Category design is the move that takes you out of the feature war. Instead of being one more AI tool a buyer compares on a spec sheet, you define the problem worth solving and make your approach the obvious answer – the difference between being a line item and being the budget.
The Problem
Model commoditization turns every feature claim into a race to zero
When the underlying capability is available to every competitor through the same foundation models and API access, competing on 'smarter,' 'faster,' or 'more accurate' becomes a treadmill that resets with every model release. Buyers learn to wait for the next version or to negotiate on price because they see the capability as interchangeable. Without a category that frames why your approach to a specific problem matters, you are stuck defending a feature lead that evaporates. The escape is not a better feature – it is a different conversation about the problem itself.
Buyers do not have a budget line for what you sell yet
Genuinely new AI products often solve a problem the buyer has never explicitly bought a solution for, which means there is no budget, no owner, and no urgency assigned to it. Sales teams burn cycles educating buyers from scratch on why the problem even deserves a line item, and deals die not because the product lost a bake-off but because the buyer never prioritized the problem. This is a category problem, not a sales problem – the market has not been taught to value what you do. Category design creates the demand and the budget line before the feature comparison ever happens.
The market shifts faster than your positioning can keep up
The AI landscape moves in weeks – new models, new entrants, and new buyer expectations reshape the competitive frame constantly. A position pegged to today's capability gap is obsolete by the next funding announcement or model drop, leaving the company in a permanent state of repositioning. Chasing the moving frame keeps you reactive and indistinguishable from the next wave of entrants. A well-designed category gives you a stable frame you own, so you set the terms of the conversation instead of reacting to everyone else's.
Investors and buyers cannot tell if you are a feature, a product, or a company
When everyone describes themselves as AI-powered, the market cannot place you – are you a thin wrapper, a point tool that a platform will absorb, or a company defining a new space? That ambiguity caps valuation, slows enterprise deals where buyers fear betting on something that gets commoditized, and makes it hard to recruit. The fear of being a feature inside someone else's roadmap is the quiet objection in every late-stage AI deal. Category design answers it by establishing that you own a problem space, not a feature.
How We Help
We start by finding the problem worth owning, because category design lives or dies on the problem you choose to make important, not on the features you ship. In the first phase we map the market frame buyers currently use, where your product is being lumped in with commoditizing AI tools, and what problem you solve that the market has not yet learned to value or budget for.
From there we design the category. We define the problem in terms the buyer feels, name the old way that your category makes obsolete, and frame your approach as the obvious answer rather than one option on a spec sheet. We build this to be durable against model commoditization and a fast-moving market – anchored to a problem and a point of view that does not reset when a competitor ships a new model.
Then we build the assets that move the market. We develop the point-of-view narrative, the evidence that the old way is failing, and the messaging that teaches buyers to value the problem before they ever compare features. We translate the category into the language sales uses to create urgency, the story that gives investors a frame bigger than a feature, and the marketing that seeds the category in the market. This connects to your marketing so the category is something you propagate, not just a deck.
Measurement for category design is about whether the market adopts your frame, not whether a campaign got clicks. We watch whether buyers start describing the problem the way you framed it, whether deals shift from feature comparisons to problem conversations, and whether analysts and investors begin using your language. The work succeeds when prospects come to you already believing the problem matters and your approach is the answer – not when you win another feature bake-off against an interchangeable competitor.
What we deliver
- A defined category anchored to a problem worth owning, not a feature that commoditizes
- A point-of-view narrative that names the old way and frames your approach as the answer
- Evidence and messaging that teach buyers to value and budget for the problem
- A market-frame story for investors that positions you as a company, not a feature
- A category seeding plan that turns the frame into demand through marketing and sales
In AI, features commoditize the moment the next model ships, but a problem you have taught the market to care about does not. Category design is the only positioning move that gets stronger as the technology gets cheaper – because it competes on the problem, not the capability.
