI refers to large-scale AI models, such as large language models (LLMs), that learn from vast data, can perform multiple tasks, and generate human-like outputs (Liu et al. 2024). Tech giants are investing heavily in GenAI-powered systems, with Microsoft alone reaching a record $30 billion in quarterly spending (Reuters 2025), to shape the future of work. For instance, Microsoft’s Copilot and ServiceNow’s Now Assist illustrate how GenAI copilots complement frontline service employees (FSEs) by collaboratively executing service routines, such as retrieving knowledge, automating tasks, and coordinating human-human interactions, thereby reconfiguring interactions across the service frontstage and backstage (Microsoft 2024). Similarly, Intercom’s Fin AI Copilot generates response recommendations, aiding FSEs in problem-solving and achieving a projected productivity increase of 31% (Intercom 2024).
Previous research has developed structured frameworks to navigate the complexities of AI infusion, systematizing capabilities that clarify when and where AI technologies are appropriate for performing specific tasks and substituting FSEs (Huang and Rust 2018). This perspective further shifted to an understanding of AI infusion as a continuum of substitution and complementation (e.g., Larivière et al. 2017; Marinova et al. 2017), which we refer to as augmentation of FSEs throughout this study. We adopt the conceptualization of Baer, Waardenburg, and Huysman (2025, p. 761), who define
Despite the aforementioned advancements in service research, we still know little about how GenAI reshapes FSEs’
Our study aims to refine the augmentation continuum of substitution and complementation (De Keyser et al. 2019; Huang and Rust 2018) and adopts a process-centric view to discern the impacts of GenAI along FSEs’ service routines and across the service frontstage and backstage. We adopt the theoretical lens of organizational routines (Feldman and Pentland 2003; Gaskin et al. 2014) to examine recurring micro-level representations of augmentation that shape and reflect evolving GenAI-infused routines. This leads us to the following research questions (RQs):
We conducted a longitudinal qualitative study (2020–2024) of GenAI infusion in four customer support organizations, drawing on 41 interviews with FSEs, managers, and GenAI experts conducted before (
Our results contribute a process-centric and integrative view of the emerging GenAI-infused service routines to the literature on hybrid human–AI service delivery (e.g., Koponen et al. 2023; Mortati and Viana Mundstock Freitas 2025; Robinson et al. 2020). The identified MAPs provide descriptive knowledge on how and where GenAI is infused into services. Thereby, our study refines the understanding of the continuum of substitution and complementation (e.g., Baer, Waardenburg, and Huysman 2025; Marinova et al. 2017). We extend prior research by examining the processual changes and patterns that augment service delivery—both at the service frontstage, the invisible backstage, and the interactions in between (Bock, Wolter, and Ferrell 2020; Liu et al. 2024). Furthermore, the resulting service permeation mechanisms explain a closer coupling of service frontstage and backstage along GenAI-infused service routines (Hogreve, Iseke, and Derfuss 2022; Mortati and Viana Mundstock Freitas 2025), as they highlight diverse FSE-GenAI collaborations that uncover simultaneous and sequential mechanisms. Applying the derived MAPs enables practitioners to leverage GenAI (Brynjolfsson, Li, and Raymond 2025) while concurrently addressing implications for FSEs’ service routines and the challenges of GenAI permeation within service delivery.
Theoretical Background
Hybrid Human–AI Service Delivery
AI infusion has traditionally been seen as a means to automate service delivery by replacing FSE tasks and directly influencing customer perceptions (Huang and Rust 2018; McLeay et al. 2021). However, in light of AI’s limitations and the demands of personal, emotional, and knowledge-intensive service contexts, the focus shifted from merely designing AI technologies for customers to examining how FSEs and AI together form
Streams of Literature on Hybrid Human–AI Service Delivery.
