By Anja Pempelfort and Luisa Lenhard | July 3, 2025
Organizations around the world are considering the implications of pay transparency and pay equity regulations that result in fair and equitable employee compensation structures. The increasing expectation for employers to be better at rewards management requires compensation and HR professionals to provide objective reasons for pay differences among “like” employee categories.
While perhaps not the most glamorous work, job evaluations are a necessary step in designing and delivering fair compensation structures that are transparent and in-line with government-issued requirements, whether they be for a region, a state or a local government.
For example, the EU Pay Transparency Directive sets clear standards: Work of equal value must be evaluated in a gender-neutral and objective way (e.g., based on skills, effort and responsibility). In practice, compensation and HR professionals face considerable challenges:
Factor-based job evaluation methods provide differentiated and reliable results. However, they also are time-consuming and resource intensive. By leveraging the power of AI, organizations can harness a fast, efficient and replicable process for job evaluations.
WTW has been working with global clients to pilot AI-supported job evaluations. After nearly two years of conducting this pilot, we have found that the software has added value in three specific areas.
01
An international technology client acquired a foreign company with 500 employees who needed to be quickly integrated into the existing compensation structure. The acquiring organization’s HR department collected the job descriptions from the company being acquired and loaded them into our automated job leveling software.
After literally minutes, detailed evaluation results — including rationales — were available. This created a perfect starting point for discussions with different department heads. And, in the following weeks, open questions were raised and clarified during interviews so the evaluation results could be adjusted as needed. As a result, the tech company experienced:
02
A global logistics client traditionally used its own summary system for job evaluations. However, uncertainties persisted. For example, were the job evaluations still in-line with the market? Our automatic job leveling software provided clarity.
Fifty jobs were evaluated in parallel and, again, the AI outputs delivered results plus confidence values. Ultimately, the assessments often matched — and any deviations actually revealed gaps in job descriptions or differences to market value.
03
An international energy client discovered that similar jobs were being rated differently in different countries. The organization used our AI tool to analyze these inconsistencies, and after a preliminary rough classification (banding), the software harmonized the ratings across locations. As a result, the company saw significant time savings, had a focused review of valuations and received important tips for optimizing their job descriptions.
As these short examples show, AI can do far more than only save time. Developed and used the right way, an AI-based job evaluation system ensures:
AI-supported job evaluation doesn’t replace human expertise; rather, it complements it in a meaningful way. The “human in the loop” will continue to be indispensable, but leveraging AI will provide a quick, objective and market-compliant support. In turn, compensation and HR professionals will see their time freed to focus on more meaningful, strategic work to support defensible employee pay decisions.
