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HR leaders are well-acquainted by now with A.I.’s potential to disrupt their industry. Some larger employers, including Mastercard and Genpact, have already started rolling out A.I. tools for their people teams that help the recruiting and hiring process, measure employee engagement, and promote learning and development.
But not everyone is ready to fully commit to the A.I. revolution. In fact, it seems few HR leaders are prepared to significantly invest in the new technology right away, according to a poll of 49 HR leaders at U.S. multinational firms with over 5,000 employees, conducted by consulting firm Mercer, and shared exclusively with Fortune.
None of the HR heads plan to financially invest in A.I. for their teams in a significant or moderate way in 2023, although 39% plan to invest a little, and 32% are unsure. The share of people leaders currently planning to invest in A.I. through 2024 also remains low at 6%, with 42% unsure about their A.I. investment plans through next year.
Although the HR departments are mostly holding off on investing financially in A.I., they are still eager to put in their due diligence about the technology, says Jason Averbook, a senior partner and the global leader of digital HR strategy at Mercer.
“One of the things I thought was funny, on a live cast that we did, is someone said: ‘Are you asking about investment in money, or time? Because if it’s money, it’s no, if it’s time, it’s yes,’” Averbook says. He also suspects that the share of HR leaders planning to significantly invest in A.I. by the end of 2024 will rise much higher than the current 6%, as teams get clearer about how they want to use the technology.
“People are experimenting, people are thinking, people are learning. And whenever that happens, the resource shifts from time to money,” Averbook adds. “We’re just early in that curve.”
Before they make A.I. a line item in their budget, Averbook suggests that HR teams start thinking about four key areas now:
—Identify the use cases for A.I. Organizations tend to put the cart before the horse with A.I. implementation. Instead of adopting a tool for the sake of it, identify a few problems that A.I. technology can be used to solve, says Averbook.
—Clean up your data. A.I. tools are only as good as the data they’re built on, and implementing a chatbot or other tool on top of bad data is like “putting frosting on top of a moldy cake,” Averbook says. Imagine, for example, how unhelpful a benefits chatbot would be if its language learning model was built on 19 different versions of an explanation of benefits document. He recommends organizations start investing in “massive cleanups” now.
—Prepare your workforce for A.I. investment. Algorithmic aversion is one of the most prominent risks employers face today with A.I. implementation. Now is the time to prepare your workforce for how A.I. will change their jobs, and get them excited about its potential, and eager to experiment with tools.
—Establish A.I. governance policies. Teams should establish guidelines for the safe and fair use of A.I.—well before implementing and training these tools.
“Data cleanup, experimentation, and governance are where people should be investing the time today so that when it comes time [for] the money investment, they’re ready,” says Averbook. “If they don’t do it today, they are falling behind by the day.”
Paige McGlauflin
paige.mcglauflin@fortune.com
@paidion
Reporter's Notebook
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Around the Table
A round-up of the most important HR headlines.
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Watercooler
Everything you need to know from Fortune.
On the road again. Business travel spending is estimated to grow 32% this year and surpass pre-pandemic levels next year.—Chris Morris
No more percentages. China has put a pause on measuring their youth unemployment rate following a record high in June. That month, 21% of Chinese citizens aged 16-24 were unemployed. —Nicholas Gordon
Six figure blues. Why is unemployment rising fastest among households making at least $125,000? All indications are pointing toward rising interest rates, which have hit high-wage sectors like tech and finance the hardest. —Irina Ivanova
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