City St George's Organisational Psychology Group Blog

By Lola Goldstein (24-25)

Gen-Z are facing a mental health crisis unlike any previous generation. Research shows that one in six Gen-Z experience depression and two in five experience anxiety, while the overall incidence of psychological distress has almost doubled in comparison to the two generations before. At the same time, Gen-Z consistently ranks mental health as one of the most pressing issues facing their generation, making it an issue that organisations cannot afford to ignore.

Born between 1996 and 2012, Gen-Z were the first generation to grow up with smartphones in hand. While this brought new opportunities, it also disrupted critical developmental experiences, including by limiting social learning and increasing sleep deprivation, ultimately resulting in significant changes in mental health. The first iPhone was released in 2007, and most homes had access to social media by 2012. This coincided with a peak in mental illness, with rates of teen depression more than doubling between 2010 and 2015. However, these mental health challenges have not been left behind in adolescence but now shape Gen-Z’s working lives, creating severe implications for employers.

Employee well-being has received growing attention in recent years, with organisations acknowledging the far-reaching impact this can have. Work well-being involves both the physical and psychological health of employees in the workplace. Low employee well-being can be highly costly for organisations, resulting in higher turnover intentions, reduced retention, absenteeism and presenteeism. On the other hand, organisations that invest in well-being can experience productivity gains of up to 20%, as employees who handle workplace stress better show greater levels of creativity, engagement, and innovation. Therefore, investing in employee well-being is not just ethically important, it also creates a competitive edge. 

Gen-Z already make up 27% of the workforce, and by 2035, they are projected to be the most dominant cohort. Therefore, it is crucial that organisations understand what drives this generation’s well-being at work. 

The Study

This research set out to explore various contributors to Gen-Z’s work well-being in order to determine which are the most important. These results provide valuable insights that can help organisations to direct their resources more effectively, supporting the well-being of this generation while strengthening organisations as a whole.

Several workplace factors were explored with established links to employee well-being including: a combination of high job demands and low job control (job strain), social support, and an imbalance between the effort expended at work and rewards received. Factors specifically associated with Gen-Z were also investigated, including work-life balance, the alignment between an individual and their job (person-job fit), the alignment between individuals and organisations (person-organisation fit), and psychological safety. 

To establish these findings, 104 Gen-Z employees (aged 20-29) were recruited to compete and online survey assessing their levels of work well-being and scores on each workplace factor. 

What the Research Found

The results were clear: the strongest predictors of Gen-Z’s work well-being were person-job fit, person-organisation fit and social support. After these was work-life balance, followed by psychological safety. Meanwhile, traditional stressors like high job demands and low job control or imbalances between effort and reward were less influential for this generation. Interestingly, working in the education sector is also linked to higher well-being among Gen-Z employees.

What This Means for Organisations

These findings emphasise the vital role of “fit” for Gen-Z’s well-being, highlighting the need for harmony beteen an employee and their work environment. By putting energy into improving this alignment, organisations are likely to see positive improvements in the well-being of these employees.

To enhance person-job fit, organisations should ensure they are matching their Gen-Z employees with roles that reflect their skills, abilities and interests. This can be done through recruitment and selection processes that prioritise this alignment, helping to place people where they can thrive. On-the-job training should also be tailored to help employees to meet the required skills for the role, further promoting person-job fit. 

Person-organisation fit can also be improved through the hiring process, including by clearly and openly communicating the organisation’s mission, values, and culture to candidates in order to attract employees with similar priorities. For instance, showcasing corporate social responsibility efforts that go beyond typical business goals can attract Gen-Z candidates who are looking for meaningful work. 

The results also highlight the importance of creating supportive work environments. This can be achieved through initiatives as mentoring programmes, peer networks, and management practices that provide guidance, feedback, and encouragement. Providing this kind of social support can help employees combat job stress and is therefore a powerful way to improve Gen-Z’s work well-being.

Beyond these three essential elements, work-life balance is also another valuable focus for organisations, given its key role in supporting Gen-Z’s work well-being. This generation are thought to have re-defined work-life balance, viewing this as an integration of professional and personal life that enhances their psychological well-being, rather than treating them as completely separate entities. Therefore, organisations can help maintain this balance by providing more flexible work options, like hybrid or remote arrangements, along with clear boundaries around work-related communication.  

Why It Matters

Overall, these findings points to generational nuances in the predictors of work well-being, specifically highlighting the importance of “fit” and social support for this generation. This reflects Gen-Z’s greater emphasis on intrinsic motivators, such as meaning and values alignment, as well as positive relationships at work, over more traditional drivers like financial rewards alone. Therefore, this provides a useful guide for organisations looking to improve the well-being of the newest generation to enter their workforces. Organisations that understand and act on these priorities are likely to see notable improvements in the well-being of their Gen-Z employees, ultimately strengthening their organisation as a whole. 

By James Holden (24-25)

Artificial Intelligence (AI) is transforming workplaces at an astonishing pace. Tools like ChatGPT, Microsoft Copilot, and automated decision-making systems are reshaping how we recruit, communicate, and measure performance. For some employees, these tools feel exciting and liberating. For others, they spark anxiety, confusion, or fear of being replaced.

As organisations race to adopt AI, an important question arises:  what does the introduction or increasing use of AI mean for the wellbeing of employees?

This research explored this question by looking at flourishing, a broad measure of workplace wellbeing. Flourishing is more than the absence of stress; it is about employees feeling good and functioning well: being engaged, connected to others, finding meaning in what they do and being able to grow and contribute at work. In today’s competitive landscape, flourishing employees are more innovative, more productive, collaborate more and are more likely to stay. In short, flourishing isn’t a “nice to have”, it’s vital for organisational success.

But flourishing is under pressure in the age of AI. The very tools designed to make work easier can create new kinds of stress, often called  technostress. This research examined how AI-related technostress affects workplace flourishing, and whether personality plays a role in how employees cope with it.

The stress side of AI

When we think of ‘stress at work’, we might imagine long hours or difficult bosses. But technology itself can be a major stressor. AI brings some unique challenges:

• Techno-complexity  – when systems feel confusing, constantly changing, or difficult to master.
• Techno-insecurity  – when people fear that AI could replace their job or devalue their skills.
• Techno-overload  – when technology speeds up the pace of work and volume of information, creating pressure to do more in less time.

Most of us have felt at least one of these recently. Learning a new system that seems baffling. Wondering if AI will make your role redundant. Or feeling pressured to be ‘always on’ because technology never rests.

This study set out to see whether these stressors predict lower flourishing at work, and whether some people are more vulnerable than others.

Personality matters

Psychologists often point out that stress is not just about what happens to us, but how we interpret and react to it, thus this study looked at  neuroticism, one of the “Big Five” personality traits. People high in neuroticism tend to experience negative emotions more strongly and worry more about potential threats.

What the research covered

Almost 200 working adults in the UK from a broad range of industries  were surveyed and asked three simple things:

1. how much they were  flourishing at work  (feeling good and functioning well),
2. how much  AI-related pressure  they felt in three areas—complexity  (hard to learn),  insecurity  (fear of being replaced), and  overload  (AI pushing the pace), and
3. their  personality, focusing on  neuroticism  (tendency toward worry/negative emotion).

