Abstract
Orientation: Investment professionals operate in high-pressure environments characterised by market volatility, demanding performance expectations and limited access to support structures.
Research purpose: These conditions increase stress, impair emotional regulation and judgement and weaken individuals’ ability to pursue well-being. While organisational coaching could help, it is costly, time-consuming and difficult to scale, leaving organisations with limited mechanisms to help employees manage the psychological strain associated with high-performance financial work.
Motivation for the study: This study therefore investigated whether artificial intelligence (AI) coaching could offer practical, affordable support for well-being and goal attainment within the asset management industry.
Research approach/design and method: This qualitative study used thematic analysis of semi-structured interviews with 10 South African investment professionals who engaged with an AI coaching chatbot over 4 weeks.
Main findings: Four themes were identified: Ease of use and accessibility; reflection, self-awareness and well-being; accountability and goal attainment and tensions between AI coaching and human coaching. The findings show that the chatbot supported participants by prompting reflective thinking, enhancing emotional awareness, offering structured accountability and providing immediate support during stressful moments. Limitations emerged around the absence of empathy, richer insight and the more subtle understanding typically offered by human coaches.
Practical/managerial implications: These insights suggest that AI coaching could be effective when used within a blended developmental approach, where AI delivers continuous, low-cost support and human coaches facilitate deeper emotional and interpersonal work.
Contribution/value-add: The study contributes practical guidance for organisations seeking scalable ways to strengthen resilience, performance and well-being in high-pressure investment environments.
Keywords: AI coaching; artificial intelligence; well-being; asset management; artificial intelligence coaching.
Introduction
Asset management involves the strategic oversight of financial assets by institutions such as pension funds, government institutions and corporations (Clark, 2000). The objective of the asset managers is to generate the highest possible returns to enhance the value of the initial investment while simultaneously minimising losses (Zhang, 2024). The process is fundamental in determining the distribution and management of financial resources within the global financial markets (Clark, 2000).
In the asset management industry, investment professionals face intense stress because of the high demands of meeting client performance targets under fluctuating market conditions, which also expose firms to pro-cyclical investor behaviour and liquidity pressures (Fricke & Fricke, 2017). Research highlights that workplace pressures create environments that undermine employee well-being and productivity (Chang, 2024). These pressures can impair their ability to achieve wellness goals and ultimately well-being, as it negatively affects decision-making, overall performance and long-term health, leading to burnout, impaired judgement, mental health strain and reduced career sustainability (Chang, 2024).
A proven support mechanism in this context is organisational coaching (Grant et al., 2009). Sorensen (2023) describes coaching as a collaborative partnership in which the coach and coachee engage in a thoughtful and creative process designed to help coachees reach their potential. This partnership supports the coachee’s growth through a supportive relationship that enhances self-awareness and personal development. Central to coaching is goal attainment. Goal attainment is defined as the process where an individual’s expectation of successfully achieving a goal motivates and sustains goal-directed behaviour (Senko & Hulleman, 2013). Goal-setting theory holds that establishing concrete goals would provide a measure against which individuals assess their performance and derive satisfaction (Locke & Latham, 2002). These goals also direct an individual’s effort, establishing a positive relationship between the specific goal and enhanced performance (Latham & Locke, 2007). Tóth et al. (2018) explain that achieving an objective enhances well-being and is a predictor of overall wellness. Achieving a goal is anticipated to bring pleasure and satisfaction; the process of setting goals therefore serves as a motivator and driver for action (Grant, 2012) and could help to address the challenges that asset managers face.
However, the reach of traditional coaching is limited by accessibility and cost issues (Terblanche et al., 2022a). This is compounded by a general underutilisation within organisations, often because of a lack of maturity and understanding about how to effectively implement coaching strategies (Mihiotis & Argirou, 2016). Together, these limitations point to the need for more flexible and scalable coaching approaches, creating space for artificial intelligence (AI) to offer a complementary form of support.
The implementation of AI in coaching, particularly through AI chatbots, has been proposed as a viable alternative (Terblanche, 2024). It offers a scalable, cost-effective solution (Terblanche, 2024). Diller et al. (2024) describe AI coaching as the combination of AI technologies with traditional coaching practices. It utilises machine learning and natural language processing to replicate and enhance human coaching capabilities (Diller et al., 2024).
