Advancing People Analytics Survey
Turn people data into decisions that move the business.
Ambition is high. Follow-through is still uneven.
Organizations want analytics to support better decisions. But most are still focused on reporting, benchmarking, and day-to-day HR activity. The real opportunity is to connect trusted data to the decisions leaders make every day.
Based on responses from 616 HR and analytics leaders across 75 markets and countries:
invest in rewards benchmarking and optimization
focus on attrition or retention strategies
prioritize workforce planning
report predictive or prescriptive capability
Where the next phase of people analytics will be won
- 1 Workforce
- 2 Rewards
AI is moving fast. Readiness still depends on strong foundations.
- Organizations are building stronger measurement foundations around AI’s workforce impact, skills, career progression, and ROI. The organizations that move most effectively will start with a few practical work redesign use cases and build a clearer view of the skills that support adoption and change. Only 30% currently prioritize AI-driven work redesign.
- 41% plan to measure AI’s impact on workforce skills within 12 months.
Starting small can build the capability, confidence, and readiness needed for broader change.
Benchmarking matters. Strategic impact is the bigger opportunity.
Rewards and compensation remain the strongest foundation for HR analytics. But the value story can go further than market comparison alone — into pay equity, workforce cost, retention, and business outcomes.
- 68% say external benchmarking is important or critical.
- 52% of operational leaders say pay equity analysis is a priority.
The foundation is already in place. The next step is using reward insights to inform broader talent and workforce decisions.
The maturity gap
This chart highlights the key challenges still affecting people analytics progress, with data quality emerging as the biggest barrier.
Five priorities for advancing people analytics
-
Fix the foundations
Improve data quality, integration, and automation before adding complexity. -
Reach decision-makers
Put insight in front of operational leaders and line managers. -
Close the action gap
Connect analytics outputs to business questions, decisions, and feedback loops. -
Build a practical AI roadmap
Start with two or three high-value workforce decisions where trust and impact matter most. -
Invest in depth
Build predictive, data engineering, and AI/ML capability where it will make the greatest difference.