Recent research has advanced our understanding of the hybrid nature of AI-infused service delivery by conceptualizing how responsibilities are distributed between human and AI actors through the analysis of configurations, archetypes, and typologies (e.g., De Keyser et al. 2019; Koponen et al. 2023; Larivière et al. 2024). Advances in AI increasingly enable it to take over diverse service roles, with distinctions such as mechanical, thinking, and emotional intelligences informing predictions about when AI may substitute FSEs (Huang and Rust 2018). Building on these capability-based distinctions, scholars have moved beyond the traditional human-to-human encounter, uncovering a spectrum of configurations, from AI-to-AI to blended human–AI interactions (De Keyser et al. 2019). These evolving hybrid human–AI service encounters underscore that AI is not a one-size-fits-all solution but varies in its capacity to substitute or complement human work (Marinova et al. 2017). The augmentation of service work can thus be understood as a continuum of substitution and complementation, in which FSEs and AI perform service routines either sequentially or simultaneously (Le et al. 2025). Augmentation enables humans and AI to perform complementary or joint tasks and to co-produce the service (Blaurock, Büttgen, and Schepers 2024), while implicitly reconfiguring professional roles, positioning humans not just as service providers, but as collaborators, coordinators, or differentiators (Koponen et al. 2023; Larivière et al. 2017). For example, AI systems engage FSEs by proactively asking for input and feedback (Blaurock, Büttgen, and Schepers 2024). Despite these contributions, the literature on hybrid human–AI service delivery remains predominantly conceptual, leaving key questions unanswered about micro-level implications and the dynamics along the substitution-complementation continuum in AI-driven augmentation.
Ongoing research has also aimed to unpack human–AI hybrids by examining how AI systems enhance or substitute human roles, both visibly on the frontstage and invisibly in the backstage of service processes (e.g., Bock, Wolter, and Ferrell 2020; Mortati and Viana Mundstock Freitas 2025; van Doorn et al. 2023). Building on a service design perspective, Mortati and Viana Mundstock Freitas (2025) conceptualize AI as a technology that co-creates value and copilots service delivery with both users and machines across frontstage and backstage environments. This stream offers a more granular view of how AI supports human work (Liu et al. 2024), showing that the infusion of AI reshapes traditional boundaries between the frontstage and backstage (e.g., Mortati and Viana Mundstock Freitas 2025; Robinson et al. 2020). For example, customers may misattribute AI-generated responses to humans, resulting in counterfeit encounters and ambiguous role expectations (Robinson et al. 2020). In light of this, adequate AI support at the backstage (e.g., decision-support tools) critically impacts the quality of frontstage interactions, reinforcing their interdependence as articulated in the AI-mediated service-profit chain (Chen, Hsieh, and Chan 2024; Hogreve, Iseke, and Derfuss 2022). While the link between service frontstage and backstage is acknowledged, the influence of emerging AI technologies, such as GenAI, on this connection remains underexplored.
At the same time, growing interest in the technological capabilities of GenAI models (Ferraro et al. 2024; Liu et al. 2024) has highlighted their potential to alter how humans and AI interact in service delivery (Brynjolfsson, Li, and Raymond 2025). Given the ability to understand natural language and generate text, GenAI systems (e.g., Microsoft Copilot, SAP Joule) exhibit a higher level of agency (Chen, Hsieh, and Chan 2024; Le et al. 2025), challenging the prior insights on how FSE and AI work together along service processes and the way these systems are embedded into the service routines. Against this backdrop, the rise of GenAI introduces a new layer of complexity. Technologies such as LLMs not only extend the functional boundaries of AI in service contexts but also generate paradoxical dynamics, being perceived as both highly capable and vulnerable (e.g., prone to hallucinations and biases) and as offering personalization while raising concerns about intrusiveness (Ferraro et al. 2024; Liu et al. 2024). Recent advancements in agentic AI further accelerate these developments, as AI systems increasingly exhibit autonomy and enhanced capabilities in reasoning and learning, enabling them to leverage a wide array of enterprise tools (Acharya, Kuppan, and Divya 2025).
