AI-Enabled Sales Coaching and Generation Z Engagement

Managing younger employees is a pressing organizational concern, not just a workforce niche issue (Pichler et al., 2021). Sales organizations are implementing AI-enabled coaching and sales enablement systems to improve frontline effectiveness. However, these same systems may intensify frontline employees’ behavioral visibility to supervisors and increase managerial control over calls, activity levels, and performance metrics, potentially reducing trust and engagement (Benk et al., 2025; Dzreke & Dzreke, 2025; Jarrahi et al., 2021; van Zoonen et al., 2025).

A key knowledge gap exists regarding whether Generation Z salespeople in customer-facing roles perceive AI-enabled coaching as autonomy-supportive or as controlling oversight. These interpretations are central because they influence work engagement (Gabrielova & Buchko, 2021; Saks, 2022). Although existing research has examined AI in sales, algorithmic management, and employee engagement separately, few studies have integrated these streams to explain the development-control tension in AI-enabled coaching and the role of autonomy support and leader-member exchange (Hagger & McAnally Star, 2026; Jarrahi et al., 2021; Rockstuhl, Dulebohn, Ang & Shore, 2020; Ryan & Deci, 2020). These factors critically shape how this tension affects engagement in frontline sales contexts.

The purpose of this structured integrative review is to examine how AI-enabled coaching may influence work engagement among Generation Z employees in customer-facing sales roles. The review synthesizes literature on perceived developmental coaching, autonomy support, and leader-member exchange. Generation Z frontline sales employees are the focus because AI-enabled coaching and sales technologies are increasingly embedded in customer-facing sales contexts, while autonomy, developmental feedback, and supportive leader relationships remain central to their work experiences (Dzreke & Dzreke, 2025; Gabrielova & Buchko, 2021; Jarrahi et al., 2021).

Specifically, this review clarifies the motivational mechanism and relational conditions linking AI-enabled coaching to engagement in frontline sales. By integrating research on AI in sales, self-determination theory, and leader-member exchange, it explains how perceived developmental AI coaching can enhance engagement through autonomy support, with leader-employee relationship quality influencing whether the technology is perceived as supportive or controlling (Hagger & McAnally Star, 2026; Rockstuhl et al., 2020; Ryan & Deci, 2020; Saks, 2022).

This article contributes to the literature in three interrelated ways. First, it redirects attention to the employee perspective in AI-enabled selling, showing that the consequences of coaching technologies depend on employees’ interpretations, not merely on their presence (Høgevold et al., 2025; Jarrahi et al., 2021). Second, this integration is necessary because algorithmic management research explains how digital systems can intensify control, while engagement research explains employee involvement but often leaves the relational interpretation of AI-enabled coaching underdeveloped (Breevaart, Bakker, Demerouti & Derks, 2021; Hagger & McAnally Star, 2026; Jarrahi et al., 2021; Rockstuhl et al., 2020; Ryan & Deci, 2020). Third, it offers a review-based framework that differs from existing algorithmic management and engagement studies by explaining not only whether AI-enabled coaching affects employees, but how its meaning is filtered through autonomy support and leader-member exchange. This framework provides scholars and managers a more precise basis for evaluating the human consequences of AI-supported coaching systems (Benk et al., 2025; Saks, 2022). Ultimately, the article argues that AI-enabled coaching introduces a development-control tension, with Generation Z engagement outcomes contingent on autonomy support and leader-member exchange.

In this review, AI-enabled coaching refers specifically to digital coaching systems that use sales data, conversational analysis, or LLM-supported feedback to guide salesperson development. AI-enabled sales technologies support prospecting, interaction analysis, customer insight generation, and coaching-related feedback loops, yet only the more recent forms approximate LLM-supported coaching rather than conventional analytics or automation (Davenport et al., 2020; Hart, 2025a). AI adoption is logical because sales organizations have long valued tools that sharpen skills and support customer adaptation, yet LLM-enabled coaching adds a newer layer of personalized, language-based guidance that earlier sales technology studies could only partially anticipate (Mehrabanpour et al., 2025; Rangarajan et al., 2021).

