Technological change

Not long ago, compensation practices relied heavily on cultivated human expertise.

Professionals learned through apprenticeship-style development, slowly building the technical and judgment-based skills that allowed them to interpret market data within the context of an organization’s culture, strategy, and affordability. Annual compensation surveys created predictable cycles that dictated when benchmarking, assessment and program design took place. Human interpretation and organizational nuances were central to the process.

Read: Expert panel: How employers can rethink pay-for-performance in a volatile economy

Technology, especially artificial intelligence, has dramatically altered the compensation landscape. In recruiting, AI now generates job descriptions, screens applicants and builds shortlists. While efficient, these tools risk weakening human resources’ understanding of the true scope and required technical expertise of what roles actually involve, introducing bias and producing job postings that may lack precision or relevance.

In benchmarking, AI-driven salary data is widespread, but often unverifiable or inconsistent. Job titles may not align across companies, and self-reported salary information, particularly when influenced by emotion, as on platforms like Glassdoor, may distort expectations. Employees increasingly rely on these tools, often resulting in unrealistic perceptions of market value or compensation mix.

Meanwhile, total rewards functions have grown leaner. With fewer seasoned practitioners and greater reliance on AI tools, junior professionals have fewer opportunities to develop foundational expertise. The field is beginning to show early signs of a widening knowledge gap. 

Read: AI supporting, but not replacing, pension, benefits teams: experts

As AI becomes more integrated, early-career compensation roles will likely shift away from purely analytical work toward responsibilities like program delivery, change management, and stakeholder engagement. These expectations may prove challenging without years of technical practice to build judgment and confidence. Compensation has always been a blend of science and art. While AI may support the science, the art —........

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