Everyday background factors like  age, education, income comfort, industry, and  risk tolerance  (how comfortable people feel taking risks) were also recorded. Then standard statistical models were run to see what actually predicts flourishing, and whether neuroticism  changes  the impact of those AI pressures. 

What were the results

• No direct AI-pressure hit to flourishing. When the background factors, were accounted for, none of the three AI-related stressors (techno-complexity, techno-insecurity, or techno-overload) directly predicted lower flourishing.

• Personality mattered. People higher in neuroticism reported lower flourishing overall. Meanwhile, people with higher risk tolerance  reported  higher flourishing. These were  direct  links with flourishing (i.e. how people felt and functioned at work); they weren’t tied to higher or lower AI stress per se.

• No “amplifier” effect.  When it was tested whether neuroticism  amplified  the impact of different AI-related technostressors, it didn’t. The interaction terms were not significant.  

In short: in this mixed UK sample, current AI pressures didn’t knock flourishing down, but who you are (especially lower neuroticism and higher risk tolerance) did relate to how well you felt you were thriving at work.  

Why this matters for organisations

A common assumption is that “AI stress will undermine wellbeing.” These data suggest: not necessarily—at least not for flourishing. Flourishing reflects resources such as support, meaning, growth, and connection. Removing stressors is helpful, but it is often  not sufficient; flourishing grows where resources are deliberately built.

• If your goal is to protect flourishing, don’t focus only on “removing stressors.” Instead, build resources around people and teams (e.g. clarity, support, autonomy, learning, recognition). These are the soil flourishing grows in.  

• Neuroticism  is a reliable risk flag for lower flourishing. That doesn’t mean “the person is the problem”; it means they’ll benefit more from  predictability, reassurance, and quick access to support. 

• Risk tolerance  looks like a quiet  asset. People comfortable with uncertainty tend to  flourish more  during change. Harness that, without glorifying unnecessary risk.  

Practical steps leaders can take (now)

1. Resource first, tech second.
   Pair AI rollouts with  short, targeted training, easy help channels, and  manager check-ins. Don’t just deploy tools, deploy  support. 

2. Tune your comms to different people.
   • For employees higher in worry/uncertainty: give  clear roadmaps, timelines, and “what this means for your role” briefings; signpost  emotion-regulation/mindset micro-skills  (10–15 minutes is enough).
   • For higher risk-tolerance staff: involve them in  early pilots  as  AI champions  and co-designers.  

3. Protect boundaries.
   AI can speed everything up. Set norms for  notification management, response times, and  right-to-disconnect practices so pace doesn’t quietly become overload.  

4. Make flourishing visible.
   Track simple, actionable indicators (e.g.,  learning progress,  team belonging,  manager support). Celebrate  human strengths (e.g. judgement, empathy, creativity), so people feel valued alongside AI.  

Final thought

AI is here to stay, but its people impact is not fixed. In this study,  AI didn’t automatically erode flourishing. What mattered more was the  human side: stable dispositions like neuroticism and risk tolerance, and the  resources  organisations provide. If leaders invest in clarity, capability, and care, employees can keep flourishing, even as AI evolves. That’s not just good for people; it’s good strategy.

By Ellyn Murakami (24-25)

Why is this study important?

Organisations continue to navigate volatile, uncertain, complex, and ambiguous (VUCA) challenges, such as globalisation, digitisation, and health crises, which require leaders to adapt and exhibit new behaviours to motivate and mobilise others effectively (Lawrence, 2013). However, perceptions of how well leadership development initiatives equip leaders to handle such complexities are consistently low. In 2014, McKinsey found only 7% of senior leaders believed they were developing global leaders effectively, and, similarly, Deloitte found only 13% of organisations thought they had done a quality job training their leaders (Gurdjian et al., 2014; Schwartz et al., 2014). These numbers are alarming considering that in 2018, it was estimated that organisations spent $370 billion on leadership development solutions globally with the USA alone spending $169 billion (Training Industry, 2020). Whilst there has been a surge of scientific interest in leadership development over the last 20 years, the field remains underdeveloped compared to the general leadership field (Day, 2024). Thus, this study aims to identify the enablers and barriers to effective leadership development in the 21st century.

What was found?

            17 individuals from seven industries were interviewed to explore their experiences of leadership development initiatives. To ensure diverse perspectives, participants were recruited across three roles: commissioners (clients of leadership development vendors), providers (E.g., consultants, in-house leadership development professionals, etc.), and participants of leadership development programmes. 

            Six themes were identified, representing the complexity of effective leadership development in the 21^st century (see Figure 1.) Effective leadership development works as a system, influenced by what happens before, during, and after a programme. Success also depends on having a well-designed measurement plan and on the wider organisational context.

Figure 1

Themes, subthemes, and illustrative quotes.

Note: Green text represents enablers and red text represents barriers. 

1. Leadership Development System

Participants consistently emphasised that leadership development works best when treated as a system, not just a course. This means it is crucial to consider what happens before, during, and after programme, how success is measured, and the organisation’s context. In this view, development is more of an ecosystem and ongoing process than a one-off classroom event.

2. Before a Leadership Development Programme

Common barriers to effective leadership development before a programme starts are not properly diagnosing the organisation’s strategy and leadership gaps and requiring participation. Without this, programmes feel disconnected from strategy, may not address the organisation’s needs, and participants may not know why they’re there. Also, requiring attendance also undermines motivation, which can negatively affect the whole group.

3. During a Leadership Development Programme

Three main enablers stood out during programmes:

1. In-person cohorts over time: participants shared that building trusting relationships and having sufficient time during and between sessions to learn, connect, and reflect were pivotal in making learning stick. 
2. Self-awareness: almost everyone cited this as the most impactful skill to learn and as a prerequisite for future leadership growth.
3. Experiential learning: challenging projects where participants applied new skills were seen as the most effective.

4. After a Leadership Development Programme

Many participants shared the importance of ongoing support after leaving the classroom. They noted that learning often fizzles due to not having enough time to continue applying and unsustained accountability, whether that comes from themselves, the programme, their leader, or the organisation. Participants also suggested check-ins, bite-sized follow-up workshops, or alumni meetups as ways to keep momentum going.

5. Measurement Plan

            Designing a measurement plan for leadership development initiatives is necessary to evaluate their effectiveness; however, this is rarely done and is challenging. Participants highlighted the importance of identifying key metrics and measuring a baseline, followed by immediate and longer-term evaluation to determine the lasting impact.

6. Context of the Organisation

Many participants emphasised that organisational culture and leadership behaviours strongly impact their development. Without support from managers or when leaders fail to model the behaviours participants are learning, development efforts quickly lose traction. Conversely, when senior leaders participate in and sponsor initiatives, it signals priority, builds a common language, and reinforces change.

What does this mean?

The study found that leadership development is most effective when approached as a holistic system with enablers and barriers at every stage before, during, and after programmes, and within the measurement plan and organisational context. Based on these findings, leadership development systems can be classified into four types. The programme and the organisational context are crucial because participants reported spending most of their working lives embedded in the organisational context, with only limited time devoted to formal programmes. As a result, even well-designed programmes may not compensate for an unsupportive organisational environment, just as a strong context may be insufficient to make up for a poorly designed programme. This interdependence is illustrated in Figure 2, which depicts four leadership development system types.

