The Attribution Gap is the space between 'this worked' and 'we know why it worked.' It looks small from a distance and it's the most expensive gap in the discipline, because everything downstream depends on closing it.
An unattributed win is barely more useful than a loss. It gave you revenue once but nothing you can steer with, because you can't tell which conditions to recreate. The next initiative starts from zero, and the organization keeps buying lottery tickets instead of building a machine.
The trap is that the win buys permission to stop asking questions. It worked, so why interrogate it — which is exactly backwards. The win is the most valuable thing you have to study, and the thing organizations study least.
Your ability to scale breaks first, silently. You try to stand up what worked in one team somewhere else and it doesn't take, because you were carrying the memory of the result, not the actual cause. The failure gets blamed on the new team instead of the missing attribution.
Take your best AI success story. Could you recreate the exact conditions that produced it, on purpose, in a different team, next quarter? If you're not sure what those conditions even were, you have an attribution gap.
Attribution Gap is the mid-tier pattern of the Business Value & Measurement layer in the AI BRIDGE Assessment — the diagnostic for whether your operating model is ready for AI. The assessment scores every layer, names the pattern under each, and tells you which one is holding you back the most.
The free AI BRIDGE Assessment names the pattern breaking you first. 30 questions, ~10 minutes.
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