Know at 30 days whether your AI feature is driving retention — or just curiosity.

A structured session to define exactly when a user has experienced genuine value from the feature — not just opened it, but used it in a way that makes them more likely to stay and pay. Without this definition, adoption metrics are measuring the wrong thing.

Your AI feature gets a purpose-built adoption funnel: exposure, meaningful interaction, and value moment reached. This structure lets you identify where adoption stalls — awareness, engagement, or the payoff moment itself.

The patterns that predict poor adoption in AI features — high initial use followed by drop-off, low repeat engagement, feature abandonment after one session — are documented against benchmarks from comparable launches. You know the warning signs before you see them.

Adoption targets are anchored to your actual user behaviour, not industry averages that don't reflect your audience. This produces 30/60/90-day thresholds that are both ambitious and credible.

AI features introduce a different willingness-to-pay dynamic than standard product functionality. This assessment gives you a documented read on how your users are likely to respond to usage-based, tiered, or add-on pricing before you commit to a model.

A complete event taxonomy covering exposure, meaningful interaction, value moment reached, and failure signals. Engineers get a spec they can implement directly — no ambiguity about what to track or why.

Written benchmarks for what healthy adoption looks like at 30, 60, and 90 days post-launch, calibrated to your user base. Your team knows from day one what success looks like and when to act if numbers fall short.

Built in your live PostHog instance and connected to real data before the engagement ends. You're not inheriting a mockup — you're inheriting a working dashboard your team can open on launch day.

A structured test for validating your AI feature pricing, including two variant definitions, targeting criteria, and a defined success metric. You can run this immediately after launch rather than waiting months for organic pricing signals.

A reusable template for reviewing adoption performance at each 30-day milestone, with your benchmark thresholds already embedded. Running a structured post-launch review goes from a two-hour preparation task to a 20-minute exercise.

The single most important event in your AI feature's lifecycle — the moment a user gets real value — is instrumented and verified before launch. Without this, you're measuring vanity engagement rather than the behaviour that predicts retention.

Every event, property, and expected value is documented in a format your engineering team can implement without clarification. This cuts instrumentation time and prevents the tracking gaps that make post-launch analysis unreliable.

A written explanation of how each benchmark threshold was set, so your team can defend the targets in a board review or investor conversation without needing to reconstruct the logic from scratch.

Step-by-step documentation of how the PostHog dashboard was built, so your team can extend it, rebuild it after a migration, or replicate the structure for a future feature launch.

On launch day, you have direct access to flag anomalies, interpret early data, and make real-time decisions. The first 24 hours of data often require interpretation that a dashboard alone can't provide.

A structured call seven days post-launch to review early signals and adjust if needed, plus a documented framework for your 30-day performance review so the analysis runs the same way every time.