Insights from the Total Rewards Lab Series 1/2026
Total Rewards is changing. Frameworks are becoming harder to defend. Employees are asking questions the playbook cannot answer. AI is arriving before the foundations are ready. And the workforce these systems were built for is already changing.
These are some headlines from our first Total Rewards Labs series we launched earlier this year together with UFlexReward. In this summary we are giving a glimpse of the discussions led during the four sessions.
Most reward leaders know something has shifted
Simone Schmitt-Schillig, founder and managing director of Unequity and UFlexReward CEO Leigh Bornstein came together to explore that honestly. Over four sessions, known as the Total Rewards Labs, they brought senior reward professionals from global organizations across Europe, North America, Israel, and beyond into a space designed for open dialogue rather than presentations. No predetermined answers. No agenda. Just people who work in reward every day, comparing notes on what they are seeing, what they have tried, and what has not worked as expected.
This blog article draws on those conversations. Rather than offering definitive answers, it reflects the recurring themes, tensions, and questions that surfaced across the four sessions.
What the conversations revealed
- The workforce has already changed. Reward has not.
The architecture of total rewards assumes a stable, full-time, long-term workforce. That assumption is becoming harder to sustain. Fractional workers, project-based teams, and ecosystem contributors are growing fastest, yet reward systems still serve almost exclusively the traditional core workforce. More details here. - Flexibility is promised more often than it is delivered.
Every organization wants to offer flexibility. Most have tried. Across multiple organizations, default take-up rates on flexible offerings ran at 70 to 80 percent even after extensive communication and education. The gap is not ambition. It is the infrastructure needed to support genuine choice. Findings in detail. - Data is available. The confidence to act on it is not.
Most reward teams have more data than they can act on. The constraint is connecting that data to decisions that matter to the people who make them, and finding the hook that makes leadership pay attention. Benefits data often becomes relevant to Finance only when you show them the pension liability number. More info. - AI exposes weak foundations. It does not fix them.
Personal AI adoption among participants runs at seven to nine out of ten. Professional AI adoption in reward sits at three to four. The gap is not technology. It is governance, inconsistent documentation, legal nervousness, and a lack of organizational confidence in the output. Read full article. - The gap between design and practice is the defining challenge.
Across all four sessions, one tension surfaced consistently. The distance between how reward is designed and how it lands in practice. The problem itself is not new. But one that AI, regulation, workforce fragmentation, and rising employee expectations are making impossible to defer.
Interested in joining a future Lab?
The Total Rewards Lab series continues. Sessions are intentionally small, practitioner-led, and limited to a maximum of twelve participants. If you work in reward and want to be part of the next conversation, we would like to hear from you.
To express interest in a future session, complete the short registration form by clicking here. If you are new to Unequity or UFlexReward, you can learn more in our article From data to dialogue.
If you would prefer a conversation, we would be glad to hear from you. Reach us at simone@unequity.com with a meeting request.
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- Simone Schmitt-Schillig