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 AI, the tech and consulting giant can predict with 95 percent accuracy the employees who 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 the employees who 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 AI to learn who 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.
.. Almost every Fortune 100 company, said Brian Kropp, group vice president for advisory firm Gartner’s HR practice, 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 HR.”
.. 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.
“The person was here, and then the person was not here,” Kropp said. “It is where the more sophisticated analytics work in HR is going.”
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 worries about the high cost of not keeping great employees. The cost of trying to hire a replacement, Kropp said, is about half that person’s salary.
IBM’s use of AI in HR, which began in 2014, comes at a time when the 108-year-old company has been trying to shift its massive 350,000-person workforce to the most current tech skills and includes 18 different AI 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 the department 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, such as 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 she 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 as having less-valuable skills or who are low performers don’t necessarily get the same response.
“Our universe for doing this is not the whole IBM universe, and does not include low performers,” Gherson said. “The ones who are in high demand today and high demand tomorrow are going to be the ones we treat with a very high-touch” response.
IBM does not analyze or monitor employees’ email, external social media accounts or other internal message boards as part of its predictions on who has one foot out the door. But some start-ups have scraped publicly available LinkedIn data, for instance, to predict probable departures.
Meanwhile, other vendors have recently begun analyzing data to predict how lower employee engagement scores can give companies a nine-month heads-up about the groups of workers that might be at risk of leaving. Josh Bersin, an industry analyst who focuses on HR technology, said some companies have taken a high-level look at email to make predictions.
He recently wrote about how some firms have studied email “metadata” and communication patterns, finding that people who quit were less engaged in their email for up to six months before leaving.
“Predictive attrition” methods are becoming popular, he said, because “it’s so hard to hire people. Companies just want to know why people are leaving, and they want data about why people are leaving.”
How effective 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 AI might turn up — for instance, women of childbearing age who leave tend to have higher turnover rates — might be tricky for managers to address.
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,” Kropp said. “But it’s still better than a manager guessing.”
In each instance, it has been less than a year since the allegations against these men surfaced, and in each instance, the men have done little in the way of public contrition. When they have apologized, they have done so with carefully worded, legally vetted statements. They have deflected responsibility. They have demonstrated that they don’t really think they’ve done anything wrong. And worse, people have asked for the #MeToo movement to provide a path to redemption for these men, as if it is the primary responsibility of the victimized to help their victimizers find redemption.
“Should a man pay for his misdeeds for the rest of his life?” This is always the question raised when we talk about justice in the case of harassment and rape allegations against public figures. How long should a man who has faced no legal and few financial consequences for such actions pay the price?
I appreciate the idea of restorative justice — that it might be possible to achieve justice through discussing the assault I experienced with the perpetrators and that I might be involved in determining an appropriate punishment for their crime. Restorative justice might afford me the agency they took from me. But I also appreciate the idea of those men spending some time in a prison cell, as problematic as the carceral system is, to think long and hard about the ways in which they violated me. I would like them to face material consequences for their actions because I have been doing so for 30 years. There is a part of me that wants them to endure what I endured. There is a part of me that is not interested in restoration. That part of me is interested in vengeance.
We spend so little energy thinking about justice for victims and so much energy thinking about the men who perpetrate sexual harassment and violence. We worry about what will become of them in the wake of their mistakes. We don’t worry as much about those who have suffered at their hands. It is easier, for far too many people, to empathize with predators than it is to empathize with prey.
.. he has remained in control of the narrative. He gets to break the rules, and then he gets to establish rules of his own when he must answer for his misdeeds.
.. He should pay until he demonstrates some measure of understanding of what he has done wrong and the extent of the harm he has caused. He should attempt to financially compensate his victims for all the work they did not get to do because of his efforts to silence them.
- .. He should facilitate their getting the professional opportunities they should have been able to take advantage of all these years.
- He should finance their mental health care as long as they may need it.
- He should donate to nonprofit organizations that work with sexual harassment and assault victims.
- He should publicly admit what he did and why it was wrong without excuses and legalese and deflection.
.. Whatever private acts of contrition these men, and a few women, might make to their victims demands a corresponding public act of contrition, one offered genuinely, rather than to save face or appease the crowd. Until then, they don’t deserve restorative justice or redemption. That is the price they must pay for the wrong they have done.
That sounds pretty good, I said, except that employment in the Mexican auto sector rose to 589,000 from 368,000 during the same period, an increase of 60 percent. I’m happy that 221,000 more Mexicans got jobs, but let’s be honest: Absent open borders, many of those jobs would have been in America.
.. I have never forgotten a powerful article I read in Foreign Affairs in 2007 that called for huge tax redistribution, both as a moral matter and as a mechanism for ensuring political support for free trade. (The authors were hardly left-wing shills — one had served in the administration of President George W. Bush.)
That still sounds like the right idea to me.
It’s not only morally wrong to fail to help those on the losing end of globalization, but it will also end badly politically, as the ascendant candidacy of Donald J. Trump illustrates.