Figure 2

Leadership Development System Types

Becoming aware of which leadership development system type an organisation fits is the first step in identifying what needs to change to develop leaders most effectively. The development of an assessment would allow organisations to identify their type. If they are not in the optimal zone, this evaluation can highlight the weak components that need improvement and offer solutions.

What can organisations do going forward?

Organisations should approach leadership development holistically to ensure the most effective use of the time and money invested. Specifically, some recommendations are:

• Participants in leadership development systems should integrate new behaviours and leadership goals into their personal development plans to maintain priority and ensure accountability.
• Managers should support their team members’ leadership development by encouraging participation in growth opportunities, protecting time to practise new skills, and reinforcing accountability by sharing feedback.
• Managers and organisational leaders should model desired behaviours by participating in and sponsoring leadership development systems.
• Organisations should partner with Organisational Psychologists to diagnose needs and design, implement, and evaluate leadership development initiatives.

            In summary, the study found that leadership development is most effective when approached as a holistic system. This allows organisations to prioritise enablers and address barriers at every stage before, during, and after programmes, and within the measurement plan and organisational context. Viewed this way, leadership development becomes more than individual programmes: it becomes a system that helps leaders and organisations reach their full potential.

References

Day, D. V. (2024). Developing leaders and leadership: Principles, practices, and processes (First). Palgrave Macmillan Cham. https://doi.org/10.1007/978-3-031-59068-9

Gurdjian, P., Halbeisen, T., & Lane, K. (2014). Why leadership development programs fail. https://www.mckinsey.com/~/media/mckinsey/featured%20insights/leading%20in%20the%2021st%20century/why%20leadership%20development%20programs%20fail/why%20leadership%20development%20programs%20fail.pdf?shouldIndex=false

Lawrence, K. (2013). Developing leaders in a VUCA environment. http://www.execdev.unc.edu

Schwartz, J., Bersin, J., & Pelster, B. (2014). Global human capital trends 2014 – Engaging the 21st-century workforce. https://www.deloitte.co.uk/makeconnections/assets/pdf/global-human-capital-trends-2014.pdf

Training Industry, Inc. (2020). The size of the training industry. https://trainingindustry.com/wiki/learning-services-and-outsourcing/size-of-training-industry/

By Alice Wallace (24-25)

Introduction 

Burnout remains one of the biggest challenges in healthcare, with more than one in three professionals reporting symptoms during their careers (Nagarajan et al., 2024). Long hours, emotional demands, and exposure to distressing situations place heavy strain on staff – reducing clinical judgement and increasing the risk of errors (Matsuo et al., 2022). These pressures highlight the need to support the workforce, with artificial intelligence (AI) emerging as a transformative tool for improving efficiency, accuracy, and patient outcomes (Amann et al., 2020). AI is already making tangible contributions across healthcare. It has demonstrated high accuracy in diagnosing a range of conditions: hypertension, diabetes, Alzheimer’s disease, and several cancers. Beyond diagnostics, AI is advancing precision medicine by tailoring treatments to patient’s genetic and lifestyle choices (Huang et al., 2018). 

Despite these benefits, adoption of these nuanced technologies is not a linear process. Uptake depends on the people who must work with AI in demanding environments. Many healthcare staff report concerns about ethics, accountability, and risk (Warrington & Holm, 2024); these perceptions can strongly shape willingness to engage with AI (Choudhury, 2022). Dingel et al. (2024) found that higher perceptions of risk were linked to lower willingness to adopt AI-enabled decision support systems. While these concerns highlight important organisational challenges, it is important to consider the personal resources clinicians bring to their roles. Individual factors such as occupational self-efficacy (i.e., the confidence clinicians have in their ability to manage complex challenges) can shape how staff respond to new technologies (Rigotti et al., 2008). Kuper et al. (2025) found that clinicians with greater confidence in their own judgement were less likely to rely on AI when classifying skin images as benign or malignant – suggesting that confidence may reduce willingness to engage with technological support. 

Building on the current research, this study seeks to answer two key questions: (1) How does perceived risk affect clinicians’ willingness to use AI; and (2) Does occupational self-efficacy impact this relationship?

Methods

This research was conducted as part of a MSc in Occupational Psychology at City, University of London. A total of 125 participants took part, consisting of both qualified healthcare professionals (i.e., Nurses, Paramedics, and Psychiatrists) and students completing their clinical placements (i.e., Nursing, Midwifery, and Medicine). Recruitment was carried out using several online platforms: Facebook, Instagram, LinkedIn, and Reddit). Additional recruitment took place through university channels, via student and staff mailing lists. 

The study was hosted online, where participants completed three main questionnaires: risk perception (i.e., concerns about safety or errors), willingness to use AI (i.e., intentions to adopt these tools), and occupational self-efficacy (i.e., confidence in managing workplace challenges). To ensure the results were reliable, several other factors that might influence attitudes towards AI was also considered: age, gender, clinical experience, student status, risk aversion, technology literacy, and AI literacy. Including these controls helped rule out alternative explanations and provided a clearer picture of the psychological factors most relevant to AI adoption. 

Results

Approximately one-third of participants (31.2%) reported using AI in their clinical practice or placement, suggesting that widespread integration of these tools is relatively still limited. The results highlighted four key factors that influenced these individuals’ willingness to adopt AI in their practice…

• Student status: Students were significantly more willingness to use AI compared to professionals. This suggests that openness to these technologies may vary across stages of professional development. 
• AI literacy: This captures individuals’ awareness and understanding of how AI systems operate. Here, clinicians and participants with higher levels of AI literacy were more willing to adopt these tools in their practice. 
• Technology resistance: The measure incorporates a reluctance towards adopting new technologies. The findings showed that participants with higher technology resistance were less willing to engage with AI. 
• Risk perception: This reflects how individuals evaluate the potential dangers or uncertainties associated with AI. Participants who had higher risk perceptions of AI were less willing to use these tools in practice.

Discussion 

The findings of this study reinforced that the psychological and organisational factors play an important role in shaping AI adoption within healthcare settings. Risk perception emerged as an important predictor: clinicians and students who viewed AI as uncertain or unsafe were less inclined to adopt these technologies. This is consistent with wider evidence showing that in high-stakes environments such as healthcare, potential losses often carry more weight than possible gains (Kahneman & Tversky, 1979). In practice, this reflects a professional culture that prioritises patient safety and accountability (Warrington & Holm, 2024). 

Alongside risk, AI literacy emerged as the strongest influence on willingness to adopt these technologies in clinical practice. These findings extend previous work with nursing students, where Sumengen et al. (2024) demonstrated that AI literacy was an important factor influencing willingness – the present study extends these findings to a wider range of healthcare professions. Additionally, students were more willing to adopt that compared to current healthcare professionals. This suggests that openness to these technologies seems to vary at different stages of an individual’s professional development. These findings highlight the need for structured training that develops individuals’ confidence and competence in using AI, ensuring that both current and future professionals are adequately supported. 

