How AI Is Transforming Employee Compensation Strategies in HR

AI-Powered Tools in HR Compensation Strategies

More companies are using AI to improve how they set employee compensation. AI helps analyze pay data across different markets, which is especially useful for pricing new or rare roles. This article is part of a series on fostering innovation in business.

AI brings more structure and strategy to compensation decisions. A 2025 Korn Ferry survey found that about 25% of 5,717 companies are already using AI for compensation. While only 22% use AI for external pay benchmarking, 63% are considering it.

Ruth Thomas, chief compensation strategist at Payscale, explains that AI can promote pay transparency and give HR teams a clearer view of new and changing job markets by analyzing large compensation data sets. However, Gord Frost, global rewards solution leader at Mercer, emphasizes that human oversight is crucial to protect confidential information and avoid data errors that could cause pay bias.

AI Can Assist with Filling Compensation Gaps

Payscale combines AI modeling with salary data contributed by HR teams to help customers price jobs. Their tool, Payscale Verse, is particularly useful when data is limited, such as for new or niche roles with unique location, company size, education, or experience requirements.

Kristen Damerow, an HR analyst at SmithGroup, notes the challenge of pricing niche jobs, which Payscale's tools help solve. Payscale Peer, a dataset with salary information from over 5,400 organizations, updates daily with employer-reported data gathered from HR information systems. This differs from many vendors who rely on job-posting data that can be outdated.

Payscale Peer shows current market pay for roles, while Payscale Verse uses AI to find similar roles across locations and suggests market prices for new jobs. This lets compensation managers quickly compare pay by location, industry, and company size. For example, if unsure how to price a culture experience specialist in hospitality, Payscale can pull data from a related field like travel and tourism.

Since adopting AI, companies have accepted about 88% of Payscale’s salary recommendations, a big jump from the previous 12% acceptance rate.

AI for Automating HR Tasks

AI also automates routine HR tasks like submitting salary data for compensation surveys and retrieving benchmark data. With faster access to external market data, rewards teams can adjust salaries in real time to respond to talent market changes.

Additionally, AI helps HR professionals identify which parts of total rewards programs most impact retention and performance. They can then invest in the programs that resonate best with different employee groups. AI can also generate personalized talking points for managers to explain pay decisions consistently across the organization.

Weighing the Risks

Despite AI’s benefits, human oversight remains essential. Before data enters Payscale’s database, it goes through automated outlier detection and human audits. Payscale designs its tools with pay transparency in mind and clearly explains data sources and usage.

HR teams should work closely with vendors to understand how AI tools are built and used. It’s the employer’s responsibility to watch for potential biases in AI models. For instance, if an AI model is trained on historical salary data showing men earning more than women, it might unintentionally recommend lower salaries for female employees.

Protecting confidentiality is another key concern when comparing employee data or using salary analytics. Total rewards teams take these responsibilities seriously. While AI is a helpful tool, the human element is critical to ensure fairness and accuracy.

For HR professionals interested in learning more about AI applications and training in this area, resources are available at Complete AI Training.