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Knowledge + Guidance

We use AI to measure things — but can we trust it?

You did not hand over judgment on purpose. It happened slowly, one report at a time, until the numbers felt like facts instead of guesses. Now you sense the gap but no one on the team has named it out loud. This is not a question of whether the AI is accurate enough. Accuracy is not binary, and no one has told you where the edges are. The real question is whether your team knows the assumptions baked into every number you now act on — and whether you've agreed, together, on what to check before you trust it. A black box shared by a team is worse than one person's blind spot. Each of you may assume someone else already checked. Fix that first.

◆ How this problem reads on the two dials
GuidanceKnowledge
More coaching
Some to learn
1:1 with AureliusWith others (a Pod)
Some one-to-one
Practise with peers
The team needs a few concrete concepts (what 'assumptions' and 'blind spots' mean in AI output) but mostly needs a structured practice of checking together, so guidance leads.
How the two dials adapt to you →
What’s really going on

You cannot trust what you have not examined, and a tool is no different. Name three things: what the AI measures, what it assumes, and what it cannot see. Do this together, in writing. Until you do, you are not using a tool — you are obeying one.

🔒 What you’ll build togetherUnlock by starting
A moveTogether, list every AI-generated number your team acted on this month. Name what it was supposed to measure.
A moveFor each one, write down what the AI assumed — about data, about categories, about normal. If no one can say, that is your answer.
A movePick one AI output and manually verify it by another method. Compare. Do this as a team exercise, not a solo audit.
A moveAssign one person per cycle to be the 'edge-finder' — their job is only to ask what the AI cannot see this time.
A moveAgree on a standing rule: no AI number goes into a decision without one person naming its assumption out loud first.
PractiseName the Assumption · a Pod of 4 · 30 min

What changes unlock by starting

  • Your team can name, specifically, what your AI tools assume and where they break down.
  • You stop treating AI output as settled fact and start treating it as a claim to test.
  • You have a shared, repeatable check instead of individual private doubt.
  • Decisions get slower by minutes, not days — but they get harder to blindside.

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