Santhosh et al. (2023) showed that AI chatbots can enhance health management and well-being via goal attainment. Their research demonstrated that AI chatbots provide personalised and accessible health management resources. In doing so, these chatbots empower users to proactively manage their health, make informed decisions and access resources efficiently. Beyond healthcare, AI chatbots have been implemented across industries such as banking, retail and healthcare to improve efficiency and personalisation (Chong et al., 2021). Despite these developments, the integration of AI into coaching is a recent development and the impact and effectiveness in this field remain largely unexplored (Terblanche et al., 2022b). To the best of current knowledge, no studies have investigated the use of AI chatbot coaching in the asset management industry. What is therefore not known is how AI coaching may address the unique challenges investment professionals face in balancing performance, resilience and well-being. This gap highlights a critical opportunity to explore how AI coaching could support both well-being and overall goal attainment in this sector.
Literature review
Challenges in the asset management industry
Investment professionals operate in environments characterised by intense performance pressure, continuous market volatility and heightened expectations from clients and regulators. Recent empirical research focusing on financial professionals indicates that excessive workload, tight deadlines, limited managerial support and career uncertainty contribute significantly to work-related stress within the financial services sector (Kutebayev et al., 2024). Research in behavioural finance has highlighted how physiological stress markers and affective states, such as fear and anger, can significantly impair information-search behaviour and exacerbate investment biases (Nofsinger et al., 2021; Wynes, 2021). Such stressors place substantial cognitive and emotional demands on professionals and, if left unmanaged, can impair judgement, decision-making quality and sustained performance.
More broadly, contemporary organisational research consistently demonstrates that prolonged workplace stress is associated with negative outcomes for both individuals and organisations, including burnout, reduced mental health, lower engagement and diminished productivity (Shiri et al., 2023). While moderate levels of stress may have a motivating effect, sustained exposure to high demands without adequate resources or support increases the risk of emotional exhaustion and psychological strain (Taris, 2024). In high-pressure professional contexts such as asset management, these risks are further amplified by performance-based cultures and constant exposure to uncertainty.
Evidence also suggests that workplace stress is often compounded by financial and job-related concerns, with recent studies demonstrating a clear link between financial well-being and burnout (Botha et al., 2024). This is particularly relevant in asset management environments, where performance volatility, remuneration structures, and job insecurity can interact with psychological stressors to undermine overall well-being.
Despite growing awareness of workplace mental health, many organisations continue to rely on traditional support mechanisms that are underutilised because of stigma, confidentiality concerns, time constraints or a lack of perceived relevance (Shiri et al., 2023). As a result, employees may delay seeking support until stress has already escalated. Recent intervention research therefore emphasises the need for proactive, accessible, and integrated approaches to well-being that can be embedded into daily work practices and provide timely support before stress becomes chronic (Taris, 2024). Organisational coaching offers a potential solution.
Coaching and goal-setting as support mechanisms
Coaching is commonly defined as a collaborative, goal-directed developmental process that integrates structured, solution-focused techniques with cognitive-behavioural methods (Grant et al., 2009). Typically applied in non-clinical settings, coaching aims to enhance individual performance, facilitate personal growth, support goal attainment and promote overall well-being (Grant et al., 2009). Extending this definition, Bachkirova et al. (2018) describe coaching as a one-on-one interaction designed to support enduring personal change.
The demand for organisational coaching aimed at enhancing professional performance and personal well-being has increased significantly in recent years, backed by significant evidence of its efficacy (Athanasopoulou & Dopson, 2018). Grant et al. (2009) highlight that coaching promotes personal growth, thereby strengthening individuals, like investment professionals’ capacity to meet both organisational demands and individual aspirations. Coaching could therefore provide a structured framework for investment professionals to manage stress, maintain focus and enhance overall performance.
Central to coaching is goal-setting theory. Goal-setting theory emerged from extensive empirical research spanning nearly four decades (Locke & Latham, 2002). Within this framework, a goal is characterised as the intended outcome or target of an action (Locke & Latham, 2006). Understanding the relationship between goals, performance and well-being is important. Successfully pursuing and attaining goals enhances both professional outcomes and personal psychological health. According to Tóth et al. (2018), the achievement of a valued goal significantly contributes to subjective well-being. They argue that goal attainment is not merely about completing tasks, but about realising meaningful personal aspirations. Within the asset management industry, where investment professionals operate under sustained high-pressure conditions, it is increasingly critical for portfolio managers to pursue structured personal and professional goals, including well-being. The ability to set and achieve clear goals is essential for maintaining resilience and achieving well-being outcomes (Tuckett & Taffler, 2012). Goal-setting theory as operationalised by coaching therefore provides the foundational mechanisms for directing effort, sustaining motivation and fostering persistence (Senko & Hulleman, 2013; Spence, 2007). Coaching plays a crucial role in helping individuals clarify, pursue and attain meaningful goals that support both well-being and performance. For example, Grant et al. (2009) found that coaching interventions enhance resilience and well-being, while Spence (2007) argues that the effectiveness of coaching is closely tied to successful goal achievement.
