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Generative AI is expected to power up human resources. A recent McKinsey report identifies HR as having “the potential to become a front-runner in utilizing generative AI to realize large efficiency gains.”
The report’s authors contend that AI is set to change HR in profound ways.
“We are seeing a lot of startups that are taking particular use cases and driving them to perfection,” Julian Kirchherr, a McKinsey partner and co-author of the report told Reworked. “There is a lot of movement and healthy competition. If you did a market scan three months ago on what’s great out there, I can tell you for sure that it is already outdated. Every week, we see exciting new players and use cases entering the market.”
The consultancy firm isn’t the only one forecasting this transformation of HR. More than three-quarters of HR leaders participating in a January 2024 Gartner survey said that failing to implement generative AI in HR within the next two years would put their organization behind competitors. And more than one-third said they are actively looking into implementing generative AI in their teams’ workflows.
Among the four areas of HR that the McKinsey report identifies as ripe for AI-driven change is people analytics. Generative AI's capabilities in data interpretation and coding could drive transformation in this space.
4 Areas Where Generative AI Can Boost People Analytics
In the past, HR practitioners had to have expertise in how the capabilities within the technology work to get the most out of digital HR tools. Today, they can use plain-language prompts to get the same — or better — caliber of analysis.
“AI reduces some of the barriers to analysis and insight,” said Stacia Garr, cofounder and principal analyst at RedThread Research.
Here are four of the best use cases for generative HR in people analytics.
Related Article: Can Driving Usage Solve the People Analytics Tech Market’s Woes?
1. Identification of management potential
While people analytics is often used to try to identify employees who may be disengaged and at risk of leaving, AI can make it easier for HR leaders to identify those on the opposite end of the spectrum: workers who are likely to become top performers.
“You can teach an AI model the characteristics of people who have become successful, then generative AI can comb through all your data and identify characteristics that are very similar,” said Kirchherr.
He cautions that it is vital to ensure this process doesn’t manifest or magnify biases within your company’s management structure: “You need to bring anti-bias thinking and explicit criteria into the AI model so as not to just replicate the status quo. I want to raise that as a big red flag.”
2. Sentiment analysis
“Analytical AI is very mechanical,” said Kirchherr. “If you do a sentiment analysis and download what’s being said about your company on Glassdoor, it’s in a sense stupid: It will give you a very simple frequency analysis.”
For example, imagine that 100 people have written a review saying your company is “inspiring,” while 50 people say they are “inspired” by your company and 25 others say you are “an inspiration.” Analytical tools would have identified these as three separate data points, while generative AI can understand that all these terms mean the same thing and can merge them into one data point with a frequency of 175.
Generative AI can not only synthesize data points about sentiment, it can pick details of them apart.
For instance, previous analysis may have been able to assess that employees were expressing dissatisfaction with their parental leave benefit but could not discern why. Generative AI, by contrast, can interpret unstructured commentary to reveal that employees wish they had six months instead of four months of parental leave.
“AI can dive down deeper,” said Garr. “It can imbibe words in a way that technology couldn't in the past to get to a new level of granularity.”
Related Article: Here's Why HR Needs to Move From Service Center to Systemic
3. Learning development and career advancement
Learning experience platforms (LXPs) have proliferated in workplaces over the past decade and have become large repositories of training and other guidance for employees.
Some of these platforms may have as many as 15,000 training modules, but they typically offer little guidance for employees on which of these abundant resources will best fit their needs for learning. Now, companies can use generative AI to automatically match each employees’ skillset and training gaps to content in the LXP, building a personalized learning journey that will help the employee grow.
Generative AI can also help in matching employees with open positions in the company to boost internal advancement and make the best use of workers’ skills and experience.
“AI in learning development can act as a career copilot,” said Kirchherr. “Potentially, in a large company, generative AI can do a much better job in matching than a human could ever do.”
4. Performance reviews
Synthesizing and analyzing data that informs performance reviews is another place in which generative AI shines in HR.
Imagine a consultant who has worked on five different five-person teams, and all 25 of these workers fill out a form about what they like about working with the consultant and where the consultant could improve. The consultant’s supervisor now has 25 narratives to assess to create a single performance review, a time-consuming and perhaps difficult task. A generative AI tool can provide a synthesis of all these inputs quickly, providing a single narrative from which the supervisor can work to create the review.
Additionally, performance data can come in a variety of formats, which AI can integrate into a single set of conclusions in a fraction of the time humans would need to parse and collate all that information.
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From Experimentation to Specific Use Cases
Generative AI clearly holds potential for the HR function, but many organizations aren't quite ready to implement such use cases in a serious way.
Academy to Innovate HR (AIHR) recently did a study of one thousand of the organization’s clients and found a general lack of knowledge about how to scale and use generative AI within their organizations.
“I believe most people are predominantly experimenting with it, rather than identifying specific use cases,” said Dr. Dieter Veldsman, the institution’s chief HR scientist. “They only know how to use it for individual productivity in HR, such as by putting together data sets, visualizing information and creating a report.”
But generative AI can help in ways that go beyond these basic outputs. It's up to HR practitioners to take the next step.
