The TR2050 Reward Labs have progressed from framing the key tensions and assumptions in traditional reward systems to actively testing and validating new models. The Geneva Lab set the foundation by identifying what works, what’s broken, and which assumptions block progress. The group adopted a sprint mindset defining experiments, gathering data, and learning fast. That work has now evolved into structured testing across three interconnected streams: Future of Reward, Technology, and Performance Management.
In Geneva, participants prioritised skill-based pay, flexible work models, and gig worker integration as critical but under-explored areas. Several companies have since launched pilots to quantify the value of skills and experiment with pay transparency. In parallel, the US Lab focused on the broader future of work, examining how AI, automation, and new workforce models are reshaping fairness, pay structures, and the role of HR itself. Work is underway to classify automatable functions, develop 5–10-year scenarios, and test distributed pay equity models.
Technology continues to be the enabler and constraint. Fragmented systems remain a barrier, but the concept of a “reward data lake” has gained traction, linking HR, reward, finance, and payroll data for analytics, such as, pay equity insights, transparency and predictive analytics all enabled by AI. Members are now piloting chatbots, pay-range transparency tools, and AI-driven analytics to strengthen decision-making.
On performance management, both Labs have challenged long-held assumptions by testing whether differentiation truly drives performance, how bias shapes ratings, and how fairness can be measured. Early findings, such as Essity’s pilot showing that merit pay influences performance while annual bonuses do not, are already influencing design.
Together, these Labs show growing momentum for experimentation. The focus has shifted from theory to action—testing, comparing results, and scaling what works. The Melbourne Lab will build on this evidence, deepening collaboration across organisations and continuing to shape the foundations of future reward systems that are fairer, data-led, and adaptive to an AI-integrated world.