AI Feature Maturity Ladder
Five dimensions, five levels, weakest dimension sets the scoreAI feature adoption problems get treated as prompt problems. Usually something else is holding the feature back, and teams have no structured way to find it. Role: Author. The framework is a synthesis; the dimension cuts and level rubric are my choices..
Score five dimensions independently and let the weakest set the level
Because a feature is constrained by its weakest dimension, not its average.
Measured bythe min-rule stated explicitly in the published rubric
Give every level both a user-visible signal and a team signal
Because a feature can look adopted while being operationally fragile, and the reverse.
Measured byboth signal columns present for all five levels
Make output quality one dimension of five, not the whole score
Because accuracy can improve while adoption stays flat.
Measured byfour of the five dimensions cover something other than output quality
The Maturity Ladder is a diagnostic framework for user-facing AI products: five dimensions, five levels, and the weakest dimension sets the score. It is a synthesis of published work on maturity models, usability and LLM evaluation; the sources are listed on the framework page.
The full framework is at /framework/. It is free to use and adapt for non-commercial work, with credit (license: CC BY-NC-SA). The blog post makes the argument for why the framework matters.