Despite growing interest in hybrid human–AI service delivery, our understanding of how GenAI influences FSEs’ tasks and their relationships with AI, beyond a dyadic view of substitution or complementation toward joint enactment, remains limited. Similarly, while distinctions between frontstage and backstage domains exist, the interplay introduced by AI across these layers is only beginning to be understood. As GenAI technologies are being infused in service delivery, this study examines their influence on service delivery by analyzing how they reconfigure routines across the frontstage, backstage, and their intersections.
Organizational Routines in Service Delivery
Organizational routines in service delivery provide a theoretical foundation for analyzing the augmentation of service work.
While early research on organizational routines emphasized their stability, later work on routine dynamics focused on how routines evolve and adapt to changing environments (Feldman and Pentland 2003). Deeply intertwined with their context, routines both shape and are shaped by it, while technologies like GenAI act as powerful exogenous forces influencing their enactment (Murray, Rhymer, and Sirmon 2021). Efforts to provide a more detailed understanding of AI technology use in routines often focus on augmentation as a feature of the infused technology itself, rather than as a function of the service routines where this augmentation occurs (e.g., Blaurock, Büttgen, and Schepers 2024; Murray, Rhymer, and Sirmon 2021). However, human actors and material aspects of technology become interconnected within the service routines (Gaskin et al. 2014), calling for an investigation of FSEs’ GenAI-infused patterns of actions and the dynamics of these socio-material ensembles in performing service routines and the resulting blurred boundaries of backstage and frontstage activities.
The organizational routines lens helps reveal how FSEs and GenAI interact at the micro level, co-performing tasks and shifting responsibilities between frontstage and backstage work. It allows us to move beyond high-level categories and to develop a more refined understanding of hybrid service delivery. This grounding motivates our study of how GenAI infusion constitutes augmentation patterns (
Methodological Approach
We employed a qualitative research approach grounded in extensive empirical data from the field of technical customer support services. By covering both predictive and generative AI phases, our longitudinal study setup (2020–2024) traces the shift from traditional AI to emerging and advancing GenAI solutions and their implications for service delivery. We conducted 41 semi-structured interviews with FSEs, managers, and GenAI experts to explore actual work practices and anticipated changes across
Overview of Our Methodological Approach.
Sample Description
Our qualitative investigation explored the realm of technical support work and IT Service Management (ITSM) as a subfield of general customer support, a field that has been extensively studied within the longstanding body of research on organizational routines (e.g., Das 2003; Pentland 1992). When issues arise with digital technologies, customers reach out to frontline service providers through various channels to resolve their technical problems. To reflect the field’s heterogeneity, we drew data from four organizations spanning diverse service channels, IT products, customer types, and B2B and B2C contexts. Detailed descriptions of the four organizations are available in Web Appendix 1. Our empirical study is part of an overarching research project that develops advanced analytics and ticketing solutions to store and manage incoming and historical customer requests and interactions. The larger project aimed to design a holistic hybrid intelligence system with distinct modules to support FSEs through AI in a human-centered manner. The iterative development process allowed participants to gain practical experience over time, which informed their responses during the data collection phase.
Data Collection
To collect empirical data, we conducted semi-structured interviews with FSEs, managers, and AI experts, aiming to triangulate views on the underlying routines, the overarching service delivery process, and the associated technical changes. This approach allowed flexible and in-depth discussions with the informants and enabled us to detail their subjective perspectives (Ritchie, Lewis, and Elam 2013). Organizational routines informed the design of the interview guidelines in terms of addressing the problem identification and problem-solving stages (Das 2003; Pentland 1992). To examine differences in infusing predictive vs. generative AI into frontstage and backstage service delivery, we conducted two interview series:
The
Data Analysis
We analyzed the collected interviews through an adapted grounded theory approach following Corbin and Strauss (1990). To deepen our understanding of service routines in light of GenAI, we employed an abductive research design to not only inform our interview guidelines but also to reflect the data of both series (Pemer 2021). We iteratively moved between emerging patterns in the data and analytical lenses offered by service routine literature (Das 2003; Pentland 1992). Our approach prioritized intensive group discussions and consensus-building through an iterative process of comparing and integrating codes and categories, rather than relying solely on quantitative measures of intercoder reliability (Berente et al. 2011). The codes and categories were discussed with the entire research team, particularly on three occasions: after the first series, after the second series, and during the analysis of the problem-solution pairs and patterns of augmentation. To address the widely recognized challenge of overcoming preconceptions in qualitative data analysis (Charmaz 2014), we later incorporated an impartial co-author into our research team.
