We’re Already Living in the Future of Talent Analytics

We’re Already Living in the Future of Talent Analytics

Recently at the Harvard Business Review, management professor Thomas H. Davenport asserted that HR “is right up there with the most analytical functions in business—and even a bit ahead of a quantitatively-oriented function like finance.” Davenport backs this claim with findings from a global survey of senior managers, directors, and VPs at large companies by Oracle, on which he collaborated. The survey found that many HR leaders are well-versed in using data and predictive analytics to make talent management decisions:

  • 51% of HR respondents said that they could perform predictive or prescriptive analytics, whereas only 37% of Finance respondents could undertake these more advanced forms of analytics.
  • 89% agreed or agreed strongly that “My HR function is highly skilled at using data to determine future workforce plans currently (e.g. talent needed),” and only 1% disagreed.
  • 94% agreed that “We are able to predict the likelihood of turnover in critical roles with a high degree of confidence currently.”
  • 94% also agreed that, “We have accurate, real-time insight into our employees’ career development goals currently.”
  • When asked “Which of the following analytics are you using?” “artificial intelligence” received the highest response, with 31%. When asked for further detail on how respondents were using AI, the most common responses were “identifying at-risk talent through attrition modeling,” “predicting high-performing recruits,” and “sourcing best-fit candidates with resume analysis.”
  • These findings suggest that the analytics transformation in HR is farther along than you might have thought, with the caveat that the survey respondents were from companies with $100 million in revenue or more, and are thus more likely to have the capacity to deploy new techniques and technologies that may be out of reach for smaller organizations. It should come as no surprise that more and more companies are adopting AI and analytics into their HR functions; what’s new in this survey data is that HR functions are becoming increasingly confident in the maturity and capability of their analytics programs.

    In terms of where companies are deploying talent analytics, Oracle’s findings track with what we have seen elsewhere: The lowest-hanging fruit is in predicting turnover, while there’s also a lot of promise in AI-powered recruiting, predicting performance, and career pathing. The focus on attrition makes sense, as employees who quit often time that decision to leave around predictable life and career events and drop lots of hints about their plans beforehand.

    If you can use data to detect these warning signs and head off unwanted departures, that can save your organization considerable amounts of money. IBM CEO Ginni Rometty made headlines earlier this month when she told attendees at CNBC’s @Work Talent + HR Summit that IBM’s AI technology was able to predict which workers were planning to quit with 95 percent accuracy:

    IBM HR has a patent for its “predictive attrition program” which was developed with Watson to predict employee flight risk and prescribe actions for managers to engage employees. Rometty would not explain “the secret sauce” that allowed the AI to work so effectively in identifying workers about to jump (officially, IBM said the predictions are now in the 95 percent accuracy “range”). Rometty would only say that its success comes through analyzing many data points.

    “It took time to convince company management it was accurate,” Rometty said, but the AI has so far saved IBM nearly $300 million in retention costs, she claimed.

    But predicting turnover with enough accuracy to add value may not require IBM-level AI capabilities. A new study from Peakon finds that employees begin showing clear signs of wanting to quit a full nine months before they pull the trigger on their resignation. A big-data study drawn from over 32 million employee survey responses in 125 countries, the Peakon report points to several key indicators of attrition that show up months in advance: declining engagement and loyalty, as well as dissatisfaction based on unchallenging work, an inability to discuss pay, an unsupportive manager, and the lack of a clear path to advancement in the organization.

    In a recent interview with David McCann at CFO, data scientist Jon Christiansen notes that it’s much easier to predict who will stay than who will leave, but highlights a few indicators that consistently point toward a greater likelihood that an employee will quit, such as whether the employee feels that their performance is evaluated fairly or that they have control over their workday. Other signs include an employee avoiding conflict, siloing themselves, focusing excessively on rewards over the common goal of the organization, and facing either too much or too little pressure at work.

    The advantage for a company like IBM, which continues to invest heavily in AI, is that it can delegate the detection of these patterns to an algorithm. Predicting quits was the first area the tech giant’s HR function focused on when deploying AI, IBM’s chief human resources officer Diane Gherson explained to Jena McGregor at the Washington Post:

    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.

    IBM is also using its Watson AI for other talent-related purposes, such as learning and development or career pathing, Carrie Altieri, IBM’s vice president of communications for people and culture, noted in a recent interview with Riia O’Donnell at HR Dive:

    AI has been a driving force of innovation for IBM’s HR team. Cognitive talent alerts mine for patterns; it searches for employees who’ve been in a job longer than usual (which could signal flight risk) and can determine whether they need more training to move up. …

    AI also can personalize learning and development for each job role and lead the way in making learning a central aspect of a company’s culture. Altieri said that more than 45,000 learners are visiting IBM’s learning platform every day and 98% of employees access it each quarter. While the company requires 40 hours of learning per year, staff average around 50 hours, regardless of tenure. Learning is a huge part of the culture at IBM, she explained, and the new system gives managers the tools to have more intentional discussions with staff.

    And like other tech companies experimenting with these technologies, IBM is not only deploying its AI capabilities internally, but also selling them as a service to other organizations. Last November, the company announced the launch of IBM Talent & Transformation, a new business venture offering AI skills training in addition to services that “harness the power of AI personalization to guide employees in developing skills and pursuing opportunities to grow within the company.”