EXPERIMENTS / AN OPEN LAB
Experiments in making knowledge functional.
There's good research on how organizations work, and most of it never reaches the people who could use it. Each experiment below takes something we know, from research and from our own client work, and turns it into a tool you can put to work today.
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Start with who you are
A senior leader responsible for results
Someone leading change from the middle
A team that keeps hitting the same wall
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Or browse all seven experiments
Each one works on its own
FIND THE REAL PROBLEMThe Intake FormDefine your program and prepare to talk to the Superadditive team.
NAME WHAT'S IN THE WAYChange BarriersFind the barriers holding a change back, and a two-week plan to test them.
BUILD MOMENTUMNavigating PoliticsFind a path to gather attention and energy for change.
CHOOSE HOW TO DECIDEThe DeciderFind a decision method that fits, with the tools to run it.
CHANGE HOW THE TEAM WORKSSparksPractical moves for problems that keep getting in a team's way.
FOLLOW AI AT WORKThe FeedEmerging research and new practices of using AI at work.
SPOT WHERE KNOWLEDGE BREAKSBrain DrainSpot where intelligence is lost, corrupted, or ignored at work, and how to respond.
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This week in The Feed
IN REVIEW · UPDATED SEP 27, 2026Alongside the tools, we scan new research and practice on AI at work from a vetted list of sources and people. Here's what's made the cut.
What the latest reading suggests
- Two working papers found open-weight and post-trained LLMs discriminate by gender, race and age in hiring simulations, with exclusion rates rising from 5.6% to 17.3% after post-training.
- An end-to-end audit of a real public employment agency's AI hiring system found age, salary and gender disparities that aggregate parity metrics had masked.
- In a simulated multicenter trial, junior clinicians caught only 15.8% of AI hallucinations; one real neuroradiology department using structured change management raised active review of AI outputs from 14% to 61%.