Where should intelligence live?

Expectations spread fast between categories. Once someone gets used to a product remembering their context, or suggesting the next step for them, they start expecting that everywhere else too. Most products aren't delivering on it.

85% of the 400 apps surveyed in our Mobile App Trends Report have a negative net promoter score.

People keep using those apps anyway, often out of habit, or simply because they're the default choice already on the phone. Usage and satisfaction aren't the same thing. A product can fall behind what customers expect long before those customers actually walk away. That doesn't make it irrelevant. It raises the bar for what counts as good.

Strava calls its AI feature "Athlete Intelligence." It summarizes your activities and, if you've set a goal, works that into the summary too (something like "a great interval session, that's great for your 10k race in June"). You also get a weekly batch of workout suggestions. That's roughly the extent of it.

Judging by the reaction online, plenty of people find it underwhelming. Andrzej Dąbrowski put it sharply on X in July: "Strava is absolutely the best entry point for AI coaching. Nothing comes even remotely close. and yet, their 'Athlete Intelligence' is completely irrelevant garbage that can't even recognize what kind of intervals you did. Even when you literally say it in the workout title."

Now look at Google's Fitbit app. Since May, it's shipped an AI feature Google calls "Fitbit Health Coach," built directly into an app that already existed. It reads sleep, recovery, and workout data and explains what that data actually means for you, and given access, it can factor in more of your life, like whether you worked late or slept badly, to adjust your training schedule.

The question was never whether a product can generate a summary. It's whether the summary is actually useful. On that measure, Google's Fitbit has done a noticeably better job than Strava's Athlete Intelligence.

Strava and Google made different calls here too. Strava enabled a Claude MCP connector for your training data (not the social layer), while Google exposes Fitbit data through a developer-facing API, meant for building on, not for asking an assistant directly. The practical difference is reach: a connector only works for people who know it exists and set it up themselves, while a coach built directly into the app reaches everyone who opens it, no extra steps required.

And here's the part worth sitting with: even with an underwhelming AI feature, I still open Strava daily, mostly for kudos. That's Strava's real moat, a social layer no outside assistant can replicate. Claude helps me understand my training; it doesn't replace what I get from opening Strava itself. Different jobs, different right answers.

Open up when you can point to two things: a clearly better outcome for the customer, and a measurable benefit for the business. If you can only name one, keep the capability inside for now, or run a smaller experiment first. Technical possibility alone was never a good enough reason.

AI assistants have raised what every product needs to clear. That doesn't mean handing your customer relationship over to whichever assistant they happen to be using. Your own product can still be the best place for someone to understand their own data, explore options, and build trust in what they're seeing.

Where intelligence belongs is different for every product. It can sit inside the product, or outside it, in integrations with external assistants and agents. We run a short discovery sprint with your team to map where intelligence genuinely pays off for your users, and what a first version of it looks like.

• Map where the value sits. We explore the use cases that matter to your users, and where intelligence can create value more effectively than what exists today.
  
  

• Prototype before you commit. We build and test promising solutions as part of the sprint, so you get early validation from real users before investing in a full build.