However, AI-enabled coaching should be distinguished from algorithmic monitoring more broadly. Whereas algorithmic control systems primarily track, evaluate, and discipline worker behavior, AI-enabled coaching uses similar data streams but frames them as individualized feedback, guidance, and real-time learning support for performance improvement (Chawla & Goyal, 2022; Jarrahi et al., 2021; van Zoonen et al., 2025; Watanabe & Ho, 2025). The distinction matters. Even when presented as developmental, AI-enabled coaching still depends on infrastructures of observation and evaluation that can increase behavioral visibility and managerial control (Primož et al., 2025; van Zoonen et al., 2025).

This tension is particularly relevant in customer-facing sales, where frontline work is highly exposed to metrics, dashboards, rankings, and performance comparisons. Digital sales enablement tools can improve effectiveness, yet the same systems can tighten managerial oversight by making more aspects of selling observable and comparable (Chawla & Goyal, 2022). Research on algorithmic management further indicates that digital systems can alter how workers experience authority, discretion, and accountability (Hart, 2025a; Jarrahi et al., 2021; van Zoonen et al., 2025). Consequently, the effects of AI-enabled coaching are unlikely to be uniform and depend on how employees interpret the role and function of that coaching.

Generation Z employees are often characterized as digitally fluent, feedback-oriented, and less tolerant of rigid managerial routines that ignore autonomy or developmental growth (Gabrielova & Buchko, 2021; Hart, 2025b; Ozkan & Solmaz, 2021; Pichler et al., 2021). Although these traits should not be overgeneralized, the literature consistently suggests that younger employees tend to expect more immediate communication, more individualized development, and more transparent relationships with leaders than many traditional workplace systems were designed to provide (Gabrielova & Buchko, 2021).

These preferences are especially salient in customer-facing sales roles. Sales work is performance-intensive, emotionally demanding, and externally visible. Success often depends on employees’ ability to balance formal metrics with responsive customer interactions (Chaker et al., 2025; Watanabe & Ho, 2025). Research on employee engagement and supportive leadership suggests that developmental climates and employee-centered management are associated with higher involvement and more positive work outcomes (Saks, 2022). For younger employees, supportive leadership and meaningful developmental feedback are likely to matter more, not less (Hart, 2025b). Likewise, the risk of disengagement increases when monitoring is perceived as intrusive.

Work engagement is therefore an appropriate focus for this review because it reflects the extent to which employees bring energy, involvement, and attention to their work, rather than simply complying with formal demands (Saks, 2022). In customer-facing sales, engagement is consequential because quality of effort, attentiveness, and adaptive behavior can influence relationship outcomes and customer experience (Homburg, Theel & Hohenberg, 2020; Park & Hur, 2025; Watanabe & Ho, 2025). Engagement helps explain how performance can be sustained rather than merely extracted.

Self-determination theory provides a key analytical lens for this review because it explains how work contexts shape internalized motivation by supporting or frustrating autonomy, competence, and relatedness (Hagger & McAnally Star, 2026; Ryan & Deci, 2020). Within organizational research, support for autonomy is especially important, reflecting whether employees experience room for volition, judgment, and self-endorsed action, rather than external pressure or rigid control (Olafsen, Deci & Halvari, 2021; Slemp, Kern & Patrick, 2021).

In sales contexts, feedback and control systems can clarify expectations and enhance performance, yet they can also reduce perceived individual discretion when monitoring is emphasized over developmental support (Mehrabanpour et al., 2025). The broader work motivation literature points in a similar direction. Employees tend to show stronger engagement when leadership and work design support autonomy and competence rather than inhibiting them through excessive regulation (Burrell, Nobles, Welch, Prunella, Hart, Eke & Harris, 2026; Hagger & McAnally Star, 2026; Olafsen et al., 2021; Saks, 2022).

