Employee burnout is a serious concern for organizations—from startups to global enterprises. Burnout reduces job performance and increases turnover. It doesn’t just harm individual employees, but the larger organization as well, creating an environment of dissatisfaction that can trickle down.
How do we know that employee burnout is growing in severity? Earlier this year, the World Health Organization added burnout to its list of official medical diagnoses. Over two thirds of all employees feel moderately-to-severely burned out according to a 2018 Gallup study that interviewed nearly 7,500 full-time employees.
So, what’s causing employee burnout and what can be done about it?
Certain tasks can be causes of employee burnout. If tasks are monotonous, they can create what’s known as “decision fatigue.” When presented with a repetitive task for hours on end, your mind can become depleted of energy, so much so that it has nothing left after work. Employees suffering from decision fatigue lose interest in activities that used to excite them, seeking extended hours of sleep as a coping mechanism.
The Debate on Automating Tedium
There are many ways to combat decision fatigue and the resulting employee burnout. Some are as simple as getting up from your desk and engaging in physical activity. But if businesses can rethink the type of tasks they can automate in a way that augments human work and provides more complex, creative, and engaging tasks for employees, it could greatly increase job satisfaction.
This is where AI and ML come in. AI drives value by automating processes and doing the grunt work, targeting employees on strategic and value creation activities, which eliminates job fatigue and boredom. This is even true for the analytics and insight generation process. AI doesn’t just make the job easier and more fulfilling, it gets it done better. For instance, HUMU is a company that uses machine learning to provide employees with small, data-backed nudges to help them master new skills. These can be simple things such as reminding managers to thank team members who ask important questions. By automating good practices through constantly recommended relevant actions, employees become better trained and more engaged. While this platform was being developed at Google, it boosted new hires’ productivity by 2%, or about $400 million a year.
Automation can’t solve everything though. For some organizations such as KickStarter, it’s been a mixed bag. Kickstarter used AI to greatly reduce the queue of ideas needed to be manually assessed by humans. Their machine learning system reduced their workload by 40-60%. Once the easily-assessed ideas had been automated, employees were left with only the most complex, challenging ideas. Though their workload had become manageable, they found that they missed getting to approve some of the more fun “slam dunk” ideas that gave them a sense of accomplishment.
AI Can Help with That
As the CEO of a data analytics company (and former Chief People Officer), I think a lot about the need to balance data with human emotion. AI can actually help infuse more human insight into HR, which by definition should be people focused. Some of the most monotonous tasks are centered in the HR department. By automating tasks and giving HR decision makers access to the data-insights that AI can offer, they’re free to make more targeted interventions into cases where employees might be suffering from burnout, or more importantly, enact processes that help in dealing with employee burnout.
Deloitte, for example, launched an AI-supported system called ConnectMe to analyze in-house data and offer employee support using chatbots for smaller issues. This intervention is supplemented by HR representatives, who now have more time to intercede in high-need cases. By implementing AI solutions, Deloitte saw that the human touch was actually amplified because it was properly deployed.
AI certainly can reduce tedious workloads and help companies deal with employee engagement and burnout but it is up to organizations how this is done. AI doesn’t have to come for all the easy tasks. Indeed, clinical psychologist Alice Boyes indicates that employees need “to vary the difficulty of mastery experiences,” so that not everything they do is challenging. The positive reinforcement that comes from completing simple tasks is necessary for a productive employee, and organizations should understand that before they overly-automate. However, with reduced workload, lowered decision fatigue, and AI-assisted HR departments that can provide targeted interventions, executives in organizations that embrace AI opportunities are less likely to face employee burnout.
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