Why Your Best Employees Are Burning Out (And How Awe Might Save Them)

Awe as a Capacity  ·  10 min read  ·  March 6, 2026

Sarah was one of those employees every manager dreams of—high performer, early adopter, always first to volunteer for new initiatives. So when her company rolled out AI tools across the organization six months ago, she was the obvious choice to lead the pilot program. She dove in with characteristic enthusiasm, learning the tools, training her team, documenting best practices, fielding questions from colleagues across departments.

Three months later, she was on medical leave for stress-related illness.

Her manager was baffled. "She's brilliant with the tools. Usage rates on her team are through the roof. I don't understand what went wrong."

What went wrong is what's going wrong in organizations everywhere right now. We're asking people to integrate transformative technology at a pace that exceeds human capacity for sustainable change. We're measuring adoption by usage metrics while ignoring the invisible cost of continuous adaptation. And we're treating burnout as an individual failure rather than a systemic design flaw.

The data is stark. 61% of professionals report feeling overwhelmed by AI's pace of change [1]. Employee burnout rates have climbed to 76% globally [2], with the highest rates among high performers and mid-level managers—precisely the people organizations are leaning on most heavily to drive AI adoption. And perhaps most telling: 42% of employees say their organizations are moving too fast with AI implementation [3], yet few companies are slowing down.

This isn't a training problem. It's a nervous system problem. And the solution might be hiding in plain sight—in a capacity we've dismissed as soft, unmeasurable, or irrelevant to business outcomes. A capacity called awe.

What If Awe Is Infrastructure, Not Luxury?

When we talk about AI adoption challenges, we focus on technical training, change management, and productivity metrics. We rarely talk about the nervous system states required to learn, integrate, and sustain transformation. We treat wonder, curiosity, and awe as nice-to-haves—pleasant experiences that might emerge if we have time left over after the real work is done.

But what if we have it backwards? What if awe isn't a luxury that follows successful transformation, but the infrastructure that makes transformation possible?

This is the question Laura François, leadership consultant and former executive, explores in her work on awe as intelligence. In our recent podcast conversation, she made a provocative claim: "Awe is not an emotional response. It's a form of intelligence that dissolves ego, expands perception, and reshapes how we lead."

The neuroscience backs her up. Research from UC Berkeley's Greater Good Science Center shows that awe activates the default mode network—the brain's integration hub—while simultaneously reducing activity in regions associated with self-focus and threat detection [4]. In other words, awe doesn't just make us feel good. It literally changes how our brains process information, expanding our capacity to integrate complexity and reducing our perception of threat.

This matters enormously for AI adoption. Because the overwhelm Sarah experienced—and that 61% of professionals are reporting—isn't just psychological stress. It's a nervous system state that actively interferes with learning, integration, and sustainable performance.

The Nervous System Cost of Continuous Adaptation

Here's what most AI adoption strategies miss: the human nervous system evolved to handle change in manageable doses, with recovery periods in between. Continuous adaptation without rest triggers a chronic stress response that degrades precisely the capacities we need most—cognitive flexibility, creative problem-solving, collaborative intelligence, and the ability to integrate new information.

As Laura François explained in our conversation, "When we're in chronic stress mode, the brain narrows its focus to immediate threats. We lose access to the very capacities that make us most human—wonder, curiosity, the ability to see connections across domains. Awe is the antidote because it shifts us out of threat mode and into a state of expansive awareness."

The research confirms this. Studies show that awe experiences:

• Reduce stress hormones by up to 25% and lower inflammatory markers associated with chronic stress [5]. This isn't just about feeling better—it's about creating the physiological conditions for sustainable learning.
• Expand time perception, making people feel like they have more time available [6]. In a moment when 61% of professionals feel overwhelmed by pace, this perceptual shift is crucial. It changes our relationship to time, reducing the sense of scarcity that drives burnout.
• Increase prosocial behavior and collaborative intelligence [7]. People who experience awe are more likely to help others, share resources, and think in terms of collective rather than individual benefit—exactly what AI transformation requires.
• Enhance cognitive flexibility and creative problem-solving [8]. Awe experiences help people break out of habitual thinking patterns and see new possibilities—precisely what's required to integrate AI into existing workflows in ways that enhance rather than replace human expertise.

