AI FEATURE LAUNCH — $2,997–$4,997 · 2-WEEK SPRINT

Jake McMahon — ProductQuant

8+ years B2B SaaS · Behavioural Psychology + Big Data (Masters)

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

A 2-week sprint that instruments your AI feature before launch — what to measure defined, the adoption funnel built, and a test design ready to run. Day 30 gives you a verdict, not a dashboard to interpret.

Start the Sprint → See what’s included ↓

Instrumentation live before your launch date · full refund guarantee

WHAT YOU HAVE AT THE END

Value moment defined The specific action that proves the AI output was actually useful — agreed before any events are built

Instrumentation spec Engineering-ready event taxonomy covering exposure, interaction, value moment, and failure signals

Adoption benchmark What “working” looks like at 30, 60, and 90 days — set before you ship

Adoption dashboard PostHog dashboard live and connected before first user sees the feature

Pricing test design Two variants, hypothesis, and success criteria ready to run at launch

$2,997–$4,997 · fixed price · 2-week sprint

Teams Jake has worked with

WHAT HAPPENS WHEN AI FEATURES SHIP WITHOUT A MEASUREMENT PLAN

Six months post-launch — nobody can say if it’s actually helping anyone

“We have interaction numbers. They look reasonable. But we can’t tell whether users are finding value, coming back, or just clicking the button once and never returning. Without a value moment in the funnel, all we have is noise.”

VP Product — B2B SaaS

Events were added after launch — they measure what was easy, not what matters

“We track ‘AI feature used’ but the value moment — the specific downstream action that confirms it actually helped — was never defined. The dashboard shows clicks. It doesn’t show adoption.”

Head of Growth — Series A

Pricing decision made by gut — no data running at launch to inform it

“We bundled it in because we didn’t know how to price it. Now we can’t tell whether it’s a growth driver or a cost centre. No test ever ran. The window to run one at launch is gone.”

CEO — Seed stage

WHY AI FEATURES NEED A DIFFERENT MEASUREMENT APPROACH

Watching DAU after an AI feature ships tells you about curiosity. It tells you nothing about adoption.

An AI feature has a different adoption curve than a standard product feature. The first interaction tells you the feature was discovered. Whether the user reaches the value moment — the specific downstream action that confirms the AI output was actually useful — tells you whether it delivered. Whether they return within 7 days tells you whether there is a habit loop or just a curiosity loop. These are three different things, and they require three different measurement points in the funnel.

Most teams build an AI feature, ship it, and watch a “users who clicked the AI button” chart. Six months later, the feature is still live, the chart looks acceptable, the AI cost is on the P&L, and the team still cannot answer the question their board will ask: is this feature delivering value or just avoiding being noticed enough to cut?

This sprint gets the right measurement in place before the feature ships. The value moment is defined in a working session before any events are built. The adoption funnel is designed around that definition. The benchmark is set. Day 30 produces a verdict — not another month of waiting to see if the data says something.

TIMELINE

From feature walkthrough to instrumentation live — before your launch date.

Week 1, Days 1–3: Feature walkthrough & value moment definition

A working session to understand what the AI feature does and what a useful outcome looks like in practice for a real user. The value moment is defined here — the specific action a user takes that confirms the AI output was genuinely helpful. This definition drives everything else. Current instrumentation, if any, is reviewed and gaps are documented.

Week 1, Days 4–7: Adoption funnel design & event taxonomy

The four-stage adoption funnel designed around the value moment your product team agreed. Event schema documented with specific names, properties, and trigger conditions for every stage — exposure, first interaction, value moment, and repeat use. Failure events included. Adoption benchmark set with 30 / 60 / 90-day thresholds. Engineering receives a spec they can implement directly.

Week 2, Days 8–12: Dashboard build & pricing test design

The PostHog adoption dashboard built and connected to the event taxonomy before launch — so the first day of data flows into a measurement system that already knows what it is looking for. Pricing test designed with two variants, a clear hypothesis, target segment, and the success criteria that produce a decision at Day 30. Launch readout template structured.

Week 2, Day 14: Handoff & launch brief

A 60-minute session with your product and engineering team. Every deliverable walked through. Implementation confirmed before the feature ships. Day 30 review date set — when adoption data is read against the benchmark and the verdict is produced. Your team ships knowing exactly what Day 30 will measure.