Consequently, AI-enabled coaching is unlikely to influence engagement simply by its presence. Its effects are more likely to arise from employees interpreting the system as either expanding or constraining autonomy. This observation is significant because it means that the same AI-supported coaching practice can signal very different meanings. Autonomy is treated here as the primary proximal mechanism because AI-enabled coaching most directly alters employees’ discretion, judgment, and sense of control over how work is carried out, although competence and relatedness may also matter and should be tested in future research (Jarrahi et al., 2021; Slemp et al., 2021; van Zoonen et al., 2025). Autonomy support therefore functions as the primary discriminating motivational link in AI-enabled coaching contexts.

While self-determination theory explains the motivational mechanisms of the problem, it does not fully account for the relational context in which AI-enabled coaching occurs. Leader-member exchange theory addresses this gap by focusing on the quality of relationships between employees and leaders, including trust, support, mutual respect, and fairness (Breevaart et al., 2021; Rockstuhl et al., 2020). Employees typically encounter AI systems through leaders who introduce, frame, interpret, and apply them, making relationship quality a key factor in how coaching is perceived.

Research on leader-member exchange suggests that high-quality relationships are associated with stronger trust and more positive employee outcomes, including engagement (Breevaart et al., 2021; Rockstuhl et al., 2020; Saks, 2022). Research on trust in AI aligns with this perspective, indicating that acceptance depends not only on technical performance but also on legitimacy, transparency, and relational confidence (Benk et al., 2025; Jazairy et al., 2025). In customer-facing sales settings, supervisor relationships influence whether performance feedback is experienced as helpful guidance (when the relationship is supportive) or as a threat (when the relationship is weak) (Hart, 2025b; Homburg et al., 2020; Høgevold et al., 2025; Mehrabanpour et al., 2025). This illustrates the relational condition at play here.

When leader-member exchange is high, AI-enabled coaching is more likely to be interpreted as developmental support because employees have reason to trust the system’s intent, framing, and application (Breevaart et al., 2021; Rockstuhl et al., 2020; Xie et al., 2025). Conversely, when the relationship is weak, the same system is more likely to be interpreted as impersonal oversight or disguised control (Benk et al., 2025; Jarrahi et al., 2021). Thus, leader-member exchange conditions moderate the motivational impact of AI-enabled coaching.

Altogether, the literature suggests a coherent explanatory pattern: AI-enabled coaching is neither inherently engaging nor disengaging; its effects depend on employee interpretation. When employees perceive AI-supported coaching as developmental, it is likely to strengthen autonomy support by signaling competence-building, useful guidance, and room for individual judgment (Hagger & McAnally Star, 2026; Høgevold et al., 2025; Slemp et al., 2021). Conversely, when the same systems are perceived as surveillance, ranking, or algorithmic control, autonomy support is less likely to emerge, and engagement may decline (Jarrahi et al., 2021; Primož et al., 2025; van Zoonen et al., 2025).

Leader-member exchange shapes this interpretive process by influencing the relational context in which AI-enabled coaching is delivered (Breevaart et al., 2021; Rockstuhl et al., 2020). In this framework, autonomy support functions as the primary motivational mechanism, while leader-member exchange serves as the relational condition that strengthens or weakens the pathway from perceived developmental AI coaching to work engagement. The proposed framework is thus not a simple technology adoption model but an employee interpretation model grounded in motivation and relationship quality.

From this integration, three review questions emerge. First, how has previous research conceptualized AI-enabled coaching in customer-facing sales settings, particularly regarding development versus control? Second, what does the literature suggest about the role of autonomy support in explaining how AI-enabled coaching influences work engagement among Generation Z employees? Third, how does leader-member exchange shape the interpretation of AI-enabled coaching in frontline sales environments? The study design, presented in the next section, facilitates answering these questions.