Despite its known benefits, traditional face-to-face coaching has limitations. Terblanche (2024) notes that while coaching has demonstrated its effectiveness, it often remains inaccessible and prohibitively expensive. The advent of AI coaching could possibly address these challenges.
The potential of artificial intelligence coaching
Artificial intelligence has experienced a resurgence in recent years, catalysed by the announcement of the Fourth Industrial Revolution in 2016 (Schwab, 2024) and the subsequent arrival of generative AI. Ng et al. (2021) define AI as a wide range of technologies, including computer vision, natural language processing, robotics, robotic process automation and virtual agents, which are designed to simulate human cognitive functions. Artificial intelligence has also made its way into the coaching profession (Terblanche, 2020). Terblanche (2024) describes AI coaching as the imitation of human coaching expertise through simulated knowledge systems that mirror how human coaches engage, guide and support clients in their development journeys. Artificial intelligence has the potential to replicate certain elements of human expertise at significantly lower cost and holds the potential to transform the coaching industry (Acemoglu & Restrepo, 2018). Artificial intelligence coaching also offers scalability and substantial cost advantages. Artificial intelligence coaches are considerably less expensive than human coaches and are available around the clock (Terblanche, 2024). With AI coaching emulators in place, organisations will have few reasons not to provide access to a ‘coach’ for all employees (Terblanche, 2024). The exploration of AI coaching technologies highlights their growing potential to support personal development and wellness outcomes. Recent studies by Santhosh et al. (2023) and Terblanche et al. (2022a) have demonstrated that AI chatbots can deliver personalised, scalable and continuous support that encourages proactive health management and goal attainment. While AI coaching is increasingly applied across sectors such as healthcare and banking, as indicated by Chong et al. (2021), there remains a notable research gap in its application within the asset management industry.
The integration of AI into coaching practices introduces a range of ethical and human considerations that must be critically examined to ensure responsible and effective implementation (Passmore et al., 2025). One of the key concerns relates to data privacy and confidentiality, as AI coaching interactions require the collection and processing of highly sensitive personal and psychological information (Passmore et al., 2025). Diller (2024) highlights that the extent to which users can trust automated systems to safeguard this information is central to the ethical adoption of AI in coaching. This issue of trust was confirmed in a study by Van der Merwe and Terblanche (2025) who showed that users’ fear of the potential harmful impact of AI deters them from engaging with an AI coaching chatbot. This all stands in contrast to the traditional norms of confidentiality upheld in human coaching relationships (Diller, 2024).
A further ethical issue involves the potential loss of human empathy, emotional connection and psychological safety (Terblanche et al., 2022b). These qualities are central to effective coaching and are closely tied to the development of rapport and trust within the coaching relationship (Terblanche et al., 2022a). Considering these limitations, scholars increasingly recommend hybrid coaching models. In these models, AI handles between-session support such as reminders, progress tracking and task support (Loughnane et al., 2025). In contrast, human coaches focus on meaning-making, values-based reflection and deeper emotional processing (Stelter, 2007).
In summary, this literature overview showed that investment professionals operate in high-pressure environments where resilience, sustained performance and clearly defined goals are critical (Ramdani et al., 2022). Organisational coaching could help but has limitations such as high cost and 24/7 availability. Artificial intelligence coaching is an alternative and has demonstrated positive outcomes in various settings, including first-time graduate employees (Terblanche & Tau, 2025) and university students (Terblanche et al., 2023). It has been shown to support self-regulation, promote behavioural change and facilitate the achievement of personal and professional goals (Santhosh et al., 2023; Terblanche et al., 2022a). Despite these developments, there is a notable lack of scholarly research on its application within the asset management industry. Little is known about whether AI coaching can support behavioural outcomes or improve goal attainment aimed at well-being specifically in this context, which is the focus of this study.
Research design
This study adopted a qualitative research design to gain detailed insight into the experiences, perceptions and interpretations of investment professionals engaging with AI chatbot coaching. A qualitative approach was appropriate because AI coaching in the asset management industry is a new and limited area of research. Qualitative methods are well suited to emerging or insufficiently researched areas of inquiry, where limited prior knowledge exists (Oranga & Matere, 2023).
