AI Might Be Telling Your Boss You’re Probably Quitting

Life continues to imitate art. Remember that movie where people are stopped before they actually do something wrong? This was possible because the film’s fictional society had the means to predict future events.

Right now, there are algorithms in use that can foretell who are most likely to quit at work. By the way, predictive analytical software isn’t new. Businesses have been using these to prepare for the future.

Why use predictive tools to determine which employees will probably leave? What happened to good old performance evaluations and employee feedback? According to this Harvard Business Review article, these are no longer reliable in gauging satisfaction in the workplace. By the time employees quit, it’s too late to stop them.

Keep costs down and remaining competitive

These are among the reasons why organizations employ AI to predict resignations. Generally, it’s more cost-effective to retain employees than to rehire and train new ones. Replacements may be available on short notice but, there’s no guarantee they would perform as well as their predecessors.

The loss of employees is disruptive, especially if they hold key positions. Future plans and ongoing projects could be derailed. Organizations may lose their advantage if their star performers quit.

A sudden change in leadership, culture, or direction can likewise spur employees to seek other jobs. Companies would like to know beforehand who these would likely be.

Arguments for Predictive AI

Many companies in Asia are experiencing high turnover rates. DBS, based in Singapore, is one of them. The bank is using a predictive algorithm that hopefully reduces its turnover rate to 1%. It’s based on hundreds of data like salary, civil status, and even absenteeism. There is no standard algorithm among organizations. But, many claim their software programs are at least 90% accurate.

Knowing who will likely quit will help companies become proactive in their retention efforts. HR personnel can now make the first move by communicating with disaffected employees. AI tools cut costs, according to IBM.  Also, these helped its HRD become more efficient and relevant.

Proponents of predictive algorithms say this is good for employees. HR personnel can now directly address individual concerns.  Soon, AI tools can design more precise career paths by analyzing employee strengths.

Of course, there are reasons for quitting a company has no control over. A desire for a career change or location, self-employment and more family time are some examples. Nevertheless, predictive software will be used continually in the future.

Arguments against AI

Predictive tools only use public data which includes social media. Already, many feel their privacy is violated if algorithms “read” their FB or Twitter posts. There’s also the chance the data could be misread by a machine that cannot analyze human quirks.  Imagine being approached by your HR or boss when you haven’t even thought of leaving.

Results from these software programs may be mishandled and cause more harm than good. Management might be unable to hide its feelings towards employees who might leave. They might give more attention to those they want to stay. On the other hand, an employee who is no longer a good fit might even be nudged to resign.

An employee who is likely to quit doesn’t mean there is actual intent. This places management in a dilemma. How does it engage employees “marked” by a machine?

Employees might begin to feel uncomfortable because software determined they’re likely leaving. They begin to second-guess and watch their every move at work. In short, they stop being themselves. This is one consequence companies should be concerned about.

So, what do you think of AI predicting who will likely quit? There’s nothing wrong with using tools to address concerns in the workplace. However, these should only complement efforts to discover what can drive employees to leave their jobs. With improved employee engagement, maybe these predictive algorithms may not be necessary at all!

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