AI Feature Strategy + Launch

Six weeks from now

VALIDATED

The scope is smaller and more defensible. You know which users are ready to adopt AI in this workflow and which need to see it prove itself first. Engineering is building the version users asked for — not the version that felt ambitious in a planning session.

PRICED

The pricing model covers compute at P90 usage. The internal debate about what to charge is over — you have an actual number backed by data from the people who would pay it. You don't end up in the situation where usage scales and the economics collapse.

ALIGNED

The board conversation changes from "when are we launching AI?" to "here's what we're launching and why this specific version." Engineering, product, and marketing are all working from the same one-page launch brief.

THE TRUST SYSTEM

Eight deliverables. The complete package from validation to launch brief.

Six weeks across two phases: validate first, then design. All deliverables, research data, and models owned by the client permanently.

T

Test

JTBD Alignment — build / reframe / defer decision

R

Research

Trust segmentation + WTP research

U

Understand

Compute cost stress test at P50/P90/P95

S

Structure

Pricing architecture + scope recommendation

T

Track

Instrumentation plan + launch brief

VALIDATE · WEEKS 1–3

AI Feature–JTBD Alignment Audit

Maps the proposed AI feature against what users actually hired the product to do — produces a clear build / reframe / defer call with evidence attached.

Engineering stops building capabilities users don't need
Build / reframe / defer decision backed by data
Scope conversation with engineering becomes easy
Avoids the 6-month post-launch "why isn't anyone using AI?" question

VALIDATE · WEEKS 1–3

AI Trust Segmentation Map

Segments your user base by trust threshold for AI in this specific workflow — not generic AI attitude, but this action, this stakes level, this context.

Exact users to launch to first — highest adoption likelihood
Everyone else sees the right version later
Override rates stay low, user confidence builds
Adoption curve goes up instead of flattening after week two

VALIDATE · WEEKS 1–3

Willingness-to-Pay Research Report

Van Westendorp or conjoint-lite across 3–4 pricing scenarios, run with real users who would actually pay for the feature.

Internal pricing debate ends — you have an actual number
The price point where it stops feeling obvious is identified
Backed by data from the people who would pay it
Scenarios modelled at multiple price points

DESIGN · WEEKS 4–6

Compute Cost + Margin Stress Test

Maps inference costs against pricing scenarios at P50, P90, and P95 usage — so you know whether the AI feature makes money before you scale it.

The board ROI conversation has a real answer
P95 usage scenario doesn't collapse the economics
Pricing and usage limits designed around the model
Built before engineering ships a line of code

DESIGN · WEEKS 4–6

AI Pricing Architecture Recommendation

A single defensible recommendation — bundle, flat add-on, credit, usage, hybrid, or outcome-based — with the evidence trail behind every choice.

One recommendation. One document.
The "how should we price AI?" debate resolved before launch
Evidence trail from WTP research + compute model
Framed for engineering, finance, and leadership

DESIGN · WEEKS 4–6

Feature Scope Recommendation

A scoped V1 spec with JTBD rationale for what's included, what's deferred, and what's cut — smaller and more defensible than the original roadmap item.

Engineering builds less and ships faster
Smaller scope has higher adoption — targets high-trust users
Every cut item has a rationale attached
Deferred items sequenced for V2 when trust is established

DESIGN · WEEKS 4–6

AI Feature Instrumentation Plan

Event taxonomy covering AI-specific signals — acceptance rate, override rate, time-to-first-value, regeneration requests — tracked from launch day, not retrofitted.

30 days post-launch, data answers whether AI is working
Override rate as a trust proxy — tracked from day one
Regeneration rate as a quality signal — not just clicks
No anecdotes and support tickets as the primary signal

DELIVERY · WEEK 6

AI Launch Brief

A 1–2 page alignment document: validated scope, pricing model, rollout sequence (trust segmentation → all users), trust UX requirements, and 60-day success criteria.

Engineering, product, and marketing working from the same document
No re-explaining the rationale in every handoff meeting
Success criteria defined before launch — not negotiated after
Rollout sequence from high-trust users to full release

Who it's for

$5M–$50M ARR, Series A–C. AI feature on the roadmap — scope defined by enthusiasm, not user research.

CPO / VP PRODUCT

AI is on the roadmap. Engineering is about to start.

