Audible · CX & UX Research · Consumer AI
As AI-powered features multiplied inside Audible's experience, listeners were left guessing which one to use for what. We ran three consecutive rounds of concept research to give the team an evidence-based blueprint for how AI assistance should show up — grounded in how listeners actually expect it to work.
Audible's AI-powered capabilities had grown up feature by feature, each with its own entry point and interface. Listeners were asking questions in the wrong places — a signal that they didn't understand which experience handled which task, and shouldn't have to. Every additional entry point raised the cognitive load, threatened adoption, and left value on the table when listeners couldn't reach AI help where and when they needed it. Audible needed evidence, not intuition, for how a unified AI experience should work.
We designed a three-round iterative concept research program with Audible members and external prospects. Round one used unmoderated testing across independent participant groups to compare four assistant concepts at scale. Round two brought the strongest directions into moderated interviews for side-by-side preference and expectation depth. Round three returned to unmoderated scale with six refined concepts, pressure-testing how the assistant's identity, context-awareness, and data-consent approach shaped trust.
Across the program, more than 100 participants evaluated twelve concept variations — every round taking the high-impact elements forward and retiring what didn't earn its place, with stakeholders seeing the strongest ideas side by side as the evidence accumulated.
Listeners don't want to be agent managers. Concepts with a single, dedicated entry point consistently outperformed fragmented and search-embedded alternatives — in moderated sessions, every participant preferred one intelligent front door that routes requests behind the scenes over choosing between AI features themselves. They expect the experience to read context — what they're listening to, where they are in the app — and meet them there. And trust proved identity-dependent: who the assistant is changes what data use feels acceptable, which makes transparent data practices and available controls load-bearing parts of the design, not afterthoughts.
The research anchored a north-star blueprint for Audible's AI experience: a unified, context-aware assistant with a single entry point, seamless behind-the-scenes routing, and a trust and consent framework matched to listener expectations — along with the validation experiments recommended to run before any scaled release. Product strategy grounded in what listeners actually expect, rather than how the technology happens to be organized.
People don't want to manage the machinery of AI — they want one door that quietly opens to the right help.