In the first interview series spanning 2020 to 2021, we employed open and axial coding to extract requirements and categorize issues (Corbin and Strauss 1990). First, we used an open coding strategy to understand FSEs’ and managers’ viewpoints and experiences with AI, drawing on the interviewees’ empirical statements (Corbin and Strauss 2015). As we gathered data before GenAI was adopted, the analysis focused on broader AI applications. Afterward, we applied axial coding to categorize the data into recurring issues related to service routines. This step was pivotal in identifying the foundational problems concerning existing service routines and aggregating expectations regarding the use of AI. Subsequently, we further categorized these concepts according to the service routine literature following our abductive approach.
Analogously, we analyzed the second interview series conducted in 2023 and 2024 by adopting the above-mentioned qualitative approach. However, the analysis of this series focused on coding solutions that emerged with GenAI. This phase was instrumental in delineating solutions from GenAI applications and attaching them to the identified issues. Accordingly, the interview guidelines and the approach to analyzing the data were informed by the results of the first interview series, in line with our abductive design. Again, we coded the interviewees’ statements and mapped the codes to the given routines during an open and axial coding phase. At this stage, we again re-engaged with the literature on service routines to interpret and categorize emerging patterns. Our abductive strategy guided the refinement of our coding scheme throughout both interview series. For example, as we encountered recurring accounts of documentation work and AI input preparation, we conceptualized a new routine category:
Then, we employed an additional axial coding phase to map the solutions to the corresponding issues. Web Appendix 4 illustrates this mapping along the service routines. Following Berente et al. (2011), we performed selective coding to link and interpret the derived problem-solution pairs and, ultimately, to discern MAPs. To analyze the MAPs within the context of service delivery, we added additional dimensions and organized the evolved patterns according to the service frontstage and backstage (Mortati and Viana Mundstock Freitas 2025). We examined the interplay between the service frontstage and backstage through the identified MAPs to account for GenAI’s impact on service delivery and theorizing mechanisms. In line with Henfridsson and Bygstad (2013), we define mechanisms as the underlying causal structures that explain how and why phenomena unfold. In our context, mechanisms are the generative processes through which GenAI permeates service delivery, and by abstracting from the empirically grounded MAPs, we conceptualize two higher-order service permeation mechanisms that explain how GenAI reshapes the coupling of human and AI activities across frontstage and backstage domains.
Findings
Our analysis uncovers seven aggregated
Micro-Level Augmentation Patterns
Through the aggregation of multiple micro-level problem-solution pairs that represent various forms of augmentation through GenAI-infusion, seven overarching patterns of augmentation on a continuum of substitution and complementation emerged:
Micro-Level Augmentation Patterns.