This article adopts a structured integrative review design. This approach is appropriate because the focal problem is interdisciplinary, encompassing AI in marketing and sales, algorithmic management, self-determination theory, leader-member exchange, Generation Z work expectations, and employee engagement. A narrower systematic review risks fragmenting the problem too acutely, whereas an unstructured narrative review may lack sufficient analytic rigor. The integrative approach aligns with the study’s objective of synthesizing multiple streams into a single explanatory framework.

The review was constructed from peer-reviewed sources selected for their direct relevance to the article’s focal problem. This study does not claim to provide a fully exhaustive or continuously updated review of artificial intelligence, sales, or Generation Z work preferences, a limitation that matters because AI-enabled coaching is changing rapidly. Instead, it applies structured integrative review logic to a bounded body of peer-reviewed literature that identifies and explores the development-control tension in AI-enabled sales coaching and its relationship to autonomy support, leader-member exchange, and work engagement.

The source base was assembled from 33 retained references relevant to the article’s major domains: AI-enabled coaching in sales organizations; Generation Z in customer-facing sales roles; self-determination theory and autonomy support; leader-member exchange as a relational condition; and broader work on algorithmic management, trust, and employee engagement. Searches used combinations of the following terms: “AI-enabled coaching”, “sales enablement”, “conversational intelligence”, “algorithmic management”, “Generation Z”, “customer-facing sales”, “work engagement”, “autonomy support”, “self-determination theory”, “leader-member exchange”, and “trust in AI”. Priority was given to sources published from 2020 onward, while AI-specific claims were weighted toward post-2022 scholarship because LLM-enabled coaching differs from earlier analytics and automation tools. Earlier works were retained only for foundational theory, trust, and sales context, while claims about LLM-enabled coaching were interpreted through newer AI and algorithmic management scholarship.

The resulting corpus was purposive rather than exhaustive, emphasizing conceptual relevance and theoretical fit while limiting the review’s ability to represent every recent development in AI-enabled coaching. Sources were retained when they offered direct explanatory value for the central question: how AI-enabled coaching in frontline sales may be interpreted by Generation Z employees and how that interpretation may shape work engagement through autonomy support and leader-member exchange.

Sources were included when they were published in English, directly addressed one of the article’s substantive domains, and contributed conceptual or empirical insight into AI-enabled coaching, Generation Z sales work, autonomy support, leader-member exchange, trust in AI, or work engagement. The review also incorporated peer-reviewed studies on trust in AI and algorithmic management that clarified how digitally mediated systems are interpreted by employees in monitored environments (Benk et al., 2025; Hart, 2025a; Jarrahi et al., 2021; Jazairy et al., 2025; van Zoonen et al., 2025).

Sources were excluded when they focused primarily on technical system design, offered broad generational commentary without empirical or theoretical grounding, addressed sectors with limited transfer to customer-facing sales, or discussed leadership and engagement without meaningful links to technology-mediated coaching or frontline performance. Studies examining leadership, work design, or engagement without meaningful links to technology-mediated coaching or frontline performance were treated as peripheral (Burrell et al., 2026). These boundaries maintained alignment with the review’s primary explanatory focus.

Each retained source was coded in a review matrix capturing publication domain, focal phenomenon, theoretical lens, motivational mechanism, relational construct, and relevance to customer-facing sales. The matrix facilitated comparison across domains and helped identify recurring tensions, especially the contrast between developmental support and control-surveillance interpretations of AI-enabled coaching. Coding emphasized how studies described employee discretion, perceived support, trust, and engagement, as these concepts were central to the review questions.