Participants were asked to use an AI coaching chatbot for 4 weeks. The AI coach used ChatGPT-4o as the underlying large language model, but it also incorporated scripted elements to ensure that a consistent and appropriate coaching process was followed. It operated through a conversational interface on the WhatsApp platform, enabling participants to interact with the system in natural language. The chatbot was designed to promote reflection, goal-setting and self-monitoring through short, structured interactions that could be accessed at any time. It guided users via open-ended and context-aware questions to clarify their goals, encouraged ongoing progress review and provided motivational reminders, which is consistent with coaching principles of self-awareness and accountability. The chatbot was specifically pre-programmed with an occupational wellness (Saraswati et al., 2019) goal tailored to professionals in the asset-management industry. The goal, titled: ‘Improve My Well-being at Work’, encouraged participants to pursue a personal well-being objective aimed at helping them feel more balanced, energised and focused on their daily work life.
Sample
Purposive sampling was used to invite people working in the South African asset management industry to participate in the study. A total of 10 participants were recruited as shown in Table 1.
Data collection
After participants completed the AI chatbot intervention, qualitative data were collected through semi-structured interviews. The interviews were guided by an open-ended question schedule that was designed to elicit insights into how investment professionals experienced and engaged with AI chatbot coaching, particularly regarding well-being and goal attainment. The interview guide ensured consistency across participants and provided flexibility to probe deeper into each participant’s responses. This approach allowed for the collection of detailed and meaningful data that aligned with the purpose of the study. There were nine main interview questions in addition to probes to gain further insights where needed. Questions covered aspects such as how participants experienced the chatbot in general, to what extent it helped them with their well-being, what aspects they liked and disliked, and how AI coaching support compared to other well-being support.
Each interview was conducted virtually, using secure video conferencing platforms, such as Microsoft Teams or Zoom, to accommodate participants across various locations. Interviews lasted between 45 min and 60 min, depending on the depth of discussion and participants’ availability. With informed consent, all interviews were audio recorded and transcribed verbatim. Participants were given the opportunity to review their transcripts to ensure accuracy before the data were analysed, which enhanced the trustworthiness of the results. Ethical protocols were maintained throughout the process, including the protection of participants’ confidentiality, secure data storage and the right to withdraw from the study at any time.
Data analysis
Data were analysed using Braun and Clarke’s six-phase thematic analysis framework (Braun & Clarke, 2006, 2024), which was well suited to exploring participants’ experiences of engaging with an AI chatbot coach. The analysis followed an iterative process of familiarisation, coding, categorisation and theme development.
In Phase 1, transcripts were read and re-read multiple times to ensure deep familiarisation with the data. Early analytic notes were recorded to capture initial observations, such as participants reporting increased convenience and reduced feelings of isolation when using the chatbot. Phase 2 involved line-by-line coding across all transcripts, resulting in an initial set of 80 codes. Performing comparison and refinement, overlapping and conceptually similar codes were consolidated into 35 distinct codes that captured shared patterns across participants.
In Phase 3, related codes were grouped into categories reflecting key aspects of participants’ experiences. For example, codes relating to time pressures and intrusive prompts were grouped under ‘Usability Issues’, representing practical barriers to engagement. Phase 4 involved reviewing and integrating related categories into broader themes. Categories such as accessibility, navigation, timing and privacy were combined into the theme ‘Ease of use and accessibility’, reflecting participants’ overall interaction with the chatbot.
In Phase 5, themes were refined, clearly defined and named to ensure internal coherence and distinction. Phase 6 involved integrating the themes into a coherent narrative aligned with the study’s research objective.
Research quality assurance
To promote research quality, the study followed established qualitative research criteria of credibility, transferability, dependability, confirmability and reliability through rigorous and transparent data collection and analysis procedures (Ahmed, 2024). Consistency was supported through the use of a common interview guide, verbatim transcription and a systematic analytical process (Braun & Clarke, 2006, 2024). An audit trail documenting coding and theme development decisions enhanced transparency and confirmability. Credibility was further strengthened through participants’ validation, whereby participants reviewed transcripts for accuracy prior to analysis (Mero-Jaffe, 2011). Reflexive practice was also employed throughout the study to manage potential researcher bias, including the maintenance of reflective notes documenting assumptions, emotional responses and interpretive decisions during data collection and analysis (Karcher et al., 2024).