Triaging and Solving Customer Requests (MAP-1)
MAP-1 centers on the initial phase of service delivery, where FSEs traditionally invest substantial time in qualifying support requests, identifying customer needs, and referring to standardized responses. In GenAI-infused service settings, customers first interact with GenAI systems that handle simple, repetitive inquiries. GenAI automates processing these routine requests, thereby relieving FSEs of repetitive tasks and allowing them to focus on more complex cases. In this way, the pattern reflects a partial substitution of initial frontline tasks while also triggering the escalation and forwarding of critical issues to human experts. Given the current volume of customer requests, FSEs view service frontstage substitution as an opportunity rather than a threat to their jobs. Hence, dividing labor between FSEs and GenAI in terms of partially automating service delivery refers to a key leverage of augmentation (Jia et al. 2024). However, the overarching premise that AI primarily handles straightforward, less complex tasks remains valid even in the era of GenAI. FSEs’ responsibilities shift to taking care of ambiguous and complex requests which GenAI cannot handle, as this AI expert commented: “
An essential routine in this initial part of the service delivery process is to create tickets, which involve collecting customer information and documenting service requests. Technological advances in LLMs further enhance the ability to clarify incomplete and partially ambiguous requests. As this interviewee explained, “
Summarizing and Handing Over Requests (MAP-2)
After initial contact and throughout service delivery, FSEs are required to document information and summarize the actions taken and conversations held. These documentation activities are essential not only for referring, transferring, or escalating requests to colleagues but also for retaining and institutionalizing service knowledge. The findings demonstrate GenAI’s potential to substitute summarization and ticket handoffs, providing customers and FSEs with comprehensive, coherent problem and resolution descriptions. By tracking conversations and gathering all customer-related data, GenAI systems with high agency create summaries that serve as a foundation for FSEs to craft impactful moments of truth. Thus, MAP-2 goes beyond simply summarizing the latest interactions. As one interviewee explained, “
Copiloting the Identification and Resolution of Requests (MAP-3)
When addressing complex customer issues handed over by a GenAI-based self-service system or another FSE, employees require contextual support such as knowledge sources or pre-formulated responses. Our analysis shows that GenAI complements these routines during real-time service delivery, enhancing FSEs’ ability to understand and resolve complex issues. This pattern reflects the simultaneous, synergistic collaboration between humans and GenAI, in which backstage GenAI permeates frontstage customer interactions through its use by FSEs. By comparing
The frustration with current search inefficiencies underscores GenAI’s potential to change traditional search paradigms, providing relevant results even from inadequately documented sources and ambiguous queries: “
Matching Knowledge and Experts (MAP-4)
Matching knowledge and experts to problems (MAP-4) has been discussed before GenAI and focuses on providing FSEs with access to contextualized knowledge and expert consultations. This approach helps them address specific service requests, particularly when transferring or escalating complex problems. The routines of allocating customer requests to the appropriate departments or experts are yet a critical field: “
FSEs express dissatisfaction when they are unable to outline next steps or guide the customer, often having to defer the interaction. The following expert comments highlight a sense of inefficacy and frustration when tickets are not directed correctly: “
Guiding and Sustaining Service Delivery (MAP-5)
Traditionally, guiding and sustaining service delivery requires FSEs to follow prescribed service processes while ensuring high-quality customer interactions. This involves knowing when and how to collect customer information, adapting responses to customers’ prior knowledge and emotional states, and documenting the interactions accordingly. MAP-5 augments this process by leveraging GenAI’s capability to observe both ongoing and past service interactions. Unlike predictive AI, which relies on static protocols, GenAI dynamically develops and adjusts guidance for FSEs (Murray, Rhymer, and Sirmon 2021). This enables FSEs to perform their routines adaptively rather than adhering to rigid procedures, ensuring that service delivery remains both personalized and efficient. As one interviewee explained, “
Enhancing Service Dialogues (MAP-6)
Solving customer problems during service delivery involves two key routines: adapting and translating existing solutions, and crafting entirely new ones. In these interactions, FSEs may lack advanced communication or language skills to convey solutions effectively. MAP-6 augments these routines by equipping FSEs with GenAI systems that enhance the efficiency and effectiveness of service dialogues. GenAI supports translating technical expertise into clear, context-sensitive communication, shifting interactions away from one-size-fits-all responses toward highly individualized solutions. This ensures that customer communication is not only accurate but also stylistically and contextually appropriate. The adaptability of GenAI in altering the technical depth of solutions based on the customer’s understanding or requirements demonstrates a sophisticated level of customer-centric problem-solving and adaptation of solutions:
Analogically, GenAI’s impact extends to creating and generating solutions, where it synthesizes information from a multitude of past interactions and data sources to offer novel solutions. Especially in terms of response style and structure, FSEs can leverage GenAI’s strengths: “
Crafting Knowledge and Training Data (MAP-7)
Documenting interactions, retaining knowledge, and feeding AI systems by crafting and curating training data have emerged as critical routines in service delivery. Traditionally, FSEs are responsible for capturing and structuring the knowledge gained during problem resolution. However, they often hesitate to fully document their solutions due to three key obstacles: a lack of motivation, limited time, and the absence of standardized documentation templates. MAP-7 augments these routines by leveraging GenAI to reduce the operational burden of documentation. GenAI assists FSEs in capturing and structuring knowledge more intuitively and with less effort, while simultaneously curating training data to improve AI systems. In this way, GenAI complements FSEs by overcoming documentation barriers and fostering the continuous enrichment of both organizational and AI knowledge bases: “
Service Permeation Mechanisms
The MAPs show that augmentation unfolds throughout the service delivery process (i.e., influencing multiple service routines) and across service frontstage and backstage (i.e., various interaction points involving FSEs and customers). While some of the patterns indicate the remaining distinction and transitions between frontstage and backstage via the line of visibility (e.g., MAP-1, MAP-2), the majority of patterns highlight the increasingly permeable boundaries between frontstage and backstage service routines due to the infusion of GenAI (
We propose
The mechanisms of GenAI service permeation.
