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Discovery & Strategy

AI Product Strategy

Ship AI your customers will pay for, not just notice.

3 wks
To a costed product plan
Unit-level
Cost-to-serve modelling
1 page
Positioning your sales team can use

The problem we solve

Adding AI to a commercial product changes positioning, pricing, support and margin at the same time. We work with product and commercial leaders to decide what the feature is worth, what it costs to serve, and how it is packaged before engineering commits.

What the work includes

Customer and job discovery

Interviews and usage analysis that separate the AI features customers ask for from the ones they will renew for.

Packaging and pricing

Seat, usage, credit and outcome-based models tested against willingness to pay and inference cost.

Cost-to-serve modelling

Token, retrieval and support costs per account so gross margin is a decision input, not a year-end surprise.

Competitive positioning

A clear read on what rivals ship, what they claim, and where a defensible difference actually exists.

Release roadmap

Sequenced releases with entry criteria, evaluation targets and the trust signals each one needs.

How the engagement runs

A sequence you can plan a quarter around.

  1. 01

    Listen

    Customer interviews, usage data review and commercial baseline.

  2. 02

    Model

    Pricing options, cost-to-serve scenarios and margin sensitivity.

  3. 03

    Commit

    Roadmap, positioning and a go-to-market plan signed off by product and sales.

Common questions

Before you commit.

Do you work with our existing product team?
Always. We bring the AI economics and evaluation lens; your team keeps ownership of the roadmap.
Can you help price an AI feature already in market?
Yes — repricing based on observed usage and cost data is usually faster and lower risk than pricing blind.

Talk through ai product strategy.

A 45-minute briefing with the people who would run the work — scope, timeline and a straight answer on whether it is the right next step.

Book a briefing