Artificial intelligence

News HR teams can (and should) use AI to predict employee burnout

By Eric Quintane, Associate Professor of Organizational Behavior, ESMT Berlin

In the fall of 2019, Wenche Fredriksen shared a personal story about burnout and recovery on LinkedIn. When she burned out, she was 37, a mother of two young children and a manager at a consulting firm. From the outside, she wrote, it looked ideal. Burnout leads to a different reality: “I have no energy. I can’t process sound, light, or movement. I can’t focus or process information. I can’t eat, sleep, or cry. My hard drive Full, dead battery. I feel like a total failure. I just want to disappear, sleep, and no one expects anything from me, ever. I’m sure I’ll never work again.”

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Across populations, geographies and industries, whether on the factory floor or at the C-suite, employee burnout is real and devastating. These symptoms are common — emotional and physical exhaustion prevents them from focusing on themselves, work or loved ones.

These are not rare and isolated cases. In Europe, approximately one in ten employees is affected by burnout. According to a report, worker burnout in Germany is said to cost its economy around €9 billion in lost productivity every year. The numbers in the US are even worse—it is estimated that US employers lose out on more than $300 billion a year. The World Health Organization’s 2019 decision to include burnout as an “occupational phenomenon” in the 11th revision of the International Classification of Diseases received global recognition. That was before the coronavirus pandemic crisis, during which burnout — especially among healthcare professionals — became a household term.

While the reported cases and their implications are sobering—especially in recent years—the astonishing developments in artificial intelligence offer new hope for identifying and combating the stressors that lead to burnout. So, what are these opportunities? How can business leaders use these tools — sooner rather than later — to improve their workplaces?

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Assessing the risk of burnout and intervening on behalf of employees remains the responsibility of trained HR professionals. We need their skills and good judgment in often complex and highly sensitive situations.

However, artificial intelligence can be used to identify who is more likely to experience severe burnout. This information can help busy HR teams prioritize specific teams, provide resources, and monitor development for additional intervention.

But how exactly can AI help us in this way?

Our organization already generates vast amounts of electronically tracked data about how we work. For example, email is widely used (and overused). It is an easily accessible source of data on collaborative behavior and, as many will admit, a source of stress and stress in its own right. A 2015 report by technology research firm Radicati said the average employee receives 121 emails per day. In Adobe’s 2018 Consumer Email Survey, US white collar workers reported spending an average of 3.1 hours per workday checking work email.

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In a study of a mid-sized organization (read the study), my researcher colleagues and I applied a predictive model to the company’s email traffic to predict which employees were at risk of burnout. We confirmed our predictions with over 80% accuracy using survey data.

Such research is just the beginning—it’s a promising development that deserves to be replicated in different large companies. We had to demonstrate whether the results were replicable, which email metrics were important in the organization, and how far in advance the predictive model’s flags should be sent. But, of course, monitoring employee email communications raises issues that must be addressed with proper handling. That said, we must ensure that systems designed for employees address workplace surveillance by prioritizing employee data privacy, even when identifiable information such as sender, recipient, and time are required to predict burnout risk and mobilize HR resources.

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As remote and virtual collaboration becomes the norm, the separation between work and life is becoming increasingly blurred. Stories of Wenche Fredriksen’s personal burnout are not uncommon, and there are likely to be more and more of them. Organizations can and should do more to prevent such experiences from happening. AI can dramatically improve HR teams’ chances of proactively dealing with extreme employee stress and burnout. The reduction in personnel costs and costs to the company’s bottom line is worth it.

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