Contrary to expectations, higher occupational self-efficacy was linked to reduced willingness to adopt AI. This finding suggests that clinicians who are more confident in their own judgement are less willing to rely on AI, as they place greater trust in their own expertise than in external technological support. While self-efficacy is usually seen as a protective resource that fosters resilience (Bandura, 2001), in this context it may inadvertently limit openness to innovation. 

Together, these findings highlight that adoption ultimately depends on the interplay of psychological resources, professional identity, and organisational culture. Addressing an individual’s perceptions of risk, enhancing AI literacy, and embedding supportive organisational practices are important here. By prioritising these factors, healthcare systems can foster the meaningful integration of AI that strengthens rather than undermining clinical practice.

Blog references

Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1). https://doi.org/10.1186/s12911-020-01332-6

Bandura, A. (2001). Social Cognitive Theory: an Agentic Perspective. Annual Review of Psychology, 52(1), 1–26. https://doi.org/10.1146/annurev.psych.52.1.1

Choudhury, A., Asan, O., & Medow, J. E. (2022). Effect of risk, expectancy, and trust on clinicians’ intent to use an artificial intelligence system — Blood Utilization Calculator. Applied Ergonomics, 101, 103708. https://doi.org/10.1016/j.apergo.2022.103708

Dingel, J., Kleine, A., Cecil, J., Sigl, A., Lermer, E., & Gaube, S. (2024). Predictors of Healthcare practitioners’ intention to use AI-Enabled Clinical Decision Support Systems (AI-CDSSS): A Meta-Analysis based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Preprint). Journal of Medical Internet Research. https://doi.org/10.2196/57224

Huang, C., Clayton, E. A., Matyunina, L. V., McDonald, L. D., Benigno, B. B., Vannberg, F., & McDonald, J. F. (2018). Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-34753-5

Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263. https://doi.org/10.2307/1914185

Küper, A., Lodde, G. C., Livingstone, E., Schadendorf, D., & Krämer, N. (2025). Psychological Factors Influencing Appropriate Reliance on AI-enabled Clinical Decision Support Systems: an Experimental Online Study Among Dermatologists (Preprint). Journal of Medical Internet Research, 27, e58660. https://doi.org/10.2196/58660

Matsuo, T., Yoshioka, T., Okubo, R., Nagasaki, K., & Tabuchi, T. (2022). Burnout and its associated factors among healthcare workers and the general working population in Japan during the COVID-19 pandemic: a nationwide cross-sectional internet-based study. BMJ Open, 12(11), e064716. https://doi.org/10.1136/bmjopen-2022-064716

Nagarajan, R., Ramachandran, P., Dilipkumar, R., & Kaur, P. (2024). Global estimate of burnout among the public health workforce: a systematic review and meta-analysis. Human Resources for Health, 22(1). https://doi.org/10.1186/s12960-024-00917-w

Rigotti, T., Schyns, B., & Mohr, G. (2008). A short version of the Occupational Self-Efficacy Scale: Structural and Construct Validity across five countries. Journal of Career Assessment, 16(2), 238–255. https://doi.org/10.1177/1069072707305763

Sumengen, A. A., Subasi, D. O., & Cakir, G. N. (2024). Nursing students’ attitudes and literacy toward artificial intelligence: a cross-sectional study. Teaching and Learning in Nursing. https://doi.org/10.1016/j.teln.2024.10.022

Warrington, D. J., & Holm, S. (2024). Healthcare ethics and artificial intelligence: a UK doctor survey. BMJ Open,14(12), e089090. https://doi.org/10.1136/bmjopen-2024-089090

By Dela Glevey (24-25)

When you think about getting ready for work, what’s on your mind? Picking an outfit? Checking your diary for the day ahead? For many Black women, one of the first questions is: what will my hair say about me today?

That might sound small, but it’s not. Hair, particularly Black hair, is never “just hair”. It is loaded with identity, heritage and culture. While in the workplace, it can also be a source of judgement, bias and daily stress. Recent research has set out to explore how Black British women navigate their hair choices, professional image and identity at work, here’s what the latest study finds.

Why Hair Matters at Work

To understand why hair matters, we need to step back. For centuries, Eurocentric styles have been pushed as the standard of beauty. In workplaces these standards have shaped what it means to be “professional”. Straight, sleek hair is seen as neutral, tidy and respectable. While natural Afro-textured styles (braids, twists, Afros, locs etc) are seen as unruly, distracting and “unprofessional”.

The evidence backs this up. The Black British Voice project (2023) found almost all respondents (98%) felt pressure to change themselves in the workplace, including hair, to “fit in”. The Broken Ladders Report (2022) found a quarter of women of colour had to alter their hair at work. While, research has shown natural styles are rated less professional or employable than straight styles by hiring managers (Donahoo, 2022; Koval & Rosette, 2020; De Leon, 2023).

These findings highlight what many Black women already know: hair is never just about style. It influences how they are seen, how they feel and the opportunities available to them.

The study

Despite growing recognition of hair bias in the US, there is a vast lack of UK focused research. To address this, 15 Black British women working across industries took part in interviews and a photograph exercise. Participants shared two photos: one when they felt “most like me” and one where they felt “most professional”.

The contrast was powerful. The simple exercise highlighted the tension between showing up as themselves, using words around authenticity and confidence in the “most like me” map, while words use for the “professional” photo spoke to pressures to conform to workplace expectations.

Figure 1 “Most like me” (top) and “Most professional” (bottom) word clouds

From the interviews three overall themes were developed- Spheres of Influence, Strategic Styling and Navigating the Personal Journey. However, two main overarching ideas stood out: the daily managing of hair and identity and the longer “hair journey” across their careers.

Finding 1: Managing hair and Identity

The first key finding was the sheer amount of thought and energy that went into managing hair on a daily basis. The women described a constant balancing act, managing external perceptions and internal anxieties. Interviews, client meetings or senior audiences often shaped their styling decisions, with Eurocentric looks favoured to minimise risk of questions, touching and doubts over their credibility. This daily management was rarely about preference, instead a strategy for avoiding negative stigma, stereotypes or unwanted attention. In practice hair became a tool of impression management, understood to remove hurdles and allow them to stand out for their work. Echoing Social-Identity Based Impression Management (SIM;Roberts, 2005), which describes how people from marginalised groups manage aspects of their identity to “fit in.”

The cost of managing, however, was high. Participants spoke of planning hairstyles weeks in advance, sticking to consistent and predictable ‘office safe’ looks, or dealing with the constant low-level anxiety of wigs slipping or curls frizzing. This drained energy that could otherwise be used on their work.

Finding 2: The “Hair Journey”

Beyond daily management, women also described a longer “hair journey” across their careers. Early in their careers, most adopted conservative, Eurocentric styles as a form of protection. Straightened or tied-back hair was viewed as the safest way to establish credibility and avoid being singled out.

But over time many women described a shift. With greater seniority, life experience, or personal turning points, they began to embrace authenticity. For some, this gave them freedom to wear natural styles, feeling they had earned the right to wear their hair naturally. Others mentioned milestones such as parenthood, a new job, relocation or even the pandemic as moments which inspired them to reconsider hair choice.