The synthesis proceeded in three stages. First, sources were grouped by substantive domain and analyzed for conceptual relevance to AI-enabled coaching, employee interpretation, autonomy support, trust, and engagement. Second, literature across domains was compared to identify recurring tensions, especially the contrast between developmental support and control-surveillance interpretations (Jarrahi et al., 2021; Primož et al., 2025; van Zoonen et al., 2025). Third, themes were synthesized into a conceptual explanation linking perceived developmental AI coaching to work engagement through autonomy support, while positioning leader-member exchange as a relational condition shaping interpretation (Hagger & McAnally Star, 2026; Rockstuhl et al., 2020; Slemp et al., 2021). The aim was not to estimate effect sizes or conduct a meta-analysis, but to develop a conceptually disciplined framework from a bounded and theoretically relevant literature base.

This procedure was necessary because the literature does not converge on a single established model. Relevant insights were dispersed across sales research, organizational behavior, and management studies. Thematic synthesis thus served a constructive role by identifying what these streams imply when considered collectively rather than individually (Davenport et al., 2020; Jarrahi et al., 2021).

The first theme is that sales and marketing literature commonly frames AI-enabled coaching systems as developmental tools intended to improve frontline effectiveness. Sales enablement technologies, conversational intelligence platforms, and performance analytics systems are frequently discussed as mechanisms that make feedback more timely, specific, and actionable for customer-facing employees (Dzreke & Dzreke, 2025; Hart, 2025b; Watanabe & Ho, 2025). From this perspective, AI-assisted coaching appears to extend the reach and consistency of managerial support.

At the same time, the broader literature on algorithmic management suggests that these same systems also expand behavioral visibility by making employee actions easier to capture, compare, and evaluate (Jarrahi et al., 2021; Primož et al., 2025; van Zoonen et al., 2025). Related research on workplace surveillance and work design further indicates that digital systems can improve coordination while amplifying perceptions of monitoring and constraint (Ball, 2021). This tension recurs throughout the reviewed literature, suggesting that AI-enabled coaching functions not only as a developmental resource but also as a monitoring architecture. This dual character helps explain why the same technology may be welcomed in one setting and resisted in another.

The second theme is the relative consistency of the Generation Z literature regarding a set of workplace expectations highly relevant to frontline sales roles. Across the reviewed studies, Generation Z employees are commonly associated with stronger preferences for autonomy, timely developmental feedback, meaningful work, and transparent relationships with leaders (Gabrielova & Buchko, 2021; Pichler et al., 2021). Although the literature does not portray this cohort as uniform, it indicates a recurring pattern in how younger employees evaluate work environments (Ozkan & Solmaz, 2021; Pichler et al., 2021).

These preferences become especially salient in customer-facing sales contexts, where performance pressure is high and employee discretion may be constrained by targets, visibility, and close supervision (Chaker et al., 2025; Park & Hur, 2025; Watanabe & Ho, 2025). Research on engagement suggests that developmental climates and supportive human resource practices are associated with stronger employee involvement and adaptability (Saks, 2022). Accordingly, the literature suggests that Generation Z employees may be especially attuned to whether AI-enabled coaching is experienced as developmental support or as an extension of control. This finding links broader generational workforce discussions to the frontline sales environment.

The third theme is that autonomy support appears to be the clearest motivational mechanism explaining how AI-enabled coaching may influence work engagement, because these systems most directly affect employees’ discretion, judgment, and sense of control over how work is carried out, even though competence and relatedness may also operate as secondary mechanisms (Hagger & McAnally Star, 2026; Olafsen et al., 2021; Ryan & Deci, 2020; Slemp et al., 2021).

This review reveals that autonomy support distinguishes coaching perceived as developmental from coaching perceived as controlling. When AI-enabled coaching is interpreted as guidance that improves competence while preserving employee judgment, it is more likely to be associated with stronger perceptions of autonomy support (Mehrabanpour et al., 2025; Slemp et al., 2021). Conversely, when the same system is interpreted as surveillance, ranking, or narrow performance control, such perceptions are less likely to emerge (Jarrahi et al., 2021; van Zoonen et al., 2025).

