Artificial Intelligence Can Boost HR Analytics, But Buyer Beware

 

By Dave Zielinski August 16, 2017
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​Artificial intelligence (AI) and machine learning have had no bigger impact on human resources than in the area of analytics. Tools designed to help HR leaders understand and predict the impact of talent decisions were among the first to hit the market and now help forecast employee flight risk, identify high-potential employees, unearth engagement issues, recommend learning courses and more.

But with the growing popularity of AI-driven analytics also comes danger. 

Experts say they see an increase in the incidence of "AI washing," a practice where technology vendors exaggerate the role of AI in their products to woo customers. A recent report from research firm Gartner Inc., which tracks commercial trends through a tool called the Hype Cycle, suggests vendors across industries often apply the AI label too indiscriminately to their offerings.

It is more important than ever that HR buyers have a good grasp of what constitutes true AI and machine learning before investing in the tools, experts say.

AI Case Studies

One vendor with seasoned machine learning and AI tools is Ultimate Software. Ultimate's technologies allow customers to use "signals" hidden in their human capital management (HCM) data to help predict outcomes like employee turnover and to determine themes influencing employee engagement.

The vendor's AI platform can also factor qualitative signals into retention equations, such as how employees feel toward their workplace or how much meaning they find in a job role, said Armen Berjikly, Ultimate's senior director of strategy and workforce intelligence in San Francisco.

Workday, another vendor, also has a retention risk analysis tool built into its core HCM platform that uses AI and machine learning to identify top performers at risk of leaving, a feature that's been available since 2014. With the help of an interactive dashboard, customers can identify and understand retention risk unique to the entire organization, a specific department or individual job roles, said Cristina Goldt, Workday's vice president of HCM products.

Similarly, vendor Cornerstone OnDemand has an AI tool called Insights that applies predictive analytics to workforce data. The technology can perform tasks like uncovering noncompliance predictive factors to reduce an organization's risk of regulatory fines, suggest learning courses that are the best fit for certain employees and help identify high-potential employees to fill pipelines for succession plans.

Mark Goldin, chief technology officer for Cornerstone OnDemand, said the AI is designed to augment human decision-making, not replace it.

"The machine learning is there to aid and improve existing processes and work in tandem with humans," Goldin said. "With succession planning, for example, the AI helps to rate people across different dimensions to determine who might be ready for promotion in six months, a year or more. It suggests candidates to fill succession pipelines based on success predictors from data the system has ingested and analyzed."

Buyer Beware: 'AI Washing' on the Rise

 "Vendors will say they have AI and machine learning, and many don't," said Helen Poitevin, a Paris-based human capital management research director at Gartner. Poitevin recommends asking vendors for references of clients who have experienced quantifiable success from use of their AI.

"Ask those references if the recommendations generated by the tool were useful," she said. "How accurate were the predictions? Did those predictions improve over time?"

Brian Sommer, founder of TechVentive, a technology consulting firm in Batavia, Ill., said HR buyers should separate those vendors who are simply "demo-ing," or marketing, AI from those whose products have proven effective in the mainstream market.

"There are a lot of products out there with a limited customer base, and you want to avoid investing in AI tools where there is no feedback mechanism and no way to train algorithms to get progressively smarter and more accurate," Sommer said. In other words, algorithms are only as good as the data sources they rely upon. If certain demographic groups aren't present in a data set, for example, they won't be selected by the algorithm going forward. Or if a data set is overweighted with one kind of population sample, then bias will likely be present.

"A feedback mechanism allows customers to provide additional data sets and to correct some of the assumptions that an algorithm may be making by providing new data inputs," Sommer said.

Many HR vendors have introduced flight-risk analytics, for example, but most rely on existing transactional data from within the client company.

"If you really want to understand a larger number of indicators for an individual's propensity to leave a firm, you'd also want to include inputs from social media profiles, job boards, salary comparison data and more," Sommer said.

Poitevin agreed that HR leaders should be judicious in using analytics like those that predict employee flight risk. She believes the best use of flight-risk data is when it serves as an input for recommendation engines driven by machine learning that can suggest next-step actions. 

For example, one contributing factor to an employee identified as a flight risk might be a weakened social network in the company following layoffs or co-worker departures. 

"The recommendation engine might suggest steps to help that person get re-engaged in the organization, like building out a coaching or mentoring relationship with less-experienced employees, and suggest who specifically might be good candidates to be mentored," Poitevin said. 

Dave Zielinski is a freelance business writer in Minneapolis.

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