*The AI Revolution:
Reshaping HR Functions for Success*
By Jillian OgawaStaff Writer, Workday
Instead, it’s the humans putting AI to work. From improving crop production in agriculture to enhancing patient engagement in healthcare, industries are leveraging AI in ways that benefit humanity.
The same is happening in human resources (HR) management. HR practitioners are using AI to help enhance employee experience in the workplace and improve the efficiency of HR processes. What’s more, AI is showing that it can bolster the next evolution of the office of the CHRO—partnering with the C-suite to drive a business strategy that’s more inclusive and holistic.
According to “AI IQ: Insights on Artificial Intelligence in the Enterprise,”a new Workday survey of 1,000 business decision-makers from around the globe, the top HR‑related tasks that are being augmented with AI include recruiting and applicant tracking, business analytics, and skills assessment tools. What’s more, 65% of respondents said their existing AI and machine learning (ML) deployments have improved employee experience, a key business indicator and a purview of HR.
Here’s a further look at what HR leaders stand to gain when they augment the following HR functions with AI.
HR leaders must have technology that supports how skills are used for the future of work that’s here today: moving away from the rigid idea that work is done through structured job roles and responsibilities, and instead viewing work as a more fluid compilation of skills to leverage for changing business requirements.
“As organizations accelerate and scale their skills‑based talent strategies, it’s impossible to know and manage the skills of their workforce—now and in the future—manually,” says David Somers, group general manager of products for the office of the CHRO at Workday. “Understanding, let alone matching, workers’ skills to business needs simply isn’t possible without AI and ML tools that help make sense of all the data.”
AI and ML go beyond identifying and mapping out the skills of employees to different projects or roles, which is a typical skills management approach. Instead, technologies augmented with AI and ML help organizations understand how skills relate to one another and how they can evolve to other adjacent skills, which is crucial insight because skills are changing constantly. For example, a worker skilled in Microsoft Excel may also have skills in data analysis, reporting, and other tasks Excel is used for. AI and ML help uncover the depth of skills in the organization and gain the insight needed for skills-based initiatives.
Companies are using AI to match labor demand with worker qualifications, skills, availability, preferences, and more to optimize schedules for both workers and the business.
Once solely an HR department priority, employee engagement has become a C-suite priority, fueling many business drivers from productivity to innovation and more. Company leaders want to understand how their employees perceive their employer and how they can take advantage of the insights they glean; HR leaders, in turn, are taking those insights to the C-suite and using them to create a more engaging workplace.
The predictive qualities of AI are helping companies gain greater insight into one of the challenging aspects of employee engagement: understanding which employees may be more apt to quit. HR leaders can use those insights to take prescriptive actions to help mitigate the risk of employee burnout and attrition.
Somers shares an example of how natural language processing—an ML technology—can help leaders understand employee sentiment. Somers explains that organizations are using Workday Peakon Employee Voice, an intelligent listening platform, to help understand and determine attrition risk. It has an attrition prediction feature that leverages AI and ML and uses a statistical model trained on the leaving behavior across millions of survey data points in the database.
According to Somers: “The model calculates attrition risk per employee based on their responses and scores over a period. It then uses employee-level attrition risk to calculate the average attrition risk for each segment, as well as for the whole company. It also compares the average risk of each segment to the average risk of the company to assign an attrition risk level—for example, it could reveal that the attrition risk in the marketing segment is in the top 10% of your organization.”
These insights can guide companies on how to improve employee engagement, such as increasing well-being benefits or assessing workloads.
The automation revolution—the push to execute tasks without manual intervention—in HR was happening well before the pandemic. But navigating unprecedented disruption accelerated the need for digital innovation and, as a result, ushered in the wave of AI adoption and the next evolution of agility and efficiency in HR: intelligent automation, which involves reading data and making predictions from that data. In other words, intelligent automation is automation paired with machine learning.
That is especially helpful for HR tasks that are routine but dynamic, such as scheduling and meeting labor demand. For example, companies are using AI to match labor demand with worker qualifications, skills, availability, preferences, and more to optimize schedules for both workers and the business. This is especially prevalent among companies that employ frontline workers where shifts are constantly changing and managers need to fill and adjust on short notice.
“With AI providing recommendations for workforce scheduling by automatically mapping worker availability and skills to open shifts, companies can ensure they’re not over- and underscheduling, while better controlling labor costs and preventing worker burnout,” Somers says.
While AI is certainly reshaping the role of HR, leaders in HR must be the drivers along with other business leaders in implementing AI.
Here’s what’s key about AI: its ability to perform tasks, such as conducting predictive analytics or generating new content (generative AI), depends on the quality of its AI foundation model, which is only as good as the data that feeds it. That strong commitment to data governance starts with the belief and implementation that HR data is relevant across the business—which happens to be the same as the foundation of Workday Human Capital Management (HCM). Built on a unified data model and single security model, Workday HCM can read diverse data sets to perform a wide variety of analytics and report use cases across the business.
“Therefore, among the big ‘watch-outs’ when it comes to implementing AI is to maintain clean and coherent data to help ensure accuracy and quality control,” Somers says. “If the data sets are not clean, HR and people leaders can end up with inaccurate outcomes that can lead to costly mistakes.”
Surveys on public sentiment toward AI—conducted globally and on the country level in places such as Great Britain and the U.S.—reveal that increasingly more people are envisioning a future where the power of AI can have a positive impact. At the same time, these surveys also reveal concern toward AI, particularly about having adequate regulation.
For every use case that leverages AI to increase the impact of HR, what’s important to remember is what makes those efforts successful: enhancing decision‑making with additional insights and improved efficiencies. It’s not about replacing what makes HR professionals so valuable: being stewards of company culture by connecting what happens in the business to the company’s purpose and values.
As Somers puts it: “Ultimately, it’s important to keep humans at the center so they are the final decision‑makers. With a human-in-the-center approach, AI and ML can help make people more productive and better informed—enabling them to solve problems they didn’t think they could solve before.”
Ultimately, it’s important to keep humans at the center so they are the final decision-makers.
