Dark Side of Workplace Surveillance: Why Datafication Needs Digital Empathy

Written by: Prisha Khanna, Moitrayee Das

Workplaces today are driven by data-driven metrics that extend far beyond futuristic hypotheticals. AI tools are now capable of tracking employees’ voice tone, typing speed, productivity rates, and even emotional expressions in meetings. While such technologies promise efficiency and objectivity, they are also contributing to the quiet evolution of the psychology of work. In the race to quantify performance, we might unknowingly be losing sight of the faces behind these numbers.

A study by Forbes reveals that at least 40% of employees know they’re under surveillance, with nearly half reporting increased stress and anxiety as a result. In 2025, at least 43% of employees reported that constant monitoring negatively affected overall company morale, while 39% stated that it damaged employer–employee relationships.

Organizations are increasingly drawn to the precision of data (Bersin, 2013). Quantitative insights help employers identify patterns in employee behavior and detect workflow bottlenecks. AI systems prove invaluable in simplifying vast amounts of workplace data—from productivity metrics to social interactions. This digitized perspective allows organizations to make evidence-based decisions, streamline recruitment processes, and even anticipate key metrics.

However, a paradox emerges. While this data makes employees more visible to organizations, it often strips them of their sense of individuality. As every click, conversation, and behavior becomes another data point, the human nuances of emotion, creativity, and circumstance get lost in the clutter of numbers.

Why Organizations Love Numbers

Organizations are rapidly transforming by adopting newer technologies and tapping into new sources of data (Polzer, 2023). As employees carry out their daily tasks, they generate vast arrays of data points. The precision this data offers is particularly appealing. Quantitative insights have proven crucial in identifying behavioral patterns, including performance and burnout rates. AI tools can quickly monitor and analyze the continuous influx of data, generating insights not only into work-related parameters but also into personal preferences, social relationships, and other non-work-related exchanges (Teebken et al., 2025). These numbers help organizations optimize efficiency, track burnout, and enhance recruitment.

In attempting to understand employees better, organizations may actually be asserting greater control. In such data-driven environments, the line between professionalism and invasion of privacy often blurs. Every email and personal break feeds into opaque algorithms, which may cause more harm than good. This constant quantification of employee behavior not only breaches privacy but also erodes trust, personal autonomy, and psychological safety—ultimately hampering productivity and motivation.

The Hidden Costs of Constant Datafication

Recent research shows that excessive monitoring can harm employee well-being. Newman et al. (2017), in their study on psychological safety, found that extensive surveillance can lead to anxiety, technostress, burnout, and an environment of distrust among employees.

Constant datafication also has complex effects on motivation. Studies indicate reduced intrinsic motivation and innovation when employees feel micromanaged or consistently observed (Glavin et al., 2024).

Cognizant’s recent feature rollout reportedly tracks mouse and keyboard activity, as well as the applications and websites employees use. The system marks users as “idle” after five minutes of inactivity and “away from system” after fifteen (Agarwal, 2025). This can be understood through the lens of self-determination theory, which highlights the importance of autonomy, competence, and relatedness in building self-esteem (Ryan & Deci, 2000). Employees adapt to constant monitoring by engaging in performative behaviors aimed at appearing productive rather than being productive. This undermines authentic motivation and job satisfaction.

In such a system—where every digital step is recorded—freedom is overshadowed by surveillance, proficiency is reduced to pleasing metrics, and the trust that binds organizations slowly fades. Workplaces risk being perceived as digital watchtowers. The very traits they claim to value—creativity, collaboration, purpose—become increasingly difficult to express under the silent gaze of algorithmic metrics. While datafication is proposed as a pathway to fairness and transparency, its unchecked use can feel impersonal and invasive. It raises legitimate questions about where to draw the line between necessary datafication and reducing people to numbers—questions organizations can no longer ignore.

The Need for Digital Empathy

The solution to dehumanized data practices lies in adopting digital empathy—the ability to interpret and act on employee data with compassion and awareness. In the digital age, empathy shouldn’t be limited to restricting technology but should focus on using it to strengthen human connections.

Organizations must re-evaluate how they engage with their people. Regular check-ins can reduce reliance on rigid dashboards as tools for monitoring well-being. Data should support—not dictate—the understanding of human behavior (Zieglmeier & Pretschner, 2023). Anonymous feedback channels and advisory systems can encourage honest communication and allow employees to participate in policy decisions. Shifting to outcome-based assessments rather than activity tracking can foster deeper trust in leadership. Reintroducing flexible work schedules and “meeting-free” hours can enhance engagement and productivity.

Numbers reveal patterns in how people work—but not the passion behind their work. In our rush to simplify human behavior into data points, we forget that not everything meaningful can be measured. The future of work does not depend on how much data organizations can collect, but on how thoughtfully they interpret it. In an increasingly algorithmic professional world, the most powerful move organizations can make is, in fact, simple: to see their people not as data points, but as humans.

References

Agarwal, A. (2025, November 17). Cognizant moves to monitor employees, laptops will be watched and termed idle after 5 mins of break. India Today. https://www.indiatoday.in/technology/news/story/cognizant-moves-to-monitor-employees-laptops-will-be-watched-and-termed-idle-after-5-mins-of-break-2821229-2025-11-17

Bersin, J. (2013). The Datafication of Human Resources. Forbes. https://www.forbes.com/sites/joshbersin/2013/07/19/the-datafication-of-human-resources/

Glavin, P., Bierman, A., & Schieman, S. (2024). Private Eyes, They See Your Every Move: Workplace Surveillance and Worker Well-Being. Social Currents , 11(4), 327–345. https://doi.org/10.1177/23294965241228874

Newman, A., Donohue, R., & Eva, N. (2017). Psychological safety: A systematic review of the literature. Human Resource Management Review, 27(3), 521–535. https://doi.org/10.1016/j.hrmr.2017.01.001

Polzer, J. T. (2023). The rise of people analytics and the future of organizational research. Research in Organizational Behavior, 42(1), 100181. https://doi.org/10.1016/j.riob.2023.100181

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://selfdeterminationtheory.org/SDT/documents/2000_RyanDeci_SDT.pdf

Zieglmeier, V., & Pretschner, A. (2023, July 26). Rethinking People Analytics With Inverse Transparency by Design. ArXiv.org. https://doi.org/10.1145/3610083

Prisha Khanna is an undergraduate Psychology student at FLAME University, Pune and Moitrayee Das is an Assistant Professor of Psychology at FLAME University, Pune.