AI feature adoptionBy Lindsey J
Why nobody is using your new AI feature
AI feature adoption is the real bottleneck: why shipping AI features outpaces real use and value, plus a five-question AI feature adoption check for any product team.
5 min read
AI feature adoption
AI feature adoption: design that gets your features used
AI feature adoption design: first-use design, trust signals and in-workflow placement, instrumented so you can see which change moved adoption.
Teams we have worked with
AI feature adoption
AI feature adoption is the share of your users who try an AI capability, get value from it and come back to it. We close the gap between shipping an AI feature and having it used by redesigning the first use, the trust signals and where the feature sits in the workflow, then instrumenting it so you can see which change moved adoption.
Why do good AI features go unused?
The pattern repeats across industries: a capable model, a successful launch and a usage curve that flattens within weeks. The causes are almost always in the experience around the model. Our diagnosis is in Why nobody is using your new AI feature.
#01
The value arrives too late
Several steps before the first useful result
#02
The feature lives in the wrong place
A chat panel beside the work instead of inside it
#03
Trust is uncalibrated
Users cannot tell a good answer from a bad one
#04
The first failure is final
No easy way to correct or retry
What gets an AI feature used?
#01
First-use design
The smallest safe action that shows real value
#02
Placement
The feature appears where the task happens, at the moment it helps
#03
Interface choice
Inline suggestion, background automation or conversation, chosen per task. See Stop bolting a chatbot onto everything.
#04
Trust signals
Confidence, sources and reasoning shown in proportion to the stakes
#05
Recovery
Edit, retry and undo paths that keep a bad first answer from ending the relationship
How is AI feature adoption measured?
Every change is tied to a number. We set up product analytics, including tools like Pendo, to track trial, first-use success, repeat use and correction rate, and connect them to a metric your business already reports. That turns the next round of design into an experiment with a result, not an opinion. More on the behavior side in how to get people to use your AI feature.
Selected work
Selected work
The engagement
What do you get?
Who is this for?
Product and growth leaders at B2B software companies who have shipped one or more AI features and are not seeing the usage, retention or revenue effect that justified building them.
- Adoption diagnosis: where users drop between launch and repeat use
- Redesigned first use and in-workflow placement
- Trust, confidence and recovery patterns
- Product analytics instrumentation and adoption dashboard
- Experiment backlog ranked by expected effect
Solutions
Related services
AI product strategy
The fastest teams decide with evidence, not opinions. We instrument products with tools like Pendo and run AI-assisted experimentation, turning real user behavior into a continuous loop of what to build, test, and sharpen next.
AI-accelerated UX and UI design
Pixels are commoditized. Brand-fluent, accessible systems aren't. Your team can ship the next screen. Keeping your fiftieth screen on-brand, on-pattern and on-business-logic is a different problem: design system governance, the discipline most internal teams haven't built yet. We've been designing since 2014, and we pair that experience with AI for what AI does well: parallel exploration, deep research and system-wide consistency. You get AI speed plus the discipline AI can't generate on its own.
Further reading
Field Notes
AI feature adoptionBy Lindsey J
Getting people to use your AI feature: a behavior-change approach
5 min read
Got questions?
Frequently asked questions
What is the AI feature adoption gap?
The AI feature adoption gap is the distance between shipping an AI capability and having customers use it often enough to create value. AI has made features cheap to build, so they now launch faster than users can find, trust or fit them into their work. The gap shows up as features with high launch traffic, low repeat use and no visible effect on retention or revenue.
Why do AI features go unused even when the model is good?
Most unused AI features fail on design, not accuracy. The value takes too many steps to reach, the feature sits in a separate panel instead of the place work happens, users cannot tell when to trust the output, or the first attempt fails with no recovery. Each of these is fixable without changing the model, which is why adoption work often pays back faster than model work.
Which metrics show whether an AI feature is adopted?
Track the share of eligible users who try the feature, the share who reach a useful result on first use, repeat use within a set period, and how often users edit or discard the output. Tie those to a metric the business already watches, such as retention, support tickets or time to complete a task, so adoption is judged by value, not clicks.
Is low AI adoption just an onboarding problem?
Rarely. Tours and launch banners reach people before they have a reason to care. Adoption improves when the feature appears at the moment in the workflow where it saves effort, when the first action is small and safe, and when a wrong answer is easy to fix. Onboarding helps once those are in place; it cannot replace them.
Bring the AI feature nobody is using
Book a 15-minute call. We will point to the most likely reason it stalled and the first change worth testing.