These aren't peripheral benefits. They're the core capacities required for sustainable AI adoption. And they're systematically degraded by the pace and pressure most organizations are imposing on their people.

Why High Performers Burn Out First

Sarah's story reveals a pattern that's playing out across organizations. High performers burn out first not because they're weak, but because they're carrying a disproportionate share of the adaptation burden.

They're the ones learning new tools fastest. Training colleagues. Fielding questions. Documenting best practices. Absorbing anxiety from teams who are struggling. Maintaining productivity while simultaneously transforming how work gets done. And they're doing all of this while receiving the message—implicit or explicit—that they should be excited about the opportunity, grateful for the challenge, energized by the pace.

As Laura François noted, "We've created a culture where burnout is a badge of honor and rest is seen as weakness. But the nervous system doesn't care about our cultural narratives. It has limits. And when we exceed those limits without recovery, we don't just burn people out—we degrade the very capacities we're trying to cultivate."

The data bears this out. Research shows that high performers are 24% more likely to experience burnout than average performers [9], and that mid-level managers—who bear the brunt of change implementation—have the highest burnout rates of any organizational level [10]. These aren't individual failures. They're predictable outcomes of a system that treats human capacity as infinite and adaptation as costless.

Awe as Antidote: A Different Approach

What would it look like to design AI adoption strategies around nervous system capacity rather than technical capability? To treat awe not as a luxury but as essential infrastructure?

Laura François offers a framework that's both radical and practical. "Awe isn't something you add on top of an already overwhelming workload," she explained. "It's a lens through which you redesign the work itself. You ask: What would it look like to approach AI adoption with wonder rather than urgency? To create space for discovery rather than demanding immediate mastery? To measure success by sustainable integration rather than speed of adoption?"

Here's what this looks like in practice:

Reframe AI adoption as discovery, not deployment. Instead of rolling out tools with the expectation of immediate productivity gains, create structured time for exploration and experimentation. Laura François calls this "protected wonder time"—periods where people can play with AI tools without performance pressure, where the goal is curiosity rather than output. Organizations that build this into their adoption strategies report higher sustained usage rates and lower burnout [11].

Design for collective awe experiences, not individual training. Research shows that awe is amplified when experienced collectively [12]. Instead of sending people to individual training sessions, create shared experiences that spark wonder—demonstrations of AI capabilities that genuinely surprise, conversations with AI researchers that reveal the technology's deeper implications, collaborative experiments that produce unexpected insights. These collective experiences build shared language and reduce the isolation that often accompanies change.

Make space for integration, not just implementation. The nervous system needs recovery time to consolidate learning. Organizations that build integration periods into their adoption timelines—where people can practice new skills without adding new demands—see higher retention and lower burnout [13]. This might look like implementing AI tools in phases, creating "integration weeks" where no new initiatives are launched, or reducing other workload demands during periods of intensive learning.

Cultivate awe through connection to purpose, not just productivity. The most powerful awe experiences connect people to something larger than themselves [14]. Instead of framing AI adoption purely in terms of efficiency gains, help people connect to the deeper purpose—how these tools might free them to do more meaningful work, serve clients better, or solve problems that matter. Laura François notes: "When people experience awe in relation to their work's purpose, they don't just adopt tools—they transform how they see their role and contribution."

The Awe Audit: A Practical Toolkit

How do you know if your organization is creating conditions for awe or conditions for burnout? Here's a practical framework leaders can use to assess and redesign their AI adoption strategies:

Assess current nervous system state. Before launching new AI initiatives, take stock of your team's baseline capacity. Are people already stretched? Showing signs of chronic stress? Operating in survival mode? If so, adding AI tools—no matter how valuable—will likely trigger burnout. The first intervention isn't training. It's creating conditions for nervous system recovery.

Audit for wonder opportunities. Look at your current AI adoption plan and ask: Where are the moments of genuine discovery? Where might people experience surprise, delight, or expanded possibility? If the answer is "nowhere"—if the plan is purely about efficiency, productivity, and speed—you're missing the infrastructure that makes sustainable adoption possible.

Measure what matters. Most organizations track AI usage rates and productivity metrics. Few track sustainable integration, creative application, or collaborative innovation. Even fewer track nervous system indicators like stress levels, recovery time, or capacity for wonder. What you measure shapes what you optimize for.