The sectors they worked in also mattered. Finance and fashion were described as stricter, with unwritten Eurocentric norms, while teaching and creative fields were seen as more open. This suggests both workplace culture and career stage shape how black women feel. Supporting what Black Feminist Thought (BFT;Collins, 2009) proposes, these choices are not just personal but shaped by wider systems. What is seen as “professional” is not neutral, but rooted in whiteness and many women describe having to unlearn this thinking and rebuild confidence in their natural selves.

Practical Implications

So, what can you do with all this? For Black women, these strategies shouldn’t be necessary, but supportive networks can help ease the burden of code-switching and build confidence. For colleagues, respect boundaries, avoid intrusive questions and challenge bias when you see them. For leaders, step up and use your influence to model inclusion. For organisations, review your policies, train managers and hiring teams on hair bias, ensure representation and co-design solutions appropriate for your workplace. Why not make a visible first step and adopt the Halo Code. The goal? Workplaces where Black women don’t need to second-guess their hair and can turn up authentically.

Conclusion

This study showed for Black Women hair is not just about style, it is central to their identity, confidence and how professionalism is judged. The findings highlight two key threads: the daily effort of managing their hair and the longer “hair journey” over their career. Both reflect how Eurocentric pressures still shape workplace expectations. While the findings support SIM and BFT they also suggest there is an emotional weight not fully captured by either theory. Making space for this issue will not only allow Black women to show up fully, but in doing so create more inclusive, creative and productive workforces.

References

Black British Voice Project. (2023). In Black British Voice (pp. 1–104). University of Cambridge. https://www.bbvp.org/

Collins, P. H. (2009). Black Feminist Thought: Knowledge, Consciousness, and the Politics of Empowerment. Routledge. https://doi.org/10.4324/9780203900055

De Leon, M. (2023). Workplace Hair Acceptance Report. In https://www.worldafroday.com/wp-content/uploads/2023/09/Workplace-Hair-Acceptance-Report-2023.pdf.

Donahoo, S. (2022). Working with style: Black women, black hair, and professionalism. Gender, Work & Organization, 30(2), 596–611. https://doi.org/10.1111/gwao.12838

Gyimah, M., Azad, Z., Begum, S., Kapoor, A., Ville, L., Henderson, A., & Dey, M. (2022). Broken Ladders: The myth of meritocracy for women of colour in the workplace. Fawcett Society and Runnymede Trust. https://cdn.prod.website-files.com/61488f992b58e687f1108c7c/628cf1924ac4e10b1ba8917b_Fawcett%20%26%20Runnymede%20Trust%20-%20Broken%20Ladders%20(final).pdf

Halo Collective. (n.d.). Hair Discrimination in Workplace. Halo Collective. Retrieved September 2025, from https://www.halocollective.co.uk/halo-workplace

Koval, C. Z., & Rosette, A. S. (2020). The Natural Hair Bias in Job Recruitment. Social Psychological and Personality Science, 12(5), 194855062093793. https://doi.org/10.1177/1948550620937937

Roberts, L. M. (2005). Changing Faces: Professional Image Construction In Diverse Organizational Settings. Academy of Management Review, 30(4), 685–711. https://doi.org/10.5465/amr.2005.18378873

By Archie Allen (24-25)

Artificial Intelligence (AI) is no longer a futuristic concept, it is here, shaping how organisations work, make decisions, and compete. From automating repetitive tasks to enhancing data-driven insights, AI has the potential to transform entire industries. But as organisations rush to adopt these tools, an equally important question arises: what kind of leadership do we need in this new era?

That was the central focus of my research. For my MSc dissertation, I interviewed twelve senior leaders and leadership development professionals across industries, from healthcare to finance to technology, to explore what human leadership looks like in the AI era. The answers were clear: leadership needs to become more human than ever.

Why leadership needs to change

AI adoption is happening at speed. Unlike past technological revolutions that unfolded over decades, today’s shift is taking place in just a few years. Leaders are navigating uncharted territory: employees are unsure about how AI will affect their roles, organisations face ethical dilemmas around fairness and transparency, and many people are simply overwhelmed by the pace of change. My research shows that what people most want from leaders today is not more data or more certainty, but more honesty, empathy, and human connection.

What was the research?

To answer the research questions, what human competencies are required to lead in the AI era, and why are they necessary, I conducted twelve in-depth interviews with senior leaders and leadership experts. The participants ranged from Chief HR Officers and Managing Directors to organisational psychologists and leadership consultants. Together, they represented decades of experience leading teams and shaping leadership development. The interviews were analysed using reflexive thematic analysis, which looks for patterns of meaning across conversations. From this, three themes emerged that capture the essence of human leadership in the AI era: human leadership, relational leadership, and visionary leadership.

Theme 1: Human Leadership

The first theme centred on integrity, empathy, and credibility. Participants described transparency as vital, even when the message is difficult. Employees want leaders who show up as real people, not polished robots. Empathy was also seen as critical, helping leaders respond to the mix of excitement, fear, and resistance that AI often generates. Finally, many warned against letting AI erode credibility. Leaders who rely too heavily on AI-generated communication risk appearing inauthentic and losing trust. The message is clear: in a world where machines process information faster than us, credibility comes from staying unmistakably human.

Theme 2: Relational Leadership

The second theme focused on how leaders relate to others. Humility was described as a powerful quality. Leaders who admitted uncertainty or mistakes were seen as strengthening connection rather than weakening authority. This vulnerability encouraged others to be open too, creating a culture of learning rather than fear. Psychological safety was also seen as essential. In the context of AI, employees need reassurance that experimenting, failing, and asking questions are not only allowed but expected. Leaders who modelled openness and normalised mistakes created the conditions for innovation and adaptability.

Theme 3: Visionary Leadership

The final theme highlighted the role of vision. When vision was meaningful, it helped anchor people during disruption by connecting daily work to a bigger, human purpose. Leaders who could tell this story reduced anxiety and built trust. But when vision was absent, employees were left to fill the gaps with their own narratives, often fuelled by fear and uncertainty. The lesson is that leaders do not need to have all the answers, but they do need to provide a sense of direction that feels steady, human, and hopeful.

Why these findings matter for organisations

The success of AI adoption depends not just on the technology itself, but on how people respond to it. If employees feel anxious, excluded, or silenced, they are less likely to engage with new systems. But if they feel heard, supported, and inspired, they are more willing to experiment, adapt, and innovate. This means organisations need to rethink how they prepare their leaders. Leadership development should make empathy, humility, and ethical communication core priorities, not optional extras. Building psychological safety should be treated as a deliberate leadership practice, one that allows teams to learn and adapt without fear. And leaders must remain present in their communication, ensuring that human tone and visibility are not lost when supported by AI tools.

Bringing theory into practice

Much leadership theory already points to these ideas, emotional intelligence, authentic leadership, transformational vision. What this research shows is that in the AI era, these are not luxuries but necessities. As machines take on more technical and cognitive tasks, the distinct value of human leadership lies in things AI cannot replicate: empathy, integrity, humility, and the ability tocreate shared meaning. Put simply, the more AI we have in the workplace, the more human our leaders need to be.