The fourth theme is that the quality of the leader-employee relationship appears to shape how technology-mediated coaching is interpreted. Research on leader-member exchange indicates that high-quality exchange relationships are associated with stronger trust, mutual respect, and more positive responses to guidance and expectations (Breevaart et al., 2021; Rockstuhl et al., 2020). Related research on trust in artificial intelligence similarly suggests that acceptance depends not only on technical performance but also on legitimacy, transparency, and relational confidence (Benk et al., 2025; Jazairy et al., 2025).

Taken together, these studies suggest that AI-enabled coaching is unlikely to be interpreted uniformly across leader-employee relationships. When leader-member exchange is high, employees are more likely to perceive AI-enabled coaching as developmental support because they trust the leader’s intent and the system’s application (Breevaart et al., 2021; Rockstuhl et al., 2020). When leader-member exchange is weak, the same system may be interpreted as distant, impersonal, or controlling (Jarrahi et al., 2021). In that sense, leader-member exchange functions less as a parallel explanation and more as a relational condition shaping the meaning of the coaching process.

Synthesizing the four themes suggests a conditional explanatory pattern in which AI-enabled coaching is most likely to support work engagement when it is perceived as developmental, when that perception enhances autonomy support, and when the leader-employee relationship provides sufficient trust for the system to be interpreted as supportive rather than controlling (Benk et al., 2025; Hagger & McAnally Star, 2026; Rockstuhl et al., 2020; Slemp et al., 2021).

This framework links perceived developmental AI-enabled coaching to work engagement through autonomy support, with leader-member exchange shaping the relational context in which that pathway develops (Hart, 2026). This framework does not suggest that AI-enabled coaching is uniformly beneficial or uniformly harmful. Rather, it proposes that the consequences of AI-supported coaching depend on how employees interpret the system and on the leader relationship through which that interpretation is filtered.

The first theoretical implication is that the consequences of AI-enabled coaching are better understood through an employee interpretation lens than through a simple adoption lens. Earlier sales technology literature emphasized capability enhancement, performance visibility, and process improvement, but post-LLM coaching raises a sharper interpretive issue because feedback can now be personalized, conversational, and creatively assistive while still functioning as managerial control (Davenport et al., 2020; Dzreke & Dzreke, 2025). Although these themes are important, they do not fully explain why the same system may produce different employee responses. The present review suggests that employee interpretation is central to understanding these differences. AI-enabled coaching appears to influence engagement not because technology is present, but because employees infer support, discretion, or control from the ways in which the technology is deployed (Jarrahi et al., 2021; Primož et al., 2025).

The second theoretical implication is that self-determination theory provides a more precise explanatory mechanism than broad motivational language alone. The reviewed literature consistently identifies autonomy support as the most plausible mechanism linking perceived developmental coaching to work engagement (Hagger & McAnally Star, 2026; Olafsen et al., 2021; Ryan & Deci, 2020; Slemp et al., 2021). This narrows the conceptual field by allowing the argument to move beyond the general claim that supportive coaching benefits employees. Instead, it suggests that AI-enabled coaching is most likely to strengthen engagement when it is experienced in ways that support volition and competence rather than undermining them (Mehrabanpour et al., 2025; Saks, 2022).

The third theoretical implication is that leader-member exchange strengthens the framework by introducing a relational boundary condition. High-quality exchange relationships appear to provide the trust context within which AI-enabled coaching can be interpreted as developmental rather than controlling (Breevaart et al., 2021; Rockstuhl et al., 2020; Xie et al., 2025). This prevents the framework from becoming overly individualistic and enhances the article’s contribution by showing that AI-enabled coaching in frontline sales is not solely a work design issue but also a leadership issue (Benk et al., 2025; Burrell et al., 2026; Jarrahi et al., 2021; Rockstuhl et al., 2020).

