Positioning Statement Workshop — by an AI council
Nail what you are, who it is for, and why it wins — à la April Dunford. Four rival AIs debate it and hand back: A sharp positioning statement: category, best-fit buyer, alternatives, unique value.
A sharp positioning statement: category, best-fit buyer, alternatives, unique value.
Signed off by an independent Final QA audit — with a “verify before you rely on this” list, never a black box.
Why this team
Positioning drifts into flattering vagueness the moment everyone in the room shares the founder’s context — so this council is built from outsiders. The Positioning Expert supplies the discipline of category, alternative and differentiated value; the Customer Psychologist and End-User Advocate test every phrase against what a cold prospect actually hears; the Copywriter forces it into words a human would say; and the Contrarian attacks the category choice itself — usually the real mistake.
Each seat runs on a different frontier model — GPT, Claude, Gemini, Grok — so it’s a genuine cross-model review, not one AI in costumes. How a run works →
What the debate sounds like
An illustrative excerpt — A team whose product monitors ML models in production calls itself an “AI observability platform”, and enterprise prospects keep confusing it with the general logging tools they already own.
The confusion is the diagnosis: “observability platform” invites comparison with the giant logging incumbents, and against them you lose on breadth by default. The buyer’s real alternative is not another platform — it is a data scientist checking dashboards manually on Monday mornings. Position against that, and the value is obvious instead of comparative.
Or go the other way entirely: own a category of one. “Model failure insurance” describes the outcome, not the mechanism. Yes, it will alienate platform-procurement buyers — and that alienation is a feature, because those buyers were never closing anyway. The question is which repulsion you can afford.
Careful: “insurance” triggers scepticism in technical buyers — it reads as marketing, and this buyer distrusts marketing. The phrase that lands with an ML engineer names their fear precisely: the model degraded for three weeks and nobody noticed. Whatever the category, the words must sound like their internal monologue.
Questions
What inputs make a positioning debate actually productive?
Honesty about alternatives. The brief asks what customers use instead — including “a spreadsheet” or “doing nothing” — because positioning against the real alternative is the entire method. Bring your current description, your best-fit customers, and the deals you lost with the stated reason.
We serve two very different segments — can one statement cover both?
The debate will almost certainly tell you no, and make you choose the best-fit one — that is the April Dunford discipline the brief encodes. You get a positioning statement for the segment where you win most, and an honest note on what the second segment costs you while you serve both.
How do I know the output is positioning and not just a slogan?
The deliverable is structural: the category to compete in, the best-fit buyer, the alternatives you displace, and the differentiated value — plus two or three headline candidates that express it. Slogans are the last five percent; if the structure is wrong, no headline saves it.
Your material is used only to run your review — never to train public models. Encrypted in transit and at rest. Security & privacy →
Want full control — pick your own minds, set the depth? Open the full council →