Conclusion

My dissertation set out to answer two questions: what are the human leadership competencies required in the AI era, and why are they necessary? The findings suggest that employees are looking for leaders who show up with honesty and empathy, who build trust and collaboration through humility and psychological safety, and who provide grounded and meaningful direction in uncertain times. AI will keep evolving, but these human needs remain constant. For organisations, the practical takeaway is clear: technology alone does not guarantee success. What makes the difference is leaders who bring a distinctly human presence, leaders who can connect, communicate, and create meaning in ways no machine ever could.

By Katerina Breznenova (24-25)

Dance communities tend to show higher levels of perfectionism and there is an ever-present discussion about if the dance environment pushing towards beautiful movement with spotless technique shapes dancers into having perfectionists or if it attracts people who already are perfectionist – or a combination of both. This study combines perfectionism with its ‘younger sibling’, a fairly recent concept called excellencism, with creativity and self-compassion with the aim to explore connections that have not been investigated yet.

Why dancers and dance teachers? 

I have been a dancer for 20 years and teacher for 2 years, leading me to many observations on these and similar topics. Being part of the industry allowed me to connect this with real data and reach out to many fellow dancers and teachers, gathering data within the population.

This study brings a novel distinction of dancers into several categories: hobby (for fun, little to no ambitions), ambitious (has goals but is not professional), part-time teachers (who teach alongside having another job to be able to support themselves) and full-time teachers (who rely solely on dancing for a living). The distinction arises from my informal observations alongside the lack of prior research on this.

Most studies only look at dance students or professional performers, but this one also focuses on teachers as a separate group. Teaching dance involves maintaining own dance skills, managing classes, choreographing, and often handling business and marketing tasks, all amid financial and job insecurity. In this sample, nearly all teachers relied on additional employment, reflecting the instable nature of the profession. 

Perfectionism vs. excellencism

Perfectionism includes striving for flawlessness and setting unrealistically high standards, often causing frustration and overly critical self-evaluations when not met. Over the last 30 years, lots of research has been done to understand this tendency and create frameworks to characterise it, with the baseline being set by Frost et al. in 1990 and Hewitt and Flett in 1991. Since then, there has been major development in our understanding of perfectionism, and newer frameworks emerged, such as the Big Three Perfectionism by Smith et al. in 2016 which is used in this study. 

The Big Three Perfectionism framework divides perfectionism into 3 types: rigid, self-critical and narcissistic. Rigid perfectionism describes the rigid insistence that we must be flawless, perfect and make no mistakes, which then impacts our sense of self-worth. Self-critical perfectionism combines concern over mistakes, doubts about actions, harsh self-criticism and the tendency to perceive others as demanding perfection from us. And finally, narcissistic perfectionism captures the tendency to hold unrealistic expectations of others, being hypercritical of others, belief of entitlement to special treatment and belief of superiority.

Excellencism is a newer concept introduced by Gaudreau in 2019, describing individuals who still strive for excellent performance and set high goals, these goals are reachable and allow satisfaction once reached, unlike perfectionistic goals which are usually beyond reachability. Previous research reports excellencism as the more advantageous approach compared to perfectionism, and this has been tested in academic setting, sports, work setting and other areas, leading to experiencing more enjoyment, less stress and workaholism, and having more growth experiences. This study finds similar results in dancers, supporting the idea that excellencism is more useful than perfectionism.

Creativity as a dancer’s tool

Creativity is a highly desirable skill for dancers, allowing them to explore movement, build new connections and create original choreographies. For teachers, creativity extends beyond movement to lesson planning, creating exercises for students and self-promotion marketing to get more work. In this study, creativity was measured through creative self-efficacy and creative personal identity. Interestingly, teachers reported greater creativity than non-teachers, which may stem from both the demands of professional teaching and the heightened awareness of creativity’s role in sustaining a career.

Previous research reports that some kinds of perfectionism may enhance creativity by pushing oneself to do better but others can hinder it. This study tested if excellencism has any advantage for creativity over perfectionism, and it does! The results suggest that dancers who pursue excellencism see themselves as more creative than those who pursue perfectionism, aligning with research on non-dance samples. In practice, this is something that could be encouraged in dance students, to set high but realistic goals and potentially limit the frustration that comes when the learning process is not as fast or seamless as initially (unrealistically) expected. 

When comparing different types of perfectionism, narcissistic perfectionism was associated with higher creativity, self-critical perfectionism with lower creativity and rigid perfectionism showed no associations whatsoever. This is likely because self-reported creativity is enhanced in individuals having narcissistic perfectionism traits, such as feelings of superiority or entitlement. On the other hand, self-critical perfectionism is a strong contrast, bringing the inner critic, fixating on mistakes and feeling that others expect them to be perfect which they believe they are not. 

Self-compassion is a coachable skill, could it help?

Additionally, this study tested self-compassion as a potential bridge between perfectionism and creativity, expecting that it could soften the effect of perfectionism on creativity. Previous research confirmed that self-compassion is coachable, facilitates creative originality, curiosity and exploration, and softens the negative effect of perfectionism on wellbeing. While this study did not confirm a connection between self-compassion and creativity, that does not reject the proposed impact, it highlights that this relationship may be more nuanced than expected and requires more research before making any conclusions. It was however found that  all Big Three Perfectionism types are associated with lower self-compassion.

Dance commitment as a novel but key element

Commitment to dance seems to correlate with certain dimensions of perfectionism and creativity, with more committed dancers generally displaying higher excellencism. It appears that as dancers advance and establish clearer goals, striving for excellence emerges as a defining trait of ambitious dancers and teachers, suggesting that an excellencist approach may foster overall improvement more effectively than a purely perfectionist mindset.

The study originally aimed to compare part- and full-time teachers, but the small number of full-time participants limited this analysis. Instead, comparisons between teachers and non-teachers indicated that teachers generally report higher levels of excellencism, narcissistic and socially prescribed perfectionism, and creativity. Whether these traits are fostered by teaching or draw individuals into it remains unclear, highlighting the need for further research to examine these preliminary patterns. The aforementioned struggles experienced by teachers may explain the higher levels of socially prescribed perfectionism, as teachers often operate under heightened scrutiny of their skills and presence. 

In conclusion, this study highlights that striving for excellence rather than perfection can support creativity in dancers and teachers. Encouraging an excellencist mindset, alongside cultivating self-compassion, may help dancers grow and navigate the challenges of their art with greater satisfaction.

By Dolores Hill (24-25)

As rates of depression, anxiety, and burnout continue to rise in employees, organisations are keen to find ways to support their workers to thrive (Chartered Institute of Personnel and Development, 2025). One way to do this is to help employees to recover from work and in turn support their wellbeing and performance (Sonnentag et al., 2022) through effective leisure time activities (Petrou & de Vries, 2025). 

Some psychologists have focused on the need to recover from effort spent at work (Meijman & Mulder, 1998)and others have described the need to invest resources to gain more back (Hobfoll, 2018). Sonnentag and Fritz (2007) identified four key experiences for recovery: Relaxation, Control, Mastery (learning new skills and feeling competent), and Psychological Detachment (switching off from work). Newman et al. (2014) built on this and developed the DRAMMA framework which identified five wellbeing needs that leisure activities should satisfy: Detachment-Recovery, Autonomy, Mastery, Meaning, and Affiliation. The addition of Meaning and Affiliation was based on wellbeing research that emphasised how important they are for creating a deep sense of life satisfaction and optimal functioning, also known as eudaimonia (Ryff, 1989). 

