Service
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.
Who is this for?
Product leaders at software companies who are adding AI to the product and need evidence, not opinions, to decide what to build, test and cut next.
Why do product teams need evidence before adding AI?
AI makes features cheap to ship, so the hard part moves to choosing which ones deserve to exist. Teams that decide by opinion ship AI features nobody uses. Teams that measure real behavior can see which features earn their place, and cut the rest before they cost more.
What do we set up?
- Product analytics: instrumentation with tools like Pendo, tied to a metric your business already reports.
- Dashboards and funnels: KPI reporting your team actually reads.
- Feedback loops: NPS, surveys and structured feedback from the people using the product.
- Experiments: AI-assisted experimentation and optimization cycles, so each change is tested against real use.
How does AI help decide the roadmap?
We use AI where it is strong: sorting feedback at volume, finding patterns across usage data and drafting options to test. People still decide. Every prioritization call ties back to a number, so the roadmap can be argued with evidence.
What if we already shipped an AI feature nobody uses?
That is a common starting point. Our AI feature adoption work finds why a capable feature goes unused and redesigns the experience around it, measured on trial, first-use success and repeat use.
Proof and practice
We run the same loop on our own work. Visits from AI crawlers and referrals from AI assistants to our site are measured in analytics, and each change is checked against them.
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Further reading
AI product strategyBy Lindsey J
When every product has AI, what actually makes yours different?
6 min read
FAQ
Common questions
What is AI product strategy?
It is deciding what to build with AI based on evidence: product analytics, user feedback and AI-assisted experiments, connected to a metric your business already reports.
Which analytics tools do you use?
We set up product analytics with tools like Pendo, plus KPI dashboards, funnels, NPS and surveys.
How is this different from AI feature adoption?
AI product strategy decides what to build. AI feature adoption fixes a feature that already shipped and is not being used.
Make your product agent-ready
We design the products, websites and design systems your customers use and their AI agents act on.