The article contributes to marketing and sales scholarship by restoring attention to the employee side of AI-enabled selling. Much of the discussion surrounding AI in sales has focused on organizational capability, data use, and customer-facing efficiency (Davenport et al., 2020; Høgevold et al., 2025; Deng, 2025). Although these issues are important, frontline value creation still depends on the salesperson motivation, attention, and sustained involvement (Chaker et al., 2025; Park & Hur, 2025; Watanabe & Ho, 2025). This review suggests that the human consequences of AI-enabled coaching deserve a more central position in sales scholarship than they have received to date.

The article also contributes by integrating bodies of literature that are often treated separately. Research on AI in sales, algorithmic management, self-determination theory, and leader-member exchange is valuable independently; however, when examined in isolation, these streams provide only partial explanations of the focal problem. This review integrates them into a unified explanatory framework because no single stream sufficiently explains the development-control tension, the motivational pathway, and the relational conditions shaping Generation Z engagement in AI-enabled sales coaching (Hagger & McAnally Star, 2026; Dzreke & Dzreke, 2025; Jarrahi et al., 2021; Rockstuhl et al., 2020). In doing so, it offers the field a clearer conceptual basis for understanding how AI-enabled coaching may shape engagement among Generation Z employees in customer-facing sales roles.

The practical implication of this review is that organizations should be cautious about assuming that greater visibility automatically produces greater salesperson learning, skill development, or judgment quality. The literature does not support that assumption. AI-supported feedback systems may improve consistency and insight, but they may also generate resistance if employees perceive them as surveillance or as constraints on discretion (Ball, 2021; Hart, 2025b; Jarrahi et al., 2021; van Zoonen et al., 2025). This is especially relevant in frontline sales environments, where performance pressure is already high and employee engagement is closely tied to interaction quality and customer outcomes (Homburg et al., 2020; Itani & Hollebeek, 2021; Park & Hur, 2025).

The reviewed literature suggests that managers may need to do more than simply implement AI-enabled tools. They may also need to frame those tools as developmental, preserve room for salesperson judgment, and maintain human dialogue around the interpretation of feedback (Hart, 2025b; Høgevold et al., 2025). In this sense, the growing availability of data and performance metrics does not appear to reduce the need for leadership so much as reconfigure it. High-quality leader-member exchange may help employees interpret AI-enabled coaching as support rather than control, which in turn suggests that technology implementation and relational leadership should be treated as linked managerial tasks rather than separate endeavors (Breevaart et al., 2021; Burrell et al., 2026; Rockstuhl et al., 2020).

The article is limited by its review-based design and by the literature it incorporates. The review prioritizes customer-facing sales, employee interpretation, motivation, and leader relationships. As a result, it excludes some adjacent work on broader digital transformation, sector-specific AI implementation, and purely technical system design (Davenport et al., 2020). This necessarily shapes the scope of the conclusions and limits their generalizability.

The article is also constrained by the rapid pace of AI development, since LLM-enabled coaching tools may change faster than peer-reviewed research can evaluate them. Consequently, portions of the framework depend on integration across related domains rather than on a large body of directly aligned empirical studies. Nonetheless, this limitation enhances the value of the present synthesis by identifying and integrating emerging conceptual patterns.

Future research should empirically test and periodically update the framework across customer-facing sales settings, especially where LLM-enabled coaching provides highly personalized feedback, assists creative selling responses, or increases monitoring intensity (Davenport et al., 2020; Dzreke & Dzreke, 2025; Huang & Rust, 2021). Mixed-method designs would be especially useful because they could examine both measurable relationships and employee interpretations in greater depth (Benk et al., 2025; Jarrahi et al., 2021). Researchers should also investigate whether the same mechanism applies to outcomes beyond engagement, such as adaptive selling, customer relationship quality, and frontline service performance (Homburg et al., 2020; Itani & Hollebeek, 2021; Park & Hur, 2025). These extensions would help determine the broader applicability of the proposed framework.