$5M–$50M ARR · Series A–C

Why this fits

Under board or competitive pressure to ship AI. The scope came from a Slack thread and a competitor's changelog, not from user research. Nobody has done WTP research. Compute costs are estimated, not modelled. This engagement runs before engineering starts — so the scope, pricing, and instrumentation are right from the beginning.

What you leave with

JTBD audit with build / reframe / defer decision before engineering commits
Pricing architecture recommendation backed by WTP data
Compute model showing whether the economics hold at scale

Engineering builds the right scope. Pricing covers the compute. The board question is answered before launch.

POST-LAUNCH

Shipped AI. Adoption went flat after week two.

$10M–$50M ARR · Post-launch

Why this fits

The AI feature launched. Some users tried it. Most didn't adopt it. Override rates are high. Nobody knows if the problem is the feature itself, the workflow fit, or the trust level. The JTBD audit and trust segmentation diagnose which layer is the constraint — and the instrumentation plan sets up the measurement to confirm it going forward.

What you leave with

Trust segmentation showing which users are ready and which need more proof
JTBD alignment audit identifying whether the feature addresses the right job
Instrumentation plan so override rate and acceptance rate are tracked going forward

You know whether to fix the scope, the rollout sequence, or the trust UX — instead of guessing which lever to pull.

SERIES B / C

AI is the differentiation story. Board expects measurable impact.

$20M–$50M ARR · Series B–C

Why this fits

AI is on the investor narrative. The question isn't whether to ship AI — it's how to ship it in a way that demonstrates business impact, not just adoption. The compute cost model and pricing architecture ensure the AI features are accretive, not a gross margin drag. The instrumentation plan gives the board the data they're expecting.

What you leave with

Compute model and pricing architecture that makes the AI economics defensible
Instrumentation plan tracking AI-specific signals from day one
Launch brief that frames AI ROI in board-ready language

The AI story changes from "we're building AI features" to "here's what we shipped, why it's the right scope, and the data showing it's working."

Six-week timeline

Three weeks to validate. Three weeks to design.

Weeks 1–3

Validate phase. JTBD alignment audit. User interviews (8–12). Trust segmentation research across your user base. WTP research conducted across 3–4 pricing scenarios.

Weeks 4–6

Design phase. Compute cost model built at P50/P90/P95. Pricing architecture recommendation produced. Scope recommendation finalised with JTBD rationale. Instrumentation plan documented. AI Launch Brief assembled and reviewed.

How it works

Four steps from first call to a validated AI launch.

01

30-minute call

We review your proposed AI feature, current analytics setup, roadmap timeline, and compute cost awareness. You leave knowing whether the data and user access are there for this engagement to produce reliable output. No pitch. No deck.

02

2-page proposal

Specific scope: interview count, WTP methodology, compute model inputs, deliverable list. Price confirmed. If the scope or timeline doesn't align with your roadmap pressure, we'll say so before you sign. Nothing ambiguous.

03

The 6-week engagement

Two phases: Weeks 1–3 validate (JTBD audit, user interviews, trust segmentation, WTP research) → Weeks 4–6 design (compute model, pricing architecture, scope recommendation, instrumentation plan, launch brief). Shared review at the phase gate between weeks 3 and 4.

04

Full handover

All 8 deliverables delivered. All research data, interview recordings, and models owned by the client permanently. AI Launch Brief ready for the team. Fixed price. Fixed scope. No ongoing dependency.

Value stack

What this would cost to build separately.

Standalone market rates for each component.

JTBD alignment audit (consultant day rate)~$2,500

AI trust segmentation research~$2,000

WTP research (Van Westendorp / conjoint)~$3,000

Compute cost + margin stress test~$1,500

AI pricing architecture recommendation~$2,500

Feature scope recommendation~$1,500

AI feature instrumentation plan~$2,000

AI launch brief~$1,500

Standalone total~$16,500

AI Feature Strategy + Launch$12,000–$16,000

Pricing

Fixed scope. One-time fee.

$12,000–$16,000

One-time. Varies with product complexity, existing analytics maturity, and research scope.

AI Feature–JTBD Alignment Audit
AI Trust Segmentation Map
Willingness-to-Pay Research Report
Compute Cost + Margin Stress Test (P50/P90/P95)
AI Pricing Architecture Recommendation
Feature Scope Recommendation
AI Feature Instrumentation Plan
AI Launch Brief

Book a 30-minute call

All 8 deliverables, all research data, all models owned permanently by the client. Fixed price. Fixed scope. No ongoing dependency.

