With the help of AI, some companies are bringing more structure and strategies to how they compensate their employees.
A 2025 survey from Korn Ferry found that about a quarter of the 5,717 companies surveyed could help determine compensation using AI. Only 22% of companies surveyed say they use AI for their external payment benchmarks, while 63% say they are considering using it.
Loostomus, the software company's leading compensation strategist, said that using AI to analyze large amounts of compensation-related data will help companies promote wage transparency and provide HR teams with additional tools to understand the new job market.
At the same time, HR workers need to audit and monitor AI-driven data tools to keep confidential information safe and avoid data errors that could potentially pay bias, said Gord Frost, global rewards solutions leader at global consulting firm Mercer.
AI can help fill in the gaps in compensation
At Payscale, the company uses a combination of AI modeling and HR-compatible pay data to help customers price their jobs.
Tools that do this are especially useful when work is limited. This is when data is limited because the job is new to the industry or there are rare requirements for the location, size of the company, education, or experience level.
“Our job is really a niche and we're struggling to find their matches,” said Kristen Damerow, an HR analyst at SmithGroup, an architecture firm that uses Payscale's services.
There is also Payscale Peer, a dataset built from payroll information that includes compensation data from over 5,400 organizations, Thomas said. This data is drawn daily from HR information systems or software platforms that help businesses manage their businesses. 100% of employers have been reported, which is different from the job post data used by other compensation vendors.
Rather than purchasing payroll surveys from research providers where information becomes outdated, peers can show depositor users whether the market is currently paying for a particular role. Thomas said if a compensation manager is trying to set a paycheck for a brand new job but doesn't have much data on that role, Payscale Verse will use Payscale Peer data and AI modeling to find similar roles in different locations. The AI algorithm then takes these differentiations and proposes market prices for new jobs.
Using AI, compensation managers can see more quickly and efficiently pay comparisons by location, industry and size. For example, if a company is not sure how much to pay for a cultural experience specialist in the hospitality industry, Payscale can bridge the gap by pulling data from similar areas such as travel and tourism.
Thomas told BI that after the gallery poem recommended a proposed salary match, the company will decide whether to accept it or not. She said companies accepted an estimated 88% of Payscale's AI-recommended matches, compared to the 12% that was previously accepted when Payscale began using the technology.
AI to automate HR tasks
Frost said AI could also automate routine and repetitive tasks for HR experts, such as submitting pay data for annual compensation surveys and obtaining benchmark data to compare wages across companies and roles.
For example, Frost has more timely and robust access to external market data, allowing the compensation team responsible for designing, implementing and managing programs that recognize employee contributions, to quickly respond to changes in the talent market by adjusting pay adjustments in real time.
Frost said HR experts can better understand which elements of the total compensation program have the most impact on employee retention and performance, and focus on investing in programs that resonate with different group of employees.
He added that HR teams can use AI to generate personalized topics to help managers explain their pay decisions and wage programs more consistently with employees across the organization.
Comparing the risks
AI can help you decide on compensation, but human surveillance remains important.
Thomas said that before data is added to Payscale's database, it has been verified using a set of automated outsight detection procedures. She added that human reviewers also regularly audit data.
“All of our tools are built with wage transparency in mind,” Thomas said. “PayScale works hard to help organizations understand where their data comes from and how they use it to reach payroll information.”
The rise of AI in compensation has also led to new questions about how vendors are building and using tools. Thomas said HR experts should work with vendors to understand that each vendor uses AI in their compensation management solutions.
“Employment is with employers to ensure they are aware of potential biases in vendor solutions,” Thomas said. For example, if AI models are trained on historical pay data showing that they acquire more women than women in the same job, they may unintentionally recommend lowering wages for female employees.
Frost said HR experts should be aware of the risks of using AI, especially when comparing employee data and protecting confidentiality when using payroll analysis tools.
“These are the kinds of responsibility that the total rewards team takes seriously, and AI is a powerful tool to help with the process, but the importance of the human element cannot be overlooked,” Frost said.