Ethical considerations
Ethical clearance to conduct this study was obtained from all participants through informed consent and the Stellenbosch Business School Research Ethics Committee (Ref. No. 33585).
Results
The main themes and categories per theme are summarised in Table 2 and presented in detail in this section.
Theme 1: Ease of use and accessibility
The first theme, Ease of use and accessibility, captures how participants experienced the AI chatbot’s functionality, structure and practical integration into their working lives. This theme comprises three categories: Accessibility and usability, Usability issues and Privacy and navigation. Together, these categories show how design features both enabled and constrained engagement with AI coaching.
Participants generally described the chatbot as intuitive, accessible and easy to use. Its continuous availability and structured design enabled engagement at times that would not be possible with human coaching. As S1 explained:
‘It was user friendly and always available, even at night … I could log in after a long day, pick up exactly where I had stopped, and it still made sense … which I wouldn’t be able to do with a person.’ (S1)
Similarly, N1 emphasised that the logical flow of the system reduced cognitive load:
‘The system was well laid out, so I always knew what to expect next … even when I didn’t have much energy, it felt manageable because the flow made sense.’ (N1)
Participants also noted the chatbot’s professional tone as supportive rather than mechanical. C1 described this balance as enabling deeper reflection:
‘It guided me clearly through each step and responded in a way that felt supportive, not robotic … that structure really helped me think more deeply.’ (C1)
However, participants also identified usability challenges. While the structured format was often helpful, it sometimes became restrictive when participants were under time pressure. J1 described this tension:
‘On busy days it felt like one more thing to get through … it’s not that the chatbot was hard to use, it just required a kind of focus that isn’t always possible.’ (J1)
Others experienced frustration when the system repeated questions. LU1 explained:
‘Sometimes it kept circling back to the same question … I wanted to go deeper, but it pulled me back to the same place.’ (LU1)
Privacy perceptions were mixed. For some, anonymity enhanced openness; for others, uncertainty about data storage limited disclosure. F1 noted:
‘I was careful about what I shared because I wasn’t sure how private it really was.’ (F1)
In contrast, LU1 viewed the lack of human judgement as freeing:
‘Because it wasn’t a person, I didn’t have to worry about being judged … that made the conversation more honest.’ (LU1)
Navigation limitations also affected engagement. C1 described feeling constrained when the chatbot struggled to shift topics:
‘It felt stuck on one issue … I wished I could just go back to a main menu and decide what to focus on next.’ (C1)
Overall, this theme illustrates how ease of use, flexibility and perceived safety shaped engagement. When accessibility aligned with user expectations, the chatbot supported meaningful reflection; when repetition, time pressure or uncertainty intervened, engagement diminished.
Theme 2: Reflection, self-awareness and well-being
The second theme, Reflection, self-awareness and well-being, captures how participants experienced the chatbot as a reflective and supportive presence that encouraged introspection, emotional regulation, and well-being. Four categories underpin this theme: Facilitating reflection and awareness, Emotional and conflict support, Well-being and stress management and Health aid.
Participants consistently described how the chatbot’s reflective prompts encouraged them to pause and examine their thoughts and reactions. L1 explained:
‘The questions made me stop and think in a way I normally wouldn’t … it helped me connect the dots between how I felt and what I did.’ (L1)
T1 similarly described gaining insight through writing:
‘When I typed it out, I could see my thought process more clearly … I wouldn’t usually take time to do that.’ (T1)
For several participants, the chatbot functioned as a mirror, increasing awareness of emotional triggers. N1 reflected:
‘I started recognising which situations brought out stress or frustration … it made me think about how I could handle them differently.’ (N1)
The chatbot also provided emotional and conflict support. Participants described using it as a private outlet during moments of stress or isolation. T1 noted:
‘It helped me feel less alone … just typing it out there helped me release some of that tension.’ (T1)
In conflict situations, reflective prompts encouraged reframing rather than rumination:
‘Instead of replaying what upset me, it pushed me to look at the situation from another angle.’ (N1)
Participants also highlighted the chatbot’s role in emotional regulation. T1 explained:
‘It helped me calm down before reacting … I didn’t say things I would regret later.’ (T1)
In terms of well-being and stress management, participants described reduced overthinking and moments of clarity. As T1 put it:
‘It stopped me from overthinking and helped me move forward with a clearer mind.’ (T1)
Several participants experienced the chatbot as a form of health aid that extended beyond work-related reflection. L1 described it as a non-judgemental outlet:
‘I could put everything into words without worrying about how it would sound … it gave me a sense of relief.’ (L1)
LU1 highlighted its role in supporting self-care habits:
‘It reminded me to take breaks and pay attention to my well-being … even small reminders made a difference.’ (LU1)
Collectively, this theme shows how the chatbot supported reflection, emotional processing and well-being. While it did not replace human empathy, it created structured moments of awareness and regulation within demanding work contexts.
