I don’t buy “chaos → AI → insights.” If your inputs are a mess—spreadsheets, one-off exports, “someone’s SQL,” three versions of the same metric—AI just helps you get to confidently wrong faster. Metabase’s community report basically echoes that: AI is everywhere, but trust in AI outputs is still low, especially among more technical folks.
So I’m going with “chaos → Metabase → AI → insights ×10.” First you use Metabase to turn the mess into something you can stand behind: cleaned tables, consistent definitions, a few canonical metrics, clear refresh cadence, and traceability. Then AI does what it’s actually good at: reading the processed data, spotting patterns and weirdness, explaining what changed, proposing plausible drivers, and helping you decide what to check next.
That’s how you earn trust: the AI isn’t inventing reality — it’s interpreting a reality your system already made solid.
So I’m going with “chaos → Metabase → AI → insights ×10.” First you use Metabase to turn the mess into something you can stand behind: cleaned tables, consistent definitions, a few canonical metrics, clear refresh cadence, and traceability. Then AI does what it’s actually good at: reading the processed data, spotting patterns and weirdness, explaining what changed, proposing plausible drivers, and helping you decide what to check next.
That’s how you earn trust: the AI isn’t inventing reality — it’s interpreting a reality your system already made solid.