Industry Insights

With the accelerating demand for continuous transformation, faster insights, and tighter governance, data science and AI teams are increasingly experiencing decision fatigue as they interpret complex model outputs under time pressure. At the same time, mass layoffs and large-scale workforce repurposing have intensified emotional exhaustion, directly impacting career stability, mental health, and long-term well-being.

For organizations undergoing AI-driven transformation, especially private equity–backed firms aiming for operational efficiency and improved profit margins, the hidden cost of decision fatigue is very real. It directly affects judgment quality, risk tolerance, ethical oversight, and, ultimately, the ability to positively influence EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization). As data and AI adoption accelerate, leaders must evolve not only how systems are designed but also how humans are supported in overseeing them.

As you read on, this article will examine how decision fatigue manifests in AI and data science roles, why traditional wellness approaches fall short, and how leaders can strengthen psychological safety through clear decision pathways, distributed oversight, and targeted training. It will also propose piloting evidence-based, non-traditional wellness interventions, including music therapy, art therapy, virtual reality, meditation, and mindfulness, to address workplace burnout at its cognitive and emotional roots.

Research and Insights That Support the Impact of Decision Fatigue and Burnout in AI & Data Science Roles:

Feng & Sarma (2025) surveyed 442 software developers across diverse organizations, roles, and levels of experience and found that GenAI adoption heightens burnout by increasing job demands, while job resources and positive perceptions of GenAI mitigate these effects, reframing adoption as an opportunity.

The ambiguity and lack of trustworthiness in AI have led to algorithmic vigilance (the constant need for humans to be “on guard” for errors, bias, or unintended consequences). Over time, this sustained vigilance significantly increases cognitive load and contributes to workforce burnout.  To address this burnout, Wong et al. (2023) identified the need to adopt an approach beyond stress management and to incorporate mental health design into everyday technologies to support promotion, prevention, and intervention.

​​Further research by Valtonen et al. (2025) shows that while AI technologies can augment and accelerate human work through real-time data collection and analysis, the intensity and granularity of this monitoring can place significant psychological strain on employees. Opaque “black box” decision-making, algorithmic bias, and continuous performance surveillance may erode employees’ sense of agency, increase anxiety, and contribute to chronic stress – especially when individuals feel unable to question or understand decisions that directly affect them. Over time, these dynamics can erode trust, exacerbate power imbalances, and undermine mental well-being and sustainable productivity.

The Mental Health Impact of AI-Driven Decision Work

From a behavioral health perspective, it's observed that teams face three patterns:

• Decision fatigue — individuals are required to review dozens of micro-decisions a day.
• Moral distress — when the AI recommendation conflicts with human judgment, especially around equity or fairness.
• Isolation — oversight roles often happen individually at a screen, without team dialogue that traditionally helps people process decisions.

So, while AI is designed to reduce workload, humans often report an invisible increase in cognitive and emotional labor.

What Organizations Can Do: Designing for Psychological Safety in AI-Driven Work 

This is where AI governance must extend beyond technical safeguards to include human-centered decision design—ensuring that the people responsible for oversight, escalation, and ethical judgment are supported, trained, and protected from chronic cognitive overload. Three supports make a significant difference:

Clear Decision Rights and Escalation Pathways

People need to know:

• When they can override the model,
• How to document it,
• And that leadership will back them when they do.

This reduces fear and moral distress.

Distributed Overweight Models

Instead of one person constantly reviewing outputs, rotate responsibility or create micro-teams.This reduces monotony, distributes cognitive load, and introduces peer dialogue that builds psychological safety.

Training for Cognitive, Emotional, and Ethical Skills

Not just “How does the AI work?” but also:

• How to challenge outputs,
• How to identify bias,
• How to manage uncertainty,
• And how to maintain boundaries around screen-based oversight work.

Adopting “equity literacy for AI” helps people understand how inequities manifest in data and how to intervene.

Reframing the Human’s Role in AI Oversight 

Most importantly, we need to shift the mindset from:

“The human is here to catch errors" to “The human is here to ensure dignity, fairness, and context.”

This shift is foundational. It clarifies accountability, reduces fear-based decision-making, and enables AI systems to scale responsibly—without exhausting the very leaders and teams tasked with managing them.

A reframing reduces cognitive pressure by clarifying that humans are not competing with AI systems—they are responsible for safeguarding ethical judgment, equity, and situational awareness.

Leadership Behaviors That Build Psychological Safety for AI Adoption and Experimentation

Normalize questioning as a responsibility, not a disruption

Give your teams the license to vocalize concerns and question processes. “Your job isn’t to approve the model; your job is to challenge it.” That shifts the culture from passive acceptance to active stewardship.

De-stigmatize Overrides

We create clear processes for when and how to override model outputs — and we celebrate when someone catches a bias or error. If overrides are punished, psychological safety disappears instantly.

Create “Red Teams” for Ethical and Equity Reviews

These are diverse cross-functional groups that intentionally stress-test assumptions. It signals to everyone that dissent is not just allowed — it’s expected.

Document Decisions Transparently

When teams see how concerns lead to changes, it reinforces that speaking up has a real impact. Nothing builds psychological safety like visible accountability.