WHO'S DOING THE WORK

Jake McMahon · Founder, ProductQuant

Jake McMahon

8+ years building growth systems inside B2B SaaS · Bachelor's in Behavioural Psychology · Master's in Big Data

Eight years as a product leader inside B2B SaaS companies — product manager, growth lead, head of product, from seed-stage to $80M ARR. He kept watching smart teams make the same mistake: good tools, real talent, no system connecting any of it.

AI feature work built on JTBD research and real compute cost modelling — not AI enthusiasm and a competitor's changelog. ProductQuant is what he'd hire if he were still an operator. There's no team of junior analysts.

What he won't do:

Promise revenue numbers he can't verify
Hand you a strategy deck and disappear
Recommend work you don't need
Build something that only works if you keep paying him

"Could our growth PM do this if we gave them time?"

One PM cannot simultaneously run 8–12 user interviews using a rigorous JTBD methodology, build a compute cost model calibrated to your specific inference stack, design the pricing architecture, and produce the instrumentation spec — in 6 weeks while also managing the roadmap. Beyond bandwidth, these are four different specialisations. The TRUST System is designed for exactly this: six weeks of dedicated focus across validate and design phases, in the sequence that makes the compute model inform the pricing and the pricing inform the scope. An internal team building from scratch takes 12–18 months to reach the same output quality.

Teams Jake has worked with

Common questions

Frequently asked.

What if engineering has already started building?

The sooner this runs, the less expensive the course-correction. If engineering is already in progress, the JTBD audit and trust segmentation can still redirect the scope before it ships. The compute cost model and pricing architecture are independent of the build and can run in parallel. The most expensive outcome is shipping the wrong scope to the wrong users with the wrong pricing model — which this engagement prevents, whenever it runs.

How is this different from a pricing consultant?

Pricing consultants (Monetizely, Valueships, Pricing I/O) design pricing models. None of them bundle: a JTBD alignment audit to check if the feature solves the right job, a trust segmentation to identify which users will actually adopt it, or a compute cost stress test to confirm the pricing model holds at scale. This is a product strategy and launch engagement where pricing is one of eight outputs — not the sole deliverable.

What's the traffic or user minimum?

No traffic minimum — this engagement is about validation before launch, not experimentation after it. For the WTP research and trust segmentation, we need access to 15–25 current users for interviews and surveys. For the JTBD audit, we need access to your existing user research or the ability to conduct 8–12 user interviews as part of the engagement. We'll confirm what's available in the first call.

What does "trust segmentation" mean in practice?

Different users have different tolerance for AI taking action in a workflow — depending on the stakes of the action, their familiarity with the AI's output quality, and their prior experience with AI in similar tools. Trust segmentation maps your user base against these dimensions for this specific AI feature. The output tells you which segment to launch to first (highest trust, clearest JTBD fit), what the phased rollout sequence looks like, and what trust UX requirements the launch needs to hit override rates below a threshold.

What does the compute stress test actually model?

We model inference costs at P50, P90, and P95 usage against each pricing scenario from the WTP research. P50 is your median user. P90 is your heavy user. P95 is your extreme outlier. For most AI features, the economics hold at P50 and P90 but collapse at P95 if the pricing doesn't include usage limits or compute cost pass-through. The model tells you before you scale which pricing model covers the compute at each usage percentile — and what limits to put in place if they don't.

Our growth PM says they can figure this out internally. Should they?

Maybe — if they can simultaneously run 8–12 user interviews, build a compute cost model calibrated to your specific inference stack and usage patterns, design the pricing architecture, and produce the instrumentation spec in 6 weeks while also managing the roadmap. In practice, one PM cannot run the research-heavy validate phase and the modelling-heavy design phase at that quality level while also executing normal roadmap responsibilities. This engagement provides dedicated focus for 6 weeks, which is typically the bottleneck.

Ready to ship AI your users will actually adopt?

30 minutes. You'll leave knowing whether the scope, pricing, and timeline make sense for this engagement.

Book a 30-minute call

Before you ship AI, know if it'll work.

Eight deliverables. The complete package from validation to launch brief.

$5M–$50M ARR, Series A–C. AI feature on the roadmap — scope defined by enthusiasm, not user research.

Three weeks to validate. Three weeks to design.

Four steps from first call to a validated AI launch.

What this would cost to build separately.

Fixed scope. One-time fee.

Frequently asked.

Ready to ship AI your users will actually adopt?