WHAT YOU GET

Your team ships with a measurement system already in place — not one they’ll build after launch.

Week 1 · Foundation

AI Feature Instrumentation Spec

The complete event taxonomy for your AI feature, designed before launch and written so your engineering team can implement it directly. Every event, every property, every trigger condition defined — including the failure events most teams skip because they are uncomfortable to measure.

Exposure events: who sees the feature and in what context
Interaction events: first use, output accepted vs. dismissed, time to value
Value moment event: the downstream action that confirms the AI actually helped
Failure events: errors, timeouts, outputs not acted on — the early abandonment signals

Week 1 · Benchmark

Adoption Benchmark — 30 / 60 / 90-Day Thresholds

What “working” looks like at each milestone, defined before the feature ships rather than reverse-engineered when the data arrives. The benchmark answers the question your team will have at Day 30: is this number good, marginal, or telling us something is wrong?

Impression-to-first-use rate: is the feature being discovered by the users it’s built for?
First-use-to-value-moment rate: the core adoption test — are users getting what the feature promises?
Value-moment-to-repeat-use rate: is there a habit forming, or just a curiosity loop?
Clear thresholds for each stage at 30, 60, and 90 days

Week 2 · Dashboard

PostHog Adoption Dashboard

Built and connected to the event taxonomy before launch. The first day of data flows into a system that already knows what adoption looks like for this feature specifically — not a generic usage dashboard, one built around the four stages of your AI feature’s adoption funnel.

Funnel view: exposure through value moment through repeat use, in one chart
Cohort view: Day 1 adopters vs. Day 7+ adopters — are patterns diverging?
Failure signal tracking: which events indicate abandonment before the value moment
Benchmark overlay: actual performance read against the thresholds set at kickoff

Week 2 · Pricing

Pricing Test Design

A willingness-to-pay test designed and ready to run at launch — two variants, a hypothesis about which user segment will pay and at what tier, and the success criteria that produce a pricing decision at Day 30. The test your team needs to answer “charge for it, bundle it, or test a third option” with data rather than a meeting.

Pricing hypothesis grounded in who reaches the value moment, not who clicks the feature
Two test variants: specific framing, tier structure, and positioning for each
Target segment: the cohort most likely to reveal willingness-to-pay signal
Success criteria: what result at Day 30 triggers a decision vs. a follow-up test

Week 2 · Readout

Launch Readout Template

The 30-day report format for leadership and investors — structured to present what the adoption data shows, what the pricing test revealed, and what the next decision is. Not a dashboard export. A narrative that answers the questions your board will ask, pre-built so the Day 30 review practically writes itself.

Adoption funnel with benchmark performance overlaid at each stage
Value moment rate: the percentage of users the feature actually helped
Pricing test results and the decision they support
Next iteration recommendation based on what the data shows, not assumptions

What this looks like in practice: a writing assistant team defines their value moment as “suggestion accepted and user continued editing for at least 60 seconds.” Exposure events confirm who saw the suggestion. Value moment events confirm who used it. Repeat use events confirm who came back. By Day 30, they know whether the feature is helping the users it was built for — or just generating clicks from curious users who never return.

FIT CHECK

Teams with an AI feature shipping soon get the most from this sprint.

Good fit

B2B SaaS team with an AI feature shipping in the next 30–90 days and no measurement plan yet
AI feature already live but tracked with generic events — team cannot tell if it’s driving value or just being clicked
Founder or CPO who needs AI feature adoption data for an upcoming investor conversation
Team that needs to decide whether to charge for the AI feature, bundle it, or tier it — and wants data to make that call
Pre-Series B team without a dedicated data team to design the measurement framework

Not the right fit

Team still in early scoping — feature not yet in active development (come back in 4–6 weeks)
Company with a dedicated data team already handling AI feature instrumentation and dashboards
AI feature with no defined user outcome — if it’s unclear what “useful” looks like in practice, measurement cannot be designed
Teams expecting revenue projections or outcome guarantees from instrumentation alone

Not sure if this fits your situation? Book a 20-minute call. If the sprint isn’t the right move right now, we’ll say so — and point to what actually is.