Figure 2 depicts the MAPs across service routines and their positioning in the frontstage and backstage, separated by the line of visibility. The logic of this framework begins with understanding the underlying service routines, proceeds to identifying and implementing problem–solution pairs that constitute the MAPs, and ultimately translates the impact of GenAI on the service delivery journey at the frontstage through the mechanisms of service permeation. The arrows indicate how the patterns and resulting services permeate service delivery. According to our systematization of the MAPs and our concept of service permeation, we found two recurring instances of service permeation mechanisms:
MAP-enabled service permeation in GenAI-infused service delivery.
Simultaneous Service Permeation Mechanism
The service backstage represents the focal point of GenAI’s greatest potential to impact the customer experience. While prior research has emphasized the AI frontstage (e.g., self-service, chatbots) (McLeay et al. 2021; Schepers et al. 2022), our results foreground the design of human–AI collaborations to leverage contextualization and service adaptation. Accordingly, the GenAI-infused service backstage enables the frontstage of service delivery (Chen, Hsieh, and Chan 2024; Hogreve, Iseke, and Derfuss 2022), with GenAI becoming part of the moments of truth in which customers interact with FSEs and services are realized and delivered. The mechanism of
The results emphasize GenAI’s role in simultaneously assisting FSEs with decision-making and problem-solving: “
Typically, service organizations aim to address GenAI’s limitations (e.g., hallucinations, lack of transparency) by having FSEs in control. However, GenAI-infused services face the challenge of FSEs’ overreliance on GenAI’s advice, driven by LLMs’ highly persuasive conversational capabilities, with ChatGPT serving as a notable example. Although FSEs interact with customers, GenAI’s backstage activities continuously spill into frontstage service interactions and influence customer experiences (e.g., by customizing writing styles and providing emotional support). Thus, initial backstage GenAI usage can indirectly influence the critical moments of truth in service interactions. Hence, service organizations must ensure human control and agency through novel mechanisms of human–AI collaboration: “
Sequential Service Permeation Mechanism
Sequential service permeation is characterized by the sequential delegation of tasks from FSEs to GenAI. It involves the indirect transfer of backstage knowledge and information into frontstage customer experiences, as exemplified by training self-service chatbots to enhance service interactions. Both automating service frontstage tasks (e.g., MAP-1, MAP-2) and augmenting service backstage processes (e.g., MAP-3, MAP-4) through GenAI depend on the quality of the underlying knowledge, data, and technological advancements. Hence, to enable sequential service permeation, a
Emerging
Discussion
As service delivery is increasingly being performed by both FSEs and GenAI, forming hybrid human–AI service teams (Le et al. 2025; Liu et al. 2024; Mortati and Viana Mundstock Freitas 2025), our study advocates for a micro-level, process-centric view of GenAI infusion by investigating service routines. Although the automation of FSE routines is crucial for managing high volumes of customer inquiries, practice and research must explore how to align humans and AI on a dynamic continuum of substitution and complementation (e.g., Blaurock, Büttgen, and Schepers 2024; Koponen et al. 2023; Marinova et al. 2017). Based on a longitudinal qualitative study involving 41 interviews, we provide empirically grounded implications for understanding GenAI’s impact on service routines, addressing both the service frontstage and backstage, as well as the mechanisms that link them (Bock, Wolter, and Ferrell 2020; van Doorn et al. 2023).