Theme 3: Accountability and goal attainment
The third theme, Accountability and goal attainment, reflects how participants experienced the chatbot as a tool that supported consistency, focus and progress towards personal and professional goals. This theme comprises two categories: Goal support and Application focus.
Participants described how reminders and check-ins promoted accountability. M1 explained:
‘It kept me accountable because I knew it would check in … a gentle nudge that kept me consistent.’ (M1)
PA1 similarly observed:
‘It made sure I didn’t just set goals and forget about them.’ (PA1)
Participants also valued the chatbot’s support with prioritisation. J1 explained:
‘It helped me sort what was urgent and what could wait … which made my day clearer.’ (J1)
Acknowledgement of progress further reinforced motivation. PA1 reflected:
‘Seeing how far I’d come … that small acknowledgement kept me going.’ (PA1)
Beyond task management, participants described how accountability was linked to well-being. LU1 explained:
‘It reminded me to balance productivity with managing my energy and stress levels … that balance became part of achieving my goals.’ (LU1)
Overall, this theme demonstrates how the chatbot supported sustained focus and progress while also encouraging a more sustainable approach to performance.
Theme 4: Tension between artificial intelligence coaching and human coaching
The final theme, Tensions between AI and human coaching, captures how participants compared AI coaching with traditional human coaching and considered its future role. Three categories informed this theme: Comparison with human coaching, Tensions between AI and human coaching and Future adoption.
Participants consistently noted that while the chatbot offered structure and accessibility, it lacked emotional depth. PA1 stated:
‘It couldn’t replace the human touch … I missed that sense of empathy.’ (PA1)
LU1 echoed this:
‘It was helpful but didn’t give the same emotional connection as a real person.’ (LU1)
At the same time, participants acknowledged trade-offs between emotional connection and accessibility. J1 explained:
‘It’s not the same as a coach, but it’s more affordable and available anytime.’ (J1)
Rather than viewing AI as a replacement, participants described it as complementary. LU1 described it as:
‘A bridge between sessions.’ (LUI1)
Others identified broader applications. N1 suggested:
‘I can see it being used for career guidance.’ (N1)
LU1 described the chatbot as:
‘A workplace copilot … something always there to guide me.’ (LU1)
T1 expressed willingness to continue using it:
‘It’s something that fits into my day easily.’ (T1)
Overall, participants recognised the emotional limitations of AI coaching but valued its accessibility, structure, and continuity. The findings point towards a hybrid model in which AI complements, rather than replaces, human coaching.
Discussion
This study examined how AI coaching supports well-being in a high-stress asset management environment via specific wellness goals. The findings suggest that AI chatbot coaching can provide meaningful support for well-being by enabling reflection, emotional regulation and sustained goal-directed behaviour in cognitively demanding roles. By fostering this self-awareness and providing immediate support, the AI coaching intervention could act as a mechanism for emotional regulation, potentially serving as a ‘de-biasing’ tool that aligns with the growing focus on the subjective well-being and psychological profiles of company management (Rzeszutek et al., 2023). However, its contribution is best understood as complementary rather than substitutive, offering structure, accessibility and continuity rather than deep emotional engagement.
A central insight from the findings is the foundational role of ease of use and accessibility in supporting well-being. Participants valued the chatbot’s constant availability, structured flow, and professional tone, which allowed engagement during moments when traditional coaching or organisational support would be impractical. This aligns with research positioning AI coaching as a way to overcome time, cost, and access barriers associated with human coaching (Acemoglu & Restrepo, 2018; Terblanche, 2024). In the asset management context, where schedules are unpredictable and confidentiality concerns are heightened, these features were particularly salient. The findings therefore extend existing AI coaching research by demonstrating its relevance in a highly regulated, performance-driven financial environment that has received limited attention in prior studies.
