How Intense Is CEO Pressure Right Now? And What Risks Are Quietly Building?
CEO pressure is moving in the right direction. Our CEO Stress Barometer now reads 64.2 out of 100, a decline of 2.5 points from our inaugural study. Scores for top stressors and stakeholders fell across the board as well. But while corporate chiefs may be sleeping better, our second CEO Insomnia Index identifies a potential blind spot: an employee trust gap that could undermine CEOs’ AI ambitions.
Nearly 90% of CEOs are confident their employees trust their leadership, but only 46% of employees agree, the latest CEO Insomnia Index finds. The employee trust gap becomes especially clear around AI, where the rank and file are looking for greater transparency from leaders about what the technology means for their roles and careers.
Closing the AI trust gap will require greater visibility, communication, and engagement from CEOs. But that imperative comes as their attention is increasingly consumed by the here and now. Our latest study finds that 60% of CEOs say near-term pressures take up a disproportionate share of their time—a 3-percentage-point increase over our first index.
CEO Pressure Is Easing Across Stressors and Stakeholders
Insights from the Second Edition of the BCG CEO Insomnia Index
- CEOs believe employees trust them to have their best interests at heart, but employees tell a different story. Nearly 70% of CEOs are confident that employees trust them to prioritize their well-being when funding critical business priorities. Only 36% of employees agree.
- When it comes to AI, the CEO-employee trust gap widens even further. Just 15% of CEOs worry their approach to AI transformation could erode employee trust, while 40% of employees say their CEO has not been transparent enough about AI’s impact on their roles and career opportunities.
- When CEOs build trust around AI, employees are more willing to go the extra mile. Half of employees who say their CEO’s approach to AI increased their trust are more willing to go above and beyond at work. Among employees who say their CEO’s leadership and communication on AI eroded their trust, fewer than 10% say the same.
- Closing the AI trust gap requires more from CEOs just as near-term pressures demand more of them. Sixty percent of CEOs say near-term pressures consume a disproportionate share of their time, up 3 percentage points from our inaugural survey. Among CEOs of the largest companies, that figure rises to 68%.
- CEOs aren’t counting on their senior teams to relieve the pressure. Only 63% of CEOs are confident their senior leadership teams can execute their vision over the next two to four quarters. Nearly 60% plan to replace a member of their senior team in the next six months, with CFOs topping the turnover list.
- For one in three CEOs, the pressure is enough to make them consider stepping down. Thirty-four percent say they have considered stepping down at some point in the past six months. Nearly half also say they often or very often put off activities that support their physical and mental well-being.
What the Index Reveals About Employee Trust and the AI Trust Gap
What is the AI trust gap between CEOs and employees?
The AI trust gap is the disconnect between how CEOs believe employees view their leadership on AI and how employees actually experience it. Just 15% of CEOs worry their approach to AI transformation could erode employee trust. Yet 40% of employees say their CEO has not been transparent enough about AI’s impact on their roles and career opportunities. That disconnect matters because employees want to understand how AI will change their work and whether their leaders have a plan. And trust tracks closely with effort: employees who say their CEO’s approach to AI increased their trust are far more willing to go above and beyond at work.
Why don’t employees trust their CEO on AI?
Employees want a clearer picture of what AI means for their jobs and careers. But 40% say their CEO has not been transparent enough about its impact on their roles and opportunities. When invited to share their feelings in our survey, employee comments suggest many experience AI as something being done to them rather than with them. One described a CEO “gushing about AI and how it could increase profits at the expense of our jobs.” Another said their CEO “is playing games with AI,” creating anxiety. Closing that employee trust gap will require more effective communication from CEOs and deeper engagement with employees throughout the AI transformation.
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The BCG CEO Insomnia Index Methodology
The Index draws from three data sources:
- A proprietary survey of 645 CEOs from leading companies ($100 million or more in revenue). The survey was designed to assess stress and sleep levels while offering deep dives within the six core stress drivers, focusing specifically on the hallmarks of workplace stress: tension, pressure, and scrutiny. The survey period was June 18 through July 5, 2026.
- A complementary survey of nonexecutive employees (n = 500) to provide the employee viewpoint. The survey period was from June 23 to June 25, 2026.
- Five years of CEO turnover data from the S&P 1200.
To fully capture the pressure CEOs face, each driver was examined in two ways:
- Personal stress levels, as self-reported in the CEO Insomnia Index Survey.
- Organizational stress signals, modeled in the CEO Job Security Model.
CEO stress is defined as a state of mental worry or tension caused by the demands of the CEO role.
Because individual survey responses are anonymized and cannot be linked to company outcomes, CEO turnover was used as a proxy for company-level stress. The model uses a logistic regression approach, with actual CEO exits from the S&P 1200 over the past five years as the dependent variable, predicting the likelihood of CEO turnover in the next quarter. Independent variables include net entry rate, sentiment analysis from our earnings call data, negative media mentions, total shareholder return, and more.
The CEO-Board Stress analysis combined independent-samples tests for the full sample with within-person paired tests and change-score regression for the panel data. This approach allows both population-level and individual-level conclusions to be drawn.