Jake McMahon — ProductQuant

Jake McMahon

8+ years B2B SaaS · Behavioural Psychology + Big Data (Masters)

I run this sprint myself. The value moment definition, the event taxonomy, the dashboard build, the pricing test design — all of it. The most persistent gap I find in AI feature launches is that teams conflate the interaction metric with the adoption metric. They are not the same thing. A user who clicks the AI button three times in their first week and disappears has a completely different story than a user who reaches the value moment on Day 1 and comes back weekly. The instrumentation has to be designed to see both — and to tell them apart.

The pricing test design is where most teams underinvest. Knowing that users engage with the feature tells you nothing about whether they value it enough to pay for it. That answer requires a test designed before launch, with a clear hypothesis and a Day 30 decision threshold. Without the test, the pricing call gets made based on competitors and gut feel — then revisited six months later with no better data.

I won’t do this:

Define adoption as clicks or impressions without a value moment in the funnel
Set a benchmark without explaining what comparable AI feature adoption patterns look like and why
Design a pricing test without first identifying which user cohort has shown willingness-to-pay signals
Deliver instrumentation that requires re-instrumenting after launch because the events turned out to be measuring the wrong thing

Does it matter which AI stack we use?

No. The instrumentation framework is designed around what the user does with the AI output — not how the output was generated. Whether the feature runs on GPT-4, Claude, Gemini, a fine-tuned model, or a retrieval pipeline, the adoption funnel and event taxonomy are identical in structure. The stack affects your product engineering. It doesn’t change how user adoption is measured.

Teams Jake has worked with

PRICING

Fixed price. Measurement framework delivered before your launch date.

$2,997–$4,997

one-time · fixed price · scope confirmed on kickoff call

2-week sprint

Value moment definition — agreed in week 1 before any events are built
AI feature instrumentation spec (event taxonomy, properties, trigger conditions)
Adoption benchmark — 30 / 60 / 90-day thresholds set before launch
PostHog adoption dashboard built and connected before your feature ships
Pricing test design — 2 variants, hypothesis, success criteria
Launch readout template for leadership and investors
60-minute handoff with your product and engineering team
Day 30 review included — adoption data read against the benchmark

Book a 30-minute call →

Guarantee: At 30 days, your team has a clear answer: users are reaching value and returning, or the data shows exactly where they’re not — or the sprint cost is refunded in full.

Questions.

Anything else, book a call.

Book a call →

What if the feature is already live? +

The sprint works for live features. The first step is assessing what your current instrumentation captures and what it misses. If the value moment is not currently tracked, the gaps are documented, the instrumentation is patched, and a clean measurement window begins after implementation. You end up with a clear picture of what the data can now tell you — later than if the sprint had happened pre-launch, but better than an indefinite state of not knowing.

How do you define the value moment for our specific AI feature? +

The value moment is the specific action a user takes that confirms the AI output was useful to them. Not “clicked the AI button” — that is an interaction, not a value signal. For a writing assistant it might be “accepted the suggestion and continued editing.” For a search feature it might be “clicked a result from the AI-ranked list and stayed on that page.” For an analysis tool it might be “exported or shared the output.” The value moment is defined in the first working session with your product lead — before any instrumentation is built.

What does “adoption” actually mean for an AI feature? +

The distinction that matters more for AI features than for standard product features: a single use tells you about discoverability. A second use within 7 days tells you about perceived value. Regular use tells you about habit. The funnel separates these three stages deliberately so you can see where users are falling off and what kind of intervention each stage requires. “Tried it once” and “adopted it” have completely different next steps.

What if we use Amplitude or Mixpanel instead of PostHog? +

The instrumentation spec, adoption funnel design, and benchmark are platform-agnostic. The event taxonomy works in any analytics tool. The dashboard build is optimised for PostHog, but the structure translates directly to Amplitude or Mixpanel. If your team is on a different platform, raise it on the first call and the scope will be confirmed before the sprint starts.

What access do you need from our team? +

A working session in week 1 to understand the feature, the intended user outcome, and what “useful” looks like in practice — this is how the value moment is defined. Read access to your PostHog instance for the dashboard build. No write access to your product codebase is required — your engineering team implements the event spec from the documentation delivered in the sprint. Most teams give access via a guest login or read-only API key.

What happens at Day 30 after the sprint? +

The Day 30 review is included in the sprint scope. Adoption data is read against the benchmark, pricing test results are assessed, and the launch readout is produced. The verdict is clear: working, marginal, or not working — with a specific next action for each outcome. Beyond Day 30, your team runs the measurement independently using the dashboard and readout template built during the sprint.