In response to RQ1, which explored the
As part of answering RQ2, which asks
Theoretical Implications
Our study makes two main theoretical contributions to the literature on hybrid human–AI service delivery (e.g., De Keyser et al. 2019; Marinova et al. 2017; Mortati and Viana Mundstock Freitas 2025).
Managerial Implications
Our research has important implications for service organizations, managers, and designers aiming to harness the potential of GenAI in customer support and beyond. By introducing MAPs and the mechanisms of service permeation, we provide actionable guidance for integrating GenAI into both frontstage and backstage service routines and managing the continuum of substitution and complementation. Service leaders should design GenAI deployment strategies that emphasize backstage utility while preserving the human touch and controlling quality in customer-facing interactions. Our findings indicate that GenAI delivers contextual value when applied to backstage functions such as summarizing tickets (MAP-2), copiloting decision support (MAP-3), or coaching FSEs during service delivery (MAP-5). Managers should move beyond viewing GenAI as just an automation tool and recognize it as an active service actor, one that collaborates with FSEs in real time, supports decision-making, and continuously enhances service routines through knowledge sharing. With greater autonomy, agentic AI is likely to combine and amplify these patterns, enabling more sophisticated orchestration of GenAI-augmented service routines. Thus, MAPs not only offer a lens for understanding current GenAI-infused services but also serve as a foundation for designing and improving future autonomous agents and multi-agent systems, ensuring their continued relevance in increasingly agentic service contexts.
With its generative and agentic traits, GenAI blurs the boundaries between the service frontstage and backstage, a phenomenon we conceptualize as service permeation. Managing this permeation requires balancing augmentation benefits with human oversight and acknowledging the duality of GenAI’s impact (Ferraro et al. 2024). On the one hand, GenAI systems can produce highly persuasive yet hallucinated outputs, heightening the risk of overreliance during simultaneous service permeation. To mitigate such risks, firms should establish guardrails, including escalation thresholds, transparency protocols, and explanation cues, across autonomous chatbots (MAP-1) and collaborative copilots (MAP-3). Practitioners must also ensure that FSEs critically engage with GenAI recommendations and continuously refine models and data (MAP-7). Conversely, GenAI can itself function as a guardrail by identifying inconsistencies (MAP-5) or inappropriate communication (MAP-6) that might otherwise escape human attention (Henkel et al. 2020; Luo et al. 2021). For example, MAP-7 enhances documentation and data quality, fostering knowledge transfer and model improvement. Managers should therefore regularly assess the extent and quality of service permeation across the delivery process to detect infusion gaps and guide strategic adjustments that sustain human accountability and service excellence.
As service permeation becomes a critical driver of service performance, organizations should invest in workforce development initiatives that equip FSEs to collaborate effectively with GenAI systems. Decision-making by GenAI systems, and their reliance on curated frontline data, leads FSEs to take on expanded roles as co-creators and knowledge curators (Li et al. 2024). This includes developing judgment on when to act on or override AI recommendations and establishing routines for documenting, annotating, and refining service knowledge, practices that drive sequential service permeation and support continuous system learning. While our findings suggest that GenAI increasingly augments interpersonal communication and emotional expression (e.g., Brynjolfsson, Li, and Raymond 2025; Henkel et al. 2020), the skill demands for FSEs are evolving. Greater emphasis is now placed on domain knowledge and technical proficiency to ensure accurate service delivery. In addition, competencies such as AI literacy, context engineering, and prompt engineering are becoming essential for effectively managing and leveraging GenAI-infused services (Knoth et al. 2024). In conclusion, service organizations must equip FSEs with the skills to manage, guide, and improve GenAI interactions.
