63% of comp pros are excited to see AI reduce unfulfilling work
According to the 2025 Compensation Best Practices Report, 49% of HR and Compensation professionals are optimistic around AI, with 34% of respondents still deciding.
Technology to help you work smarter
AI Match Suggestions in Price at scale in Payfactors Advanced
AI Match Suggestions in Price at scale helps you unlock the full value of your survey data by automatically surfacing high-quality matches aligned with your pricing methodology. Review, apply, or fine-tune AI-recommended matches in bulk, all without leaving the workflow. Whether you're pricing 50 jobs or 5,000, AI match suggestions streamline your process and strengthen your results so that you can price at scale with confidence.
AI Match Suggestions in Year Over Year in Payfactors
Payfactors uses advanced large language models (LLMs) to suggest the top 5 most relevant job matches from new surveys when last year's match is unavailable. It analyzes and compares job data (including job descriptions) between survey jobs from last year and this year to identify the most similar ones. You can easily review and accept the AI-generated matches, saving significant time and reducing manual effort in updating pricings.
AI Match Suggestions in Marketpay
Marketpay's AI Match Suggestions streamline market pricing by instantly identifying strong survey matches using just your job title, purpose, and currency. Optional filters allow for further refinement, so you can spend less time sifting through rows of data - while pricing with more confidence than ever.
AI-generated Job Summaries
Job Description Manager generates job summaries using generative AI. This way, instead of starting from scratch, you can review and revise a solid draft, and focus more on collaborating with managers to ensure the job description is accurate and the pay is right.
To date, over 4,800 job summaries have been generated by 650+ users!
Peer Auto-Match
Deep Learning technology can save you time and streamline your benchmarking by serving up high-confidence matches to the Peer data source. You can manually review and accept the suggested matches or allow them to auto-match based on your selected criteria.
“The Peer Auto-Match functionality is awesome! It effortlessly auto-matches jobs for us based on multiple factors and has definitely been a time saver for our team. The tool is especially helpful for those of us who love working in compensation but find the job matching process tedious.”
Shirley Rawson, Senior Compensation Analyst, VF Corporation
Marketplace Matches
Streamline your matching process in Marketpay by seeing recommended survey matches across different survey publishers. Based on your matching history as well as common matching trends of all users, an association-rules-based algorithm serves you the best recommendations.
Education Compensation Research & Insights
Suggested Match Model
Using Machine Learning and Natural Language Processing, Peer delivers high-confidence job matches based on multiple factors including job title, survey matches, industry, and more. This model powers the Peer Auto-Match functionality.
Differential Modeling
AI helps our Data Science team curate and analyze millions of HR-reported data points to accurately reflect the market, including geo, industry, and size- differentials that power the HRMA data set.
Comp Model
The model that supports Employee Reported Data uses a K-Nearest Neighbors algorithm to find and aggregate the available datapoints that are most similar to your job of interest. It summarizes the data we have that is closest to your job title, job location, industry, educational requirements, experience requirements, skills, and certifications, allowing you to see how these various factors affect market ranges.
Companies across the U.S. and beyond trust Payscale with the important work of compensation management.
Jobs priced through Payscale in 2024
in employee salaries managed