Create awe rituals. Build regular practices that cultivate wonder into your team rhythms. This might look like: starting meetings with a moment to share something surprising discovered through AI, creating monthly "show and tell" sessions where people demonstrate unexpected uses of tools, or scheduling quarterly "wonder walks" where teams explore AI implications without performance pressure.

Protect recovery time. Awe requires a nervous system that's not in chronic stress mode. This means actively protecting time for rest, integration, and non-productive exploration. Organizations that build recovery into their adoption strategies report 40% lower burnout rates and 35% higher sustained usage [15].

What Sarah's Manager Learned

Six months after Sarah's medical leave, her manager approached AI adoption differently. Instead of identifying high performers to lead the charge, she created cross-functional exploration teams with protected time for discovery. Instead of measuring success by usage rates in the first quarter, she tracked sustainable integration over six months. And instead of treating AI tools as productivity enhancers to be deployed quickly, she framed them as opportunities for collective wonder—invitations to reimagine what's possible.

Sarah came back from leave and joined one of the exploration teams. In our conversation, she described the difference: "The first time around, I felt like I was drowning—trying to learn fast enough, support everyone, prove the tools were worth it. This time, we had permission to be curious. To try things that might not work. To say 'I don't know' without it feeling like failure. And ironically, we're adopting the tools more deeply and sustainably than we did the first time."

That's the paradox of awe-centered adoption. By slowing down enough to create space for wonder, organizations often end up moving faster in the long run—because they're building on a foundation of sustainable capacity rather than burning through people's reserves.

The Choice Ahead

We're at an inflection point in how organizations approach AI adoption. The dominant model—move fast, train quickly, measure by usage rates, treat resistance as a problem to overcome—is producing adoption at the cost of human capacity. The burnout rates, the overwhelm statistics, the medical leaves like Sarah's—these aren't isolated incidents. They're predictable outcomes of a system that ignores nervous system limits.

There's another path. One that treats awe not as a luxury but as infrastructure. That designs for sustainable integration rather than rapid deployment. That measures success by collective capacity rather than individual productivity. That recognizes wonder, curiosity, and expansive awareness as essential capacities for navigating transformation.

"The question isn't whether we can adopt AI fast enough. It's whether we can adopt it wisely enough—in ways that build human capacity rather than burn it out. And that requires a fundamentally different approach, one that starts with awe." — Laura François

Your best employees are burning out not because they're weak, but because the system is unsustainable. Awe isn't the solution to every challenge. But it might be the infrastructure that makes every other solution possible.

This article draws on insights from Laura François's conversation on the AI-Powered Women podcast. Listen to the full episode or watch on YouTube to explore how awe reshapes leadership in the age of AI.

Want to learn how to cultivate awe as capacity in your organization? Join the AI Powered Women Academy for practical frameworks and peer learning, or get certified in multi-intelligence AI leadership.

References
[1] Deloitte State of AI Report 2025
[2] Gallup State of the Global Workplace 2025
[3] McKinsey AI Adoption Survey 2025
[4] Keltner, D., & Haidt, J. (2003). Approaching awe, a moral, spiritual, and aesthetic emotion. Cognition and Emotion, 17(2), 297-314.
[5] Stellar, J. E., et al. (2015). Positive affect and markers of inflammation. Emotion, 15(2), 129-133.
[6] Rudd, M., Vohs, K. D., & Aaker, J. (2012). Awe expands people's perception of time. Psychological Science, 23(10), 1130-1136.
[7] Piff, P. K., et al. (2015). Awe, the small self, and prosocial behavior. Journal of Personality and Social Psychology, 108(6), 883-899.
[8] Chirico, A., et al. (2018). Awe enhances creative thinking. Creativity Research Journal, 30(2), 123-131.
[9] Harvard Business Review: The High Cost of High Performance
[10] Gartner: Middle Managers Are Burning Out
[11] MIT Sloan Management Review: The Right Way to Roll Out AI
[12] Bai, Y., et al. (2017). Awe, the diminished self, and collective engagement. Journal of Personality and Social Psychology, 113(2), 185-209.
[13] BCG: How to Get the Most Out of AI at Work
[14] Greater Good Science Center: How Awe Makes Us Generous
[15] Deloitte: Sustainable AI Adoption Strategies