Research suggests creative hobbies offer many of the essential recovery and wellbeing experiences identified by Sonnentag and Fritz (2007), and Newman et al. (2014) (Eschelman, 2014; Alameer et al., 2023). Furthermore, people who engage in creative hobbies during their leisure time not only feel better, but they also perform better at work and in their personal lives (de Bloom, 2018). Creative interventions have been found to be cost effective and are often more accessible than other recovery activities (WHO, 2019). This demonstrates that employees engaging in creative hobbies can benefit organisations as well. Despite this, creative hobbies have been somewhat overlooked in the employee recovery and wellbeing literature, and although rates of people engaging in at-home creative hobbies are rising, it appears that creative hobbies may be an underused tool for supporting employee wellbeing and recovery.

I set out to address this gap by asking creative hobbyists about their experiences during their hobbies, and to share insights into the impact on their wellbeing and experiences at work. I interviewed 18 participants who had creative hobbies, asking questions about the hobby itself, their jobs, and the relationship between the two aspects of their lives. I used a Thematic Analysis to analyse their responses, which is a technique in qualitative research that involves carefully reading the transcripts, labelling quotes with different ‘codes’, and then exploring ways to group those codes together into themes that meaningfully reflect the participants’ feelings. At the end of this process, I had identified three key themes titled Recuperating, Revitalising, and Reaffirming, which each represented two experiences and two impacts of the creative hobbies.

The Recuperating experience of Mental Respite was linked to the concept of Psychological Detachment, as participants talked about switching off, relaxing, and de-stressing after work. Some of them used their hobby as a chance to Independently Process their feelings from the day and make plans for the next one. These experiences meant the participants felt Recharged and Ready for Work.

The Revitalising experiences aligned more closely with COR (Hobfoll, 2018) as even though participants had used lots of resources at work, they still dedicated more time and energy to their hobby to Learn Something New and have Fun. Learning Something New met their need for Mastery, as they described using a different part of their brain, flexed their creative muscles, and became more accepting of mistakes. This experience developed their Creative Thinking and contributed to them feeling more Confident at work. A creative hobby was sometimes a chance to have Fun with others, but for many of the participants, they had the feeling of “childlike excitement” even on their own. This experience of enjoyment can be described as hedonia, the aspect of wellbeing associated with pleasure and a good mood (Deci & Ryan, 2008). Hedonia can be short-lived, and alone it does not account for the longer-term gains in wellbeing that people need to feel a true sense of life satisfaction (Ryff, 1989). Nonetheless, hedonic wellbeing is still worth supporting, and this finding is an important one for understanding how to motivate people to take up creative hobbies.

The final theme, Reaffirming, is more related to the other aspect of wellbeing, known as eudaimonia. Ryff’s (1989) model of eudaimonic wellbeing identified six components: Autonomy, Purpose, Mastery, Self-Acceptance, Positive Relationships, and Personal Growth. Having a chance to do something ‘Just for Me’ and Reaffirm their Sense of Self clearly supported the components of Autonomy and Purpose, as well as Self-Acceptance and Personal Growth, leading to a spill over effect that contributed to improved Morale at work and better Personal and Professional Performance. The participants described a sense of holistic wellbeing and satisfaction due to engaging in their hobbies. For some, this was essential and helped them cope with the demands of work, and for others it made an already pleasant job even more enjoyable.

Based on these findings, Figure 1 illustrates a proposed framework for the experiences and impacts of creative hobbies and highlights the spill over effects from wellbeing and recovery into people’s professional and wider personal lives.

Figure 1 

A framework for the experiences and impacts of creative hobbies

More research is needed to fully understand what creative hobbies offer employees and organisations, and it will be particularly important to study different groups of individuals to uncover any relationships between personality, neurodivergence, and the uptake and impact of creative hobbies. Equally, future research should seek to understand the way job traits influence the recovery experiences employees seek out.

This study contributes to the literature demonstrating that creative hobbies can have a significant impact on employee wellbeing, recovery, and performance. Not only do they offer similar experiences to other leisure time activities, meeting the needs for recovery and wellbeing, but they may offer unique advantages such as mindful Independent Processing and the chance to have Fun.

Organisations can put these findings into action by:

• Spotlighting and celebrating creative role models in the organisation to establish a culture of creative recovery, and encourage other employees to have a go
• Sharing information about the psychological underpinning of creative hobbies and the impacts they can have on wellbeing and performance, emphasising the variety of creative activities and the different experiences they can offer
• Offering flexible working hours to support employees to take part in creative activities more easily
• Supplying vouchers for materials and resources to make creative hobbies more accessible to all
• Running creative wellbeing initiatives within the organisation to develop a collaborative culture of creativity

By Tina Kamel (24-25)

I’ve worked in both worlds: the scrappy pace of startups and the structured reality of big companies. Watching leaders in each made one thing clear: the behaviours that make you a brilliant leader at 20 or 40 people don’t sustain a 300-person organisation, and vice versa. So I asked: what happens when founders must cross that gap? How do they shift from doing everything to designing systems others can run? And how do those shifts affect employees’ belonging, alignment, and decision to stay? To answer, I interviewed eight founders who scaled from ≤50 to ≥250 people, analysing what they stopped, started, and reweighted. Three leadership shifts emerged with practical lessons.

Why this is important

People often join startups for the buzz: fast decisions, incredible innovation, everyone knows everyone. As headcount grows, the company changes, new managers join, processes multiply, and the founder can’t be everywhere anymore. Handled well, this phase can unlock performance and keep people proud to be there. Handled poorly, it can drain energy, create confusion, and push good people out. The difference often comes down to how the founder evolves.

What I did

I conducted in-depth, confidential interviews with eight founders/leaders who personally led growth from ≤50 to ≥250 employees, across a mix of industries. I looked for patterns in what they actually did, what they stopped doing, and how they believed those moves shaped culture and people outcomes. Three big leadership shifts emerged, each with very practical implications.

What we found: Three shifts that help companies scale

1) Architecting for scale

Early on, every founder described being “in everything”. As teams expanded, seven of the eight leaders deliberately installed leadership layers, clarified who decides what, and formalised decision routines (think: regular leadership discussions, a simple set of metrics reviewed regularly, and clear owners for key goals). Crucially, their own role changed: from operator to strategist and communicator. They spent more time setting direction, aligning leaders, and reinforcing the “why”, and less time solving today’s tactical problem.

Why organisations should care: Clear ownership and predictable decision routines reduce bottlenecks and cross-team friction. When the founder shows up as a strategist-communicator (for example, through monthly all-hands and site visits), people hear a consistent story about where the company is going and how their work fits. That builds alignment and speeds execution.

2) People-centred leadership and culture

Five founders talked about becoming “last to speak, first to listen”. Seven described delegating more, but not blindly. The rule was autonomy when ready: give people room to run when capability and support are in place, and pair it with mentoring and time-boxed trials that can be reversed without consequences. Leaders also made culture visible in action, not just words. Several leaders spoke of backing staff when disrespected or addressing toxic behaviour quickly, which makes values real across layers and locations.