At the same time, the findings highlight important design tensions. Structured conversation flow supported the engagement, however it led to repetition, timing mismatch and limited adaptability which occasionally reduced motivation. This supports emerging critiques that overly rigid automation can undermine sustained use, particularly in high-pressure contexts where cognitive resources fluctuate (Koenig, 2025). Rather than rejecting the technology, participants’ experiences point to the need for more adaptive pacing and contextual sensitivity in well-being-focused AI coaching systems.
The study further shows that AI coaching supported well-being primarily through reflection and self-awareness. Participants experienced the chatbot as a space that enabled pausing, sense-making, and recognition of emotional and cognitive patterns during reactive moments. This mirrors prior findings that AI coaching can facilitate self-regulation through reflective questioning and feedback loops (Santhosh et al., 2023; Terblanche et al., 2022a). In this study, such reflective support was especially valuable in managing stress, overthinking, and emotional spillover associated with volatile market conditions. The chatbot functioned as a cognitive stabiliser, helping participants interrupt unproductive thought cycles and regain perspective.
However, participants were also clear about the limits of this support. While the chatbot facilitated awareness and emotional regulation, AI coach not experienced as capable of supporting deeper emotional exploration, identity work, or existential concerns. This reinforces arguments that AI coaching is more effective for behavioural and cognitive regulation than for complex emotional processing (Bachkirova & Kemp, 2025; Hardcastle et al., 2024). The findings therefore challenge more optimistic claims that AI systems can replicate the emotional depth of human coaching relationships (Mitchell et al., 2021). Instead, they suggest that emotional presence, intuition, and meaning-making remain distinctly human contributions to well-being support.
Well-being was also supported through accountability and goal attainment. Participants described how reminders, progress prompts, and prioritisation helped them to sustain well-being-oriented behaviours such as pausing before reacting, managing energy, and maintaining healthy routines under pressure. This aligns with evidence that AI coaching supports behaviour change through consistent nudging and goal tracking (Daley et al., 2020; Loughnane et al., 2025). Importantly, participants experienced well-being and performance as intertwined rather than separate. Goal progress reinforced emotional stability, while improved regulation supported more sustainable performance. This contrasts with claims that AI-driven goal support operates independently of contextual meaning (Weiner et al., 2024) and highlights the importance of aligning wellness goals with professional realities in high-performance environments.
The findings also illuminate persistent tensions between AI coaching and human coaching. Participants valued the chatbot’s availability, discretion, and consistency, but did not view it as a substitute for human support. Emotional understanding, shared experience, and relational depth were seen as essential for addressing complex personal and professional challenges. This aligns with ethical and relational critiques of AI coaching that emphasise the limits of empathy, intuitionand moral judgement in automated systems (Clutterbuck, 2020; Terblanche et al., 2024).
Rather than rejecting AI coaching on emotional grounds, participants articulated a preference for hybrid support. Artificial intelligence coaching was viewed as most valuable when it provided continuity, structure, and reflection between human interactions. This supports proposals for hybrid coaching models that combine the strengths of AI and human coaches (Graßmann & Schermuly, 2021). It also challenges assumptions that users fundamentally distrust AI coaching systems (Rubin et al., 2024). In this study, trust was linked not to simulated empathy but to transparency, predictability, and clarity of purpose (Li et al., 2024). When participants understood what the chatbot could and could not do, they were more willing to engage with it as a well-being support tool.
The findings of this study suggest that AI coaching could support well-being in high-stress asset management environments when positioned as a cognitive and behavioural aid rather than an emotional replacement. Its value lies in enabling accessible reflection, supporting emotional regulation, and sustaining well-being-oriented behaviours under pressure. However, its effectiveness depends on contextual fit, adaptive design, and integration with human support. These findings contribute to a more nuanced understanding of AI coaching, positioning it not as a disruptive replacement for human coaching, but as an enabling infrastructure that extends access to well-being support in demanding professional settings.
Contributions
This study contributes to the growing body of literature on goal-setting, well-being, behavioural change and coaching by extending these concepts into the emerging domain of AI development. Research has established the effectiveness of human coaching in promoting reflection, goal commitment and well-being. A theoretical contribution lies in the evidence that reflection and self-awareness can be activated through structured, automated questioning. This challenges traditional assumptions that effective reflection depends on interpersonal interaction or emotional exchange (De Haan et al., 2016). The findings of this study demonstrate that objectivity and psychological safety within an AI context can enhance openness and authenticity. This suggests an alternative pathway for self-directed development. Furthermore, this study adds to the literature on well-being and behavioural change by introducing the concept of on-demand, self-regulated coaching, where support is accessed flexibly and integrated into daily work routines. This extends existing models of coaching, which have primarily emphasised scheduled, relational interactions.
