How cutting-edge HR leaders use AI to raise the standard of performance reviews

Most performance reviews still run on one manager’s view of each person, months-old feedback and hours of prep nobody has time for. Jessica Zwaan and Thomas Forstner decided that wasn’t good enough. Both changed how reviews work at their companies, then built AI workflows to make the new approach stick. In this 30-minute live session, they show you exactly what they built and what changed.
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At Leapsome, Jessie opened up peer feedback so anyone could give it to anyone, anonymously or by name. To check whether it worked, she vibe-coded an organizational network analysis dashboard in Leapsome. Connections doubled, and cross-team feedback rose by 10%. She’ll walk you through how the network view adds context to calibration. It shows who your most connected people are, and it flags cases where a weak leadership score sits next to strong peer feedback.
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At Juro, Thomas replaced six-monthly reviews with OIL checks: short conversations every two months that look forward as much as back. More check-ins usually mean more prep, so his team built a way around it. Employees use a Claude skill to pull context from Slack, Notion and Granola into a structured self-reflection. Managers then log the finished conversation into Humaans with one command.
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We’ll discuss:

• How one change to peer feedback doubled the connections in your performance data
• How to read manager ratings alongside an organizational network view
• How to run check-ins every two months without drowning employees and managers in prep
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Can’t make it live? Register anyway and we’ll send you the recording.

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