Why organisations should care: Matched autonomy plus psychological safety fuels belonging and better decisions. When leaders enforce values in visible ways, employees trust that “what we say we believe” is not just a poster on a wall. That keeps culture coherent when the founder isn’t in every room.

3) A talent and development engine

Six founders raised the hiring bar for senior roles and surrounded themselves with strong specialists, often saying, in plain terms, “hire people better than you and trust them”. Four built repeatable development mechanisms (mentoring programs, funded education, structured training) and made careers visible (clear role levels, promotion criteria, and succession). The combination mattered: structure without skill-building frustrated people; training without visible paths encouraged them to take their new skills elsewhere. Retention improved when both worked hand-in-hand.

Why organisations should care: People stay when they can see where they can go and feel supported to get there. A credible internal pipeline reduces costly external hiring and protects hard-won culture.

How these shifts affect employees

Founders linked the three shifts to three human outcomes:

• Belonging: People felt trusted when autonomy matched readiness, they had a voice in healthy debates, and roles were clear. Belonging showed up as confidence, pride, and stronger team identity.
• Alignment: Purpose and values were codified in simple language, then reinforced by behaviour. Regular, founder-led communication kept the mission and priorities front-of-mind as layers grew.
• Commitment: Employees were more likely to stay when they saw investment in their growth, fair and transparent rewards, and visible care during tough moments (for example, practical support in crises).

These aren’t “soft” wins. They translate into lower friction, faster decisions, better execution, and reduced turnover; the foundations of sustainable growth.

What organisations can do next

• Write down the founder’s role shift: What decisions will they stop and keep? Then, schedule a steady communication rhythm (monthly all-hands, quarterly roadshows).
• Grant autonomy when employees are ready: Define readiness (skills, track record, support) and pair it with mentoring and reversible, time-boxed trials.
• Make values visible: Set non-negotiables and act consistently when they’re tested.
• Hire leaders who raise the standard: People better than you in their domain, and trust them.
• Build structure and capability together by publishing career paths and funding mentoring, stretch roles, and targeted learning.
• Reward fairly and transparently, including team targets where suitable.

What makes this different from generic “leadership tips”?

Three things. First, it’s grounded in the lived experience of founders who have already navigated the transition from small team to large company. Second, it translates ideas into observable behaviours (e.g., who decides what; how often you communicate; what you do when values are tested). Third, it clarifies conditions: autonomy works when people are ready; culture sticks when leaders act; retention is strongest when structure and capability move together.

A final word to founders (and those who support them)

If you’re in that messy middle, too big to run on hustle and not yet built for scale, your job is changing! You don’t have to become someone else, but you do have to evaluate how you lead. Do these things, and you’ll do more than grow a company. You’ll build a place where people belong, pull in the same direction, and choose to stay, exactly the conditions that turn early promise into lasting performance.

By Enya Miller (24-25)

Artificial intelligence (AI) is no longer just a concept for the future; it’s already integrated into many workplaces. From chatbots responding to customer inquiries to software that monitors employee productivity, AI is transforming organisational operations. For employers, these tools offer the promise of a more productive workforce. However, for employees, AI monitoring can often feel quite different: intrusive, unfair, and for some, threatening.  

How does AI monitoring impact employees’ trust in their employers? Trust is essential to bind workplace relationships. When it’s lacking, employees can become demotivated, disengaged, or even resentful towards their organisation.  

Why trust matters at work:

When individuals have trust in their employer, they are more inclined to share ideas, collaborate, and remain loyal to the organisation.  

However, trust is delicate. Suppose employees perceive themselves as being under constant surveillance, especially from a machine they don’t completely comprehend. In that case, they may see this as a signal that their employer questions their competence. Studies have indicated that when trust drops, employees may resist, disengage, or even depart from the organisation.  

Therefore, the stakes are significant: implementing AI monitoring could enhance oversight, but it also threatens to undermine the very trust that organisations need to succeed.  

What I did in this study:

I conducted an online study involving over 100 working adults from varying work sectors. Rather than monitoring them directly, participants were presented with short scenarios (referred to as vignettes) depicting a workplace situation.  

Some scenarios featured AI monitoring (for instance, software tracking engagement, health and safety, or well-being).  

Others illustrated human monitoring (where a human manager was responsible).  

Participants also completed a baseline trust measure prior to encountering any monitoring.  

Following each scenario, participants evaluated how much they trusted their employer.  

The study also asked: did they feel the AI was acting in their best interest?  

What I found:

The results indicated: trust was significantly lower when AI monitored employees compared to being monitored by humans or not monitored at all.  

Interestingly, my study did not uncover strong evidence that the monitoring domain (engagement, well-being, or health and safety) significantly impacted trust levels. Even when AI was monitoring for positive reasons, such as ensuring employee safety, the decline in trust persisted.  

However, there was one encouraging finding. Employees who felt that AI was being utilised fairly and in their best interest reported higher levels of trust. This highlights that the way organisations introduce, explain, and apply AI can significantly influence outcomes.  

Why does this matter for organisations?

For managers, leaders, and HR professionals, these findings suggest:  

AI undermines autonomy. Self-Determination Theory suggests that employees need autonomy, competence, and relatedness to stay motivated. AI monitoring risks autonomy, making individuals feel controlled rather than empowered.  

Psychological safety is at risk. A workplace should be an environment where employees feel safe to take risks, make mistakes, and be themselves without fear of judgment. AI monitoring can create a sense of constant observation, resulting in pressure and anxiety.  

Fairness is crucial. Procedural Justice Theory indicates that individuals are more likely to accept decisions, even unfavourable ones, if they believe the process is fair. If employees perceive AI as being implemented ethically, transparently, and with their well-being in mind, trust is far more likely to endure.  

What should organisations do?  

Be transparent. Employees should be informed about what is being monitored, the reasons behind it, and how the data will be utilised. 

Emphasise support, not control. AI should be positioned as a tool for development, rather than solely for evaluation.

Build trust before technology. Organisations that prioritise strong relationships with their employees are better equipped to adopt new technologies without provoking resistance.  

Check for fairness. Regular reviews should be conducted to ensure that AI monitoring is applied consistently, without bias, and in alignment with employees’ expectations.  

Consider Leadership training to ensure empowered employees, to mitigate any effects of AI implementation. 

What this research adds  

This study contributes significantly to the ongoing discussion regarding AI in the workplace. It reveals that:  

The presence of AI monitoring is enough to diminish trust, regardless of its stated purpose.  

Perceived fairness and intention serve as critical buffers, helping to sustain trust even in the presence of AI.  

Organisations must carefully consider not just what AI monitors, but also how it is introduced and communicated to employees. 

Limitations and next steps  

Like all research, this study has its limitations. The scenarios were hypothetical, meaning participants didn’t undergo real monitoring. Future research should examine AI in actual workplace environments to determine if similar effects occur. My sample size was also limited, and group imbalance might have affected statistical power.  

Nevertheless, the findings offer a solid foundation for understanding employee responses to AI monitoring and guiding practical solutions. 

Final thoughts  

My research indicates that when AI monitoring is applied ethically, transparently, and with genuine concern for employees, AI could evolve into a supportive tool rather than a means of control.