Model Humility as a Leader

Regularly say things like:“I may be missing something — who sees a risk I haven’t called out?”Leaders set the tone. When we show openness, others follow.‍

Wellness Beyond EAPs: Evidence-Based, Non-Traditional Approaches 

Emerging research shows that creative and non-traditional wellness interventions — such as art therapy, music therapy, meditation, or “low-stimulation” unwind spaces — can play a meaningful role in reducing burnout and supporting psychological well-being in high-stress environments. Art therapy programs, even in structured group formats, have been associated with significant reductions in emotional exhaustion, stress, anxiety, and burnout, with benefits sustained at follow-up, suggesting they help people process emotions and build resilience in ways traditional talk-based support sometimes doesn’t (Tjasink et al., 2025).

Music-based interventions in workplace settings have likewise shown positive effects on psychological and physiological markers of stress, improving mood and relaxation while reducing anxiety and work-related distress (Nyarubelli et al., 2005)

Beyond formal therapy, spaces designed for minimal sensory stimulation or mindfulness (like quiet rooms for meditation, breathing exercises, or restorative breaks) offer employees a chance to reset cognitive load and reduce chronic stress. Broader well-being research – including an exclusive article published in The Guardian (2024)- indicates that engagement with creative and restorative arts and cultural activities boosts emotional resilience, reduces symptoms of depression, and increases overall quality of life and productivity. 

Digital interventions, including cognitive-behavioral therapy, stress management, mindfulness/meditation programs, and self-help interventions, have shown promise for reducing stress, anxiety, depression, and burnout and improving psychological well-being when applied in workplace settings (Cameron et al., 2025). Research by Ppali et al. (2025) identified the benefits of a virtual reality app that offers stretching, guided meditation, and open exploration, and that also meets the diverse physical and mental health needs of knowledge workers. The app includes an AI assistant that suggests activities based on users' emotional states.

Together, these approaches help organizations expand the definition of “wellness” beyond fitness or EAP offerings toward holistic, psychologically supportive environments that address burnout at its emotional, cognitive, and social roots.

Conclusion

Decision fatigue and burnout among AI professionals and data scientists are no longer emerging risks—they are predictable outcomes of sustained cognitive overload, ethical vigilance, and constant pressure to make high-stakes judgments in complex systems.

When left unaddressed, these conditions quietly erode judgment quality, innovation, and trust in both people and technology. Leadership plays a defining role here. Leaders who model curiosity over certainty, normalize questioning of data and model outputs, clarify decision rights, and actively protect psychological safety create environments where professionals can think clearly rather than operate in survival mode.

Equally important is expanding how organizations think about well-being. Non-traditional wellness approaches—such as creative practices, mindfulness and low-stimulation spaces, reflective pauses, and restorative breaks—directly support the cognitive and emotional demands of AI work. These practices help reduce mental fatigue, restore focus, and sustain ethical reasoning, especially for teams tasked with continuous oversight and complex decision-making. When combined with supportive leadership behaviors, they shift wellness from an individual burden to an organizational capability.

Ultimately, organizations that address decision fatigue and burnout through human-centered leadership and innovative wellness strategies are better positioned to retain talent, sharpen decision-making, and strengthen trust in both people and AI systems. 

Supporting the psychological safety and well-being of AI professionals reduces burnout, sharpens decision-making, and strengthens trust in both people and AI systems.

Burtch Works and members of the Expert Network, like me, who extend beyond their organizations, have consistently observed this pattern in conversations with senior data leaders, AI architects, and private equity portfolio executives: the technical capability to deploy AI is outpacing the organizational readiness to support the humans overseeing it. Addressing decision fatigue is not a “soft” initiative—it is a prerequisite for sustainable AI performance and long-term talent retention.

References 

Cameron, G., Mulvenna, M., Ennis, E., O'Neill, S., Bond, R., Cameron, D., & Bunting, A. (2025). Effectiveness of digital mental health interventions in the workplace: umbrella review of systematic reviews. JMIR Mental Health, 12(1), e67785.

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Campbell, D. Editor. (2024, December 16). Consuming arts and culture is good for health and wellbeing, research finds. The Guardian. https://www.theguardian.com/society/2024/dec/17/consuming-arts-and-culture-is-good-for-health-and-wellbeing-research-finds?CMP=share_btn_url

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Feng, Z., Afroz, S., & Sarma, A. (2025). From Gains to Strains: Modeling Developer Burnout with GenAI Adoption. arXiv preprint arXiv:2510.07435.

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Nyarubeli, I.P., Moen, B.E., Krüger, V. et al. Music-based interventions in the workplace: a scoping review. BMC Complement Med Ther (2025). https://doi.org/10.1186/s12906-025-05221-1

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Ppali, S., Psallidopoulos, H., Constantinides, M., & Liarokapis, F. (2025, October). VR as a “Drop-In” Well-Being Tool for Knowledge Workers. In 2025 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 1213-1223). IEEE.

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Tjasink, M., Carr, C. E., Bassett, P., Soosaipillai, G., Ougrin, D., & Priebe, S. (2025). Art therapy to reduce burnout and mental distress in healthcare professionals in acute hospitals: a randomised controlled trial. BMJ Public Health, 3(2), e002251. https://doi.org/10.1136/bmjph-2024-002251 

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Valtonen, A., Saunila, M., Ukko, J., Treves, L., & Ritala, P. (2025). AI and employee well-being in the workplace: An empirical study. Journal of Business Research, 199, 115584.

Wong, N., Jackson, V., Van Der Hoek, A., Ahmed, I., Schueller, S. M., & Reddy, M. (2023, April). Mental Well-Being at Work: Perspectives of Software Engineers. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-15)

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