Our Methodology
Our category design engagement treats model commoditization and the fast-shifting market as the reason category matters rather than an obstacle to it. The first phase maps the frame buyers use today, finds where your product is being lumped in with interchangeable AI tools, and identifies the problem worth owning – one the market has not yet learned to value or fund.
The second phase designs the category: the problem definition, the old way it makes obsolete, the point-of-view narrative, and the evidence that teaches buyers to care before they compare features. We then build the assets that propagate the frame across sales, marketing, and the investor story, and we pressure-test the category against the speed of the market so it holds up past the next model release.
What makes this different from a brand or analyst-relations exercise is that we design the category to escape the feature war that model commoditization forces on AI companies. A standard agency sharpens how you describe your features. We change the conversation entirely – from which AI tool is better to whether the buyer has even prioritized the problem – because in a category where capability is increasingly free, owning the problem is the only defensible position.
How We Work
Initial engagements typically run 3 to 5 months because designing a category – finding the problem worth owning, building the point of view, and seeding it – is foundational work that takes more than a positioning sprint. The first 30 days map the market frame, find where you are commoditizing, and identify the problem worth owning. The middle phase designs the category and the point-of-view narrative. The final phase builds the seeding assets and the sales, marketing, and investor language.
Our team includes a category strategist who owns the problem definition and the point of view, a narrative writer who builds the story and evidence, and a researcher who maps the market frame and tests the category against buyers. From your side we need access to leadership for the vision, sales for where deals stall on problem urgency, and a few customers to validate that the problem lands. We do not invent a category in a vacuum and hope the market follows.
The cadence is working sessions through the build – market mapping and problem selection up front, category and narrative reviews as the frame develops, and a finalization review where we walk the full category platform and seeding plan. Because this is a foundational build, the deliverable is a complete category design and the assets to propagate it, with the option to extend into ongoing category seeding through content, marketing, and analyst engagement as the frame takes hold in the market.
- A category frame that escapes the AI feature war and the race to zero
- Buyers who value and budget for the problem before they compare features
- An investor story that positions you as a company defining a space, not a feature
- A market frame durable enough to survive the next model release
If your ai / machine learning company needs category design leadership, we should talk.
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Frequently asked questions
How much does a category design engagement cost for an AI/ML company?
A defined build typically runs in the $40K-$90K range depending on how novel the category is, how much market research it requires, and how much seeding material comes out of it. That is comparable to a senior strategy engagement but aimed at owning a market frame rather than polishing a brand.
How long before we see results from a category design engagement?
A defined build runs 3 to 5 months from market mapping to a finalized category and seeding plan, with the problem definition and point of view taking shape in the first two months. Category adoption itself is a longer game – the market starts using your frame over quarters, not weeks – but you see early signal when sales conversations shift from feature comparisons to problem urgency.
How does the category design team integrate with our leadership and sales staff?
We work with leadership to ground the category in the real vision and with sales to see exactly where deals die on problem urgency rather than feature loss. That input is what keeps the category honest – a category nobody on the team believes in will not propagate.
Why is category design more important for AI companies than for other software?
AI capability is commoditizing faster than almost any technology, because competitors can reach similar capability through the same foundation models, so competing on features is a race to zero. A category anchored to a problem you have taught the market to value gets stronger as the underlying technology gets cheaper, while a feature lead gets weaker.
How do you measure the value of a category design engagement?
We track whether the market adopts your frame – whether buyers start describing the problem the way you defined it, whether deals shift from feature bake-offs to problem conversations, and whether analysts and investors pick up your language. The headline is whether prospects arrive already believing the problem matters and your approach is the answer.
What type of AI/ML company is the right fit for category design?
Companies stuck in feature comparisons against interchangeable AI tools, or selling a genuinely new capability the market has no budget line for, are the strongest fit. So are AI companies whose valuation or enterprise deals stall because the market cannot tell whether they are a feature or a company.
