Turning Around an Underused AI Product: Diagnosis, Fixes and Metrics

Adoption of an AI product you manage is low. How would you find out why, and how would you turn it around? ```hint Find where people stop Split adoption into the steps a user goes through, from learning the AI product exists to relying on it every week, and find the step where most of them drop out before you choose a fix. ``` ### Clarifying Questions - Is this a standalone AI product, or an AI capability inside an existing product, such as an assistant built into a security operations console? - How is adoption measured today, and is it low compared with the launch target, with other features, or with a competitor? - What has to happen before a user can try it: does an administrator need to enable it, is it a paid add-on, or does it need access to the customer's data? - How long has it been live, and has usage been low from the start or did it drop after an early spike? ### What a Strong Answer Covers - A precise definition of adoption and of the eligible population, and a check that the measurement itself is right - A diagnosis that breaks adoption into stages and segments to find where, and for whom, it fails - Root causes specific to AI products, such as trust in the output's accuracy, unclear value, data and privacy concerns, and poor fit with existing workflows - Fixes matched to the diagnosed cause and prioritized, rather than a list of generic growth tactics - Success metrics that track retained, valuable use with guardrails, and a point at which to reposition or retire the product ### Follow-up Questions - Usage jumps after you turn the AI feature on by default, but a month later most of those users have stopped. What do you conclude? - Customers say they do not trust the output enough to act on it. What would you change, and how would you measure whether trust improved? - When would you decide the product should be repositioned or shut down rather than fixed?