Compensation Teams Struggle with AI Adoption Despite Data Readiness

The report, which surveyed over 525 total rewards professionals, identifies a persistent "say–do gap" within the industry. Organizations are 2.4 times more likely to have the requisite data infrastructure in place than they are to actually deploy AI use cases. While data readiness capabilities average over 53% adoption, actual AI implementation lags significantly at 22%. According to Alex Cwirko-Godycki, general manager of market data at Pave, the primary barrier is not budget or technology, but a lack of governance and structured data preparation.

The Path to Measurable ROI

The 15% of organizations successfully achieving business impact follow a distinct, five-step sequence: establishing a standardized job architecture, documenting compensation philosophies, implementing AI-powered benchmarking, enforcing data quality processes, and integrating compensation systems. AI-powered benchmarking emerges as the most effective catalyst, as it allows for human oversight while automating the gathering of market data. Conversely, companies that bypass these foundational steps to adopt advanced tools often find their progress stalling. The data also highlights a visibility disconnect, with team leads reporting a 25% impact rate compared to a near-zero rate among C-level executives, suggesting that management remains largely unaware of the practical realities of their own AI integration efforts.