This study offers practical insights for organisations, coaches, and technology developers seeking to integrate AI tools into professional well-being and development strategies. In high-stress asset-management environments, the findings indicate that AI chatbot coaching can complement existing well-being and performance initiatives by providing flexible, on-demand support that encourages reflection, accountability and stress regulation. Positioned as an accessible first layer of support, AI coaching can offer continuous guidance alongside or between human coaching engagements.
For asset managers, AI coaching provides a private, low-pressure space to process stress, regulate emotions, and maintain behavioural consistency during periods of market volatility. Reflective prompts and accountability features help manage cognitive overload and sustain focus, supporting healthier work habits and potentially reducing burnout risk. Human resource and development teams may benefit from integrating AI coaching into broader well-being programmes, particularly where time constraints limit access to traditional coaching. Framing AI coaching as a self-directed aid rather than a replacement may enhance engagement and normalise reflective practices.
For AI developers and coaching professionals, the findings highlight the importance of adaptive dialogue, contextual sensitivity, and reduced repetition to sustain user engagement. Finally, for human coaches, the study demonstrates how AI tools can act as effective between-session support, enabling sessions to focus more deeply on meaning, values, and long-term development. Together, AI and human coaching may extend access to support while enhancing overall coaching effectiveness.
Limitations and suggested future research
This study has several limitations that need to be taken into account when interpreting the findings. Firstly, the study was qualitative in nature and based on a small sample of investment professionals within the South African asset management industry. While this design allowed for rich, in-depth exploration of experiences, it limits the ability to generalise findings to the broader population of financial professionals or other industries. Secondly, participant self-selection may have introduced bias. Those who volunteered to engage with the AI chatbot and take part in interviews may have been more open to technology or reflective practices than the average investment professional, potentially influencing the range of perspectives captured. Thirdly, the duration of the intervention was relatively short. Participants interacted with the chatbot over a limited period, which may not have been sufficient to observe sustained behavioural or well-being changes over time. Longer-term studies could provide deeper insights into how engagement patterns evolve and whether the benefits persist. Despite these limitations, this pilot study provides a meaningful foundation for understanding how AI coaching could support well-being and goal attainment in high-performance environments. The insights generated offer valuable directions for both future research and practice.
Future studies could extend this research by including a larger and more diverse sample of investment professionals across different geographic regions and financial sectors. Expanding the sample size and context would allow for broader insight from the findings and reveal whether similar themes emerge in other high-performance environments. Future studies could combine interviews with basic quantitative measures, such as stress levels or usage patterns, to provide a fuller picture of how AI coaching supports individuals over time. Following participants across a longer period would also help to see whether initial improvements in engagement or behaviour are maintained in the long run.
Conclusion
This pilot study explored how AI chatbot coaching could support well-being and goal attainment among investment professionals operating in high-performance environments. The research responded to a gap in existing literature by examining how digital coaching tools function in an industry characterised by pressure, precision and psychological strain. The findings revealed that AI chatbot coaching holds promise as an accessible, reflective and supportive intervention that complements traditional coaching models rather than replacing them. The analysis demonstrated that AI chatbot coaching facilitated key mechanisms supporting goal attainment and well-being, including self-reflection, accountability and stress management. This study affirms that while AI chatbot coaching cannot substitute the depth of interpersonal understanding found in human coaching, it represents a meaningful evolution in the coaching landscape. It makes developmental support more accessible, responsive and aligned with the modern demands of the asset management industry.
Acknowledgements
This article is based on research originally conducted as part of Lynn Lekay’s MBA thesis titled, ‘The role of an AI chatbot coach in facilitating wellness goals in the asset management industry’, submitted to Stellenbosch Business School in 2025. The thesis was supervised by Nicky Terblanche. The thesis was reworked, revised, and adapted into a journal article for publication. The original thesis is not publicly available.
Competing interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
CRediT authorship contribution
Lynn Lekay: Data curation, Formal analysis, Software, Writing-review & editing. Nicky Terblanche: Conceptualisation, Writing - original draft, Writing - review & editing. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication, and take responsibility for the integrity of its findings.
Funding information
The authors received no financial support for the research, authorship and/or publication of this article.
Data availability
The data that support the findings of this study are available from the corresponding author, Nicky Terblanche, upon reasonable request but subject to confidentiality and anonymity requirements.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings and content.
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