Limitations
While our study provides valuable contributions to the literature and practice, certain limitations must be acknowledged. A key limitation of this study is its empirical scope, as it focuses on four organizations within a single industry. While our approach followed qualitative research criteria for reliability and validity, the sampling limits the generalizability of the findings. In particular, we focused on technical customer requests, a sector promising the potential of GenAI infusion. Broadening the scope of this research to include various customer service domains, such as sales, general non-technical customer care, and contact centers, would shed light on the adaptability of GenAI-augmented service practices. Moreover, despite the variety of service organizations, digital services, and service channels, the derived patterns are not exhaustive. Our study’s reliance on qualitative data and subjective perspectives introduces an inherent bias, and the small number of AI experts consulted may not fully represent the field. For example, our interviews did not explicitly address multi-agent systems and agentic AI (Acharya, Kuppan, and Divya 2025), which only began to emerge and gain relevance for scaling GenAI infusion after our second interview series. Nevertheless, we argue that the identified MAPs are not made obsolete by technological progress. Instead, they capture fundamental augmentation logics that remain relevant as agentic AI evolves.
Finally, the timing of our first interview series, conducted during the COVID-19 pandemic, might have influenced the results, as the pandemic temporarily increased demand for support. However, according to a recent survey by McKinsey (2024), it appears that the heightened demand for customer requests has persisted despite the end of the pandemic and the use of chatbots. Although we only took into consideration the current state-of-the-art GenAI models (i.e., GPT-4) (2023–2024) and might be challenged by new technological breakthroughs (e.g., AI agents, agentic AI, and multi-agent systems) (Acharya, Kuppan, and Divya 2025; Pan et al. 2025), our results offer other researchers a basis for managing future emerging technologies.
Avenues for Future Research
Our contributed empirical foundation, comprising the MAPs and the mechanisms of service permeation, provides a basis for guiding future research on hybrid human–AI service delivery. Future research should conduct an in-depth exploration of specific problem-solution pairs and augmentation patterns, especially with a focus on assessing their practical effectiveness across different service settings. Each MAP offers a separate avenue for future research, illustrated by the exemplary research questions in Table 3. A promising area involves the analysis of prompts formulated by FSEs. Such research could comprise collecting, categorizing, and evaluating these prompts and recurring prompt patterns. This approach would deepen understanding of FSE–GenAI collaboration and contextualize the MAPs and emerging routines.
Furthermore, service permeation and the two identified mechanisms open promising avenues for future research and call for deeper empirical and theoretical exploration.
In sum, our study provides a process-centric foundation for understanding how GenAI augments FSEs’ service routines, offering empirically grounded patterns and mechanisms that future research can build upon.
Supplemental Material
sj-docx-1-jsr-10.1177_10946705251414283 – Supplemental material for GenAI-Infused Service Delivery: Micro-Level Augmentation Patterns at the Service Frontline
Supplemental material, sj-docx-1-jsr-10.1177_10946705251414283 for GenAI-Infused Service Delivery: Micro-Level Augmentation Patterns at the Service Frontline by Philipp Reinhard, Mahei Manhai Li, Christoph Peters, Andreas Janson and Jan Marco Leimeister in Journal of Service Research
Supplemental Material
sj-docx-2-jsr-10.1177_10946705251414283 – Supplemental material for GenAI-Infused Service Delivery: Micro-Level Augmentation Patterns at the Service Frontline
Supplemental material, sj-docx-2-jsr-10.1177_10946705251414283 for GenAI-Infused Service Delivery: Micro-Level Augmentation Patterns at the Service Frontline by Philipp Reinhard, Mahei Manhai Li, Christoph Peters, Andreas Janson and Jan Marco Leimeister in Journal of Service Research
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the German Federal Ministry of Education and Research (BMBF) and supervised by PTKA (Project HISS, 02K18D060).
Supplemental Material
Supplemental material for this article is available online.