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Aero Interactive

Solutions

Design that gets your AI features used

AI feature adoption is the share of your users who try an AI capability, get value from it and come back to it. Aero closes 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.

Who it is 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.

Why 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](/blog-posts/ai-feature-adoption-gap).

  • The value arrives too late: several steps before the first useful result
  • The feature lives in the wrong place: a chat panel beside the work instead of inside it
  • Trust is uncalibrated: users cannot tell a good answer from a bad one
  • The first failure is final: no easy way to correct or retry

What we change

  • First-use design: the smallest safe action that shows real value
  • Placement: the feature appears where the task happens, at the moment it helps
  • Interface choice: inline suggestion, background automation or conversation, chosen per task. See [Stop bolting a chatbot onto everything](/blog-posts/choosing-the-right-ai-interface).
  • Trust signals: confidence, sources and reasoning shown in proportion to the stakes
  • Recovery: edit, retry and undo paths that keep a bad first answer from ending the relationship

Measure first, then design

Every change is tied to a number. Aero sets up product analytics, including tools like Pendo, to track trial, first-use success, repeat use and correction rate, and connects 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 [You shipped the AI feature. Nobody is using it.](/blog-posts/ai-feature-adoption-behavior-change)

  • 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, and it's the discipline most internal teams haven't built yet. Aero pairs 15+ years of system practice with AI for what AI does well: parallel exploration, deep research, system-wide consistency. You get AI speed plus the discipline AI can't generate on its own.

Related projects

  • Paradigm Life Portal

    Paradigm Life: Driving Adoption with Product-Led Insights

    Paradigm Life empowers individuals to directly manage their financial lives with dynamic dashboards, annual reviews, and educational resources. By leveraging Pendo analytics, the team transformed adoption from advisor-assisted to product-led. That reduced friction, improved engagement, and laid the groundwork for long-term growth.

Further reading

FAQ

Common 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.

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