IBM wants to keep its employees from quitting. And it's using artificial intelligence to do it.
In a recent CNBC interview, chief executive Ginni Rometty said that thanks to artificial intelligence, the tech and consulting giant can now predict with 95 percent accuracy which employees are likely to leave in the next six months.
The "proactive retention" tool -- which IBM uses internally but is also selling to clients -- analyzes thousands of pieces of data and then nudges managers toward which employees may be on their way out, telling them to "do something now so it never enters their mind," Rometty said.
IBM's efforts to use the technology to learn which employees might quit is one of the more high-profile recent examples of the way data science, "deep learning" and "predictive analytics" are increasingly infiltrating the traditionally low-tech human resources department, arming personnel chiefs with more rigorous tools and hard data around the tricky art of managing people.
From recruiting to hiring to performance evaluations, human-resources executives have been investing in tech-driven data analysis to make better people decisions.
"We're kind of coming of age in our ability to really put a number on human capital, to really understand what it takes to recruit a certain skill set and what it costs the company to lose a rare talent," said Anna Tavis, a professor at New York University who studies human capital and technology.
Almost every Fortune 100 company, said Brian Kropp, group vice president for Gartner's employment practice, now has a head of "talent analytics" and a team of data scientists in human resources.
"Compare that to three years ago, when there were maybe 10 to 15 percent that had a named-and-known head of talent analytics," said Kropp, whose firm counts IBM as a client. "It's the fastest growing job in [human resources]."
Analysts say retention, in particular, is a critical area for the application of artificial intelligence. For one, there's a clear event that happens -- someone quits and leaves the company, or threatens to -- that helps data scientists seek patterns for intervening.
Meanwhile, especially in a labor market with an unemployment rate below 4 percent and a near-record rate of people quitting their jobs for new gigs, there's increasing worry about the high cost of not keeping great employees. The cost of trying to hire someone new, Kropp said, is about half that person's salary.
IBM's use of artificial intelligence in human resources, which began in 2014, comes at a time when the 107-year-old company has been trying to shift its massive 350,000-person workforce to the most current tech skills and includes 18 different artificial-intelligence deployments across the department. Diane Gherson, IBM's chief human-resources officer, said in an interview that using tech to predict who might leave -- considering thousands of factors such as job tenure, internal and external pay comparisons and recent promotions -- was the first area they focused on.
"It was an obvious issue," she said. "We were going out and replacing people at a huge premium."
IBM had already been using algorithms and testing hypotheses about who would leave and why. Simple factors like the length of an employee's commute were helpful but only so telling.
"You can't possibly come up with every case," Gherson said. "The value you get from AI is it doesn't rely on hypotheses being developed in advance. It actually finds the patterns."
For instance, the system spotted one software engineer who hadn't been promoted at the same rate as three female peers who all came from the same top university computer-science program. The women had all been at IBM for four years but worked in different parts of the sprawling company. While her manager didn't know she was comparing herself to these women, the engineer was all too aware her former classmates had been promoted and she hadn't, Gherson said. After the risk was flagged, she was given more mentoring and stretch assignments and remains at IBM.
While the program urges managers to intervene for employees who have hard-to-find skills -- offering them raises, public recognition or promotions -- potential quitters that the system identifies with less valuable skills or who are low performers don't necessarily get the same response.
How perfect such systems really are at predicting who might leave -- and whether the interventions suggested will always work to keep them -- is still somewhat unknown, Kropp said. And some patterns the technology might turn up might be tricky for managers to act on.
But they may still offer an edge over the surprise office visit from an employee no one guessed was about to leave.
"There's still always going to be a lot of art and a lot of uncertainty," he said. "But it's still better than a manager guessing."
SundayMonday Business on 04/14/2019