AI feature adoption / attach

Formula

Users (or accounts) using AI features / active users

Unit

%

Models

SaaS, Subscription

What it is

AI feature adoption / attach measures the fraction of active users (or accounts) who actually use at least one AI-powered feature in a given period. The formula: users (or accounts) using AI features divided by total active users.

How to calculate it

Define an AI feature interaction clearly and consistently — a deliberate invocation of a model-powered capability, not incidental exposure to AI-generated content in a feed. Count the distinct users who meet that definition in the period. Divide by total active users in the same period. Track at both the user level and the account level for B2B products, because account-level attach (at least one seat in the account using AI) and user-level attach (individual seat penetration) often tell very different stories about monetization readiness.

Why it matters

AI feature adoption is the precondition for charging an AI premium. If users are not engaging with AI capabilities, any AI-specific pricing tier or uplift sits on an empty foundation. The monetization correlation is meaningful: the notes here reference RevenueCat data showing AI apps achieving year-one LTV per payer roughly 40% higher than non-AI equivalents — but that premium accrues only when AI features are actually adopted and retained. A high attach rate justifies the AI SKU and de-risks the pricing strategy; a low attach rate is a product and onboarding problem before it is a revenue problem.

How to read it

There is no published benchmark for AI feature adoption / attach as a standalone metric. Survey-level data on "AI use at work" or "AI tool adoption" exists, but those figures measure general AI tool usage across the population, not feature-level attach within a specific product — they are not directly applicable here.

As an Omega Point estimate, a wide directional range is the most defensible posture: early-stage AI features in established SaaS products tend to see attach rates in the 20–60% range of active users in the feature's first year, with considerable spread depending on how prominently the feature is surfaced, whether it requires opt-in, and whether the use case is in the critical workflow or a peripheral one. This range is reasoned by analogy from feature-adoption literature for high-intent productivity tools — it is an informed directional figure, not a measured one.

Below ~20% attach is a signal to investigate discoverability and onboarding rather than to accept it as a baseline. Above ~60% in a large, diverse user base is strong and warrants scrutiny of whether the definition of "using" the AI feature is too loose. The most useful comparison is your own cohort trend: is attach improving with each new user cohort, and is attached users' retention materially better than unattached users' retention?