This breakdown is incredibly clarifying. The maturity matrix really nails why so many orgs with solid dashboards struggle with AI, they've optimized away the variance and edge cases that models need to reason properly. The observation that aggregation reduces cognitive load for humans but strips context for machines is something I wish more teams understood before they start building. Most data teams I've worked with treat this as a linear path when its actually two orthognal dimensions.
The other big difference between the two is what is measured. I find that most analytics focuses on $$, whereas AI focuses on moving some workflow forward and needs behavioral events / telemetry and user attributes for personalization.
This breakdown is incredibly clarifying. The maturity matrix really nails why so many orgs with solid dashboards struggle with AI, they've optimized away the variance and edge cases that models need to reason properly. The observation that aggregation reduces cognitive load for humans but strips context for machines is something I wish more teams understood before they start building. Most data teams I've worked with treat this as a linear path when its actually two orthognal dimensions.
Absolutely, thanks for the insights!
Love the breakdown
I wrote a similar post a bit ago: https://substack.com/@irinamalkova/note/p-174646404
2x2 matrix is 🔥!
The other big difference between the two is what is measured. I find that most analytics focuses on $$, whereas AI focuses on moving some workflow forward and needs behavioral events / telemetry and user attributes for personalization.
Loved it! The definition of ready is often misunderstood.