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Atlassian co-CEO Mike Cannon-Brookes shares internal data on AI tool adoption across 300,000+ enterprise customers — what AI features are actually used, which are ignored, and what this reveals about enterprise AI readiness.
This summary was generated from show notes and public descriptions, not from a full transcript review. Details may contain inaccuracies.
Canon
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Cannon-Brookes argues that enterprise AI adoption is governed by trust, not capability. Users adopt AI features whose outputs they can quickly verify (text summarization — I can skim the original). They reject features whose outputs require expertise to evaluate (risk prediction — how would I know if this is wrong?).
Highlights
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Enterprise AI adoption follows a power law: 5% of AI features drive 90% of usage — most AI features are built but never used
Cannon-Brookes presents Atlassian's data: of the dozens of AI features they have shipped, only a handful drive significant usage. The most-used features are simple (summarize this page, draft this comment). The most sophisticated features (predictive project management, autonomous triage) are barely used.Was this useful?