Our approach in action.
Dream patterns predict employee engagement declines ahead of benchmark surveys
Key take-aways
Employees who later reported low engagement in surveys first showed signs in their dreams—passive observation and detachment appeared more frequently.
This dream-based metric identified shifts in employee engagement at the U.S. population level before the leading survey benchmark detected them, proving its value as an early signal.
These findings show that analyzing employee dreams provides a proactive, scalable way to track engagement trends, offering insights before traditional metrics detect problems.
The challenge
Employee engagement is more than just productivity—it reflects an emotional connection to work. Engaged employees are enthusiastic, absorbed, and committed to their roles, leading to better performance, higher customer satisfaction, and stronger financial outcomes. When engagement drops, business performance suffers.
Yet, measuring engagement at scale is difficult. Traditional employee engagement surveys are costly, subject to response bias, and often suffer from low participation rates. As a result, many organizations have turned to behavioral tracking, which has doubled since 2020—today, 60% of US & European organizations use it.
However, surveillance-based solutions can erode trust:
Beyond these challenges, current engagement metrics exclude vast portions of the global workforce. Surveys can only be conducted with direct employees, often omitting contractors, gig workers, and entire industries. Historically, engagement research has focused on white-collar office workers, leaving huge swathes of the workforce invisible.
Dream analysis provides a new way to measure workforce engagement—one that is non-intrusive, scalable, and capable of capturing engagement trends across both employed and unemployed populations.
The insights
To understand how engagement manifests in dreams, we analyzed 3,200 dream reports from employed individuals and compared them to 3,200 dreams from unemployed individuals.
Unemployed individuals were chosen as a baseline for extreme disengagement—while disengaged employees psychologically detach from their work, unemployed individuals have already completed that detachment.
Using machine learning, we built a classifier that could predict employment status based on a single dream report with 62% accuracy.
Key findings included:
Unemployed individuals were more likely to dream about work, reflecting preoccupation with their job loss.
Their dreams often lacked active participation—they were more likely to be passive observers, detached from the dreamworld itself.
This mirrored real-world disengagement—as workers detach from the workplace, they also disengage from their own dream narratives.
As expected, the experience of unemployment functioned as an extreme version of workplace disengagement.
Dreams of engaged workers are Characterized by active interaction with their environment."I walked upstairs to the attic. The attic I entered looked old and creepy. In my search for the comic books I walked past a large mirror"
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"I walked upstairs to the attic. The attic I entered looked old and creepy. In my search for the comic books I walked past a large mirror" •
"I stopped running and noticed that the path to the hill was different to earlier. I was shocked and in disbelief of what I saw"
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"I stopped running and noticed that the path to the hill was different to earlier. I was shocked and in disbelief of what I saw" •
"I ran to go tell somebody and I had no energy at all. My chest hurt and I kept passing out. I was really alarmed and I knew that I needed to go to the hospital, but I didn't have the energy to yell either; I had to find where everyone was."
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"I ran to go tell somebody and I had no energy at all. My chest hurt and I kept passing out. I was really alarmed and I knew that I needed to go to the hospital, but I didn't have the energy to yell either; I had to find where everyone was." •
With a validated classifier for disengaged dreamers, we scored a large dream dataset for levels of psychological disengagement.
To test reliability, we compared dream-measured engagement trends to Gallup’s global employee engagement benchmark. This benchmark is created by collating survey-based measures of employee engagement across thousands of organizations globally.
The “dream disengagement index” we sourced from our large dream dataset follows the same pattern as Gallup’s global employee engagement benchmark. The close correlation (R= -.7, p<.01) confirmed that dream analysis provides an accurate, scalable indicator of workforce engagement, aligning with traditional survey-based methods without requiring direct self-reporting.
Crucially, the dream index shifted one month before the Gallup benchmark, suggesting that dream analysis can detect engagement declines before conventional survey data captures them.
Dreams of disengaged workers are Characterized by a passive attitude to the narrative."Last night I was watching a man hold onto a bridge as the water rose, but I couldn’t do anything to help"
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"Last night I was watching a man hold onto a bridge as the water rose, but I couldn’t do anything to help" •
"I either can't move in my dream, or I feel too terrified and trapped to move. I can hear something coming."
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"I either can't move in my dream, or I feel too terrified and trapped to move. I can hear something coming." •
"The aliens are are slowly marching/hovering forward. All I can do is stand there helpless."
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"The aliens are are slowly marching/hovering forward. All I can do is stand there helpless." •
So what?
Workforce engagement has long been measured through surveys and behavioral tracking, but these methods have serious limitations—they are expensive, biased, intrusive, and exclude large segments of the global workforce. This study demonstrates that dream data offers a scalable, real-time alternative for tracking engagement trends.
For HR and talent intelligence teams: Dream data enables early detection of disengagement trends, allowing companies to respond proactively before engagement declines impact performance.
For organizations concerned about survey fatigue: This approach provides insights without requiring employees to actively participate, reducing bias and participation barriers.
For broader labor market intelligence: Dream analysis provides a global, non-intrusive measure of workforce engagement, including populations that are traditionally excluded from engagement studies.
By integrating dream data into HR analytics and strategic intelligence, organizations gain access to a new dimension of workforce sentiment—one that captures engagement in a way that traditional methods cannot.
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Ready to get ahead of disengagement? Contact the Center for Organizational Dreaming today to explore how dream analysis can enhance your HR strategy and provide early, data-driven insights into employee commitment, organizational alignment, and engagement risks.