Reality
Most AI MVPs fail because they built five capabilities instead of nailing one.
After design discovery, most AI-native teams have clarity on users, problems, and use cases. What they don’t have, a framework for choosing which AI capability to build first.
The instinct is to demonstrate everything the model can do. The research says the opposite. 65–75% of AI MVPs fail to progress beyond early validation, and the most common reason is building too many AI capabilities at once. Traditional prioritisation methods like RICE, MoSCoW, and stack ranking weren’t designed for AI products, they ignore model constraints and the gap between capability.
Users expect the AI to do the right thing, not just the next thing.
Users expect prioritisation to feel invisible, not effortful.
Users expect the product to earn trust before asking for more.
AI products across industries.
An HRMS evaluation employees avoided. We made it a conversation.
A performance review that feels like talking to a thoughtful manager. Not a compliance exercise.
Our Crafts
Every use case scored on four dimensions. No gut feel. No loudest voice wins.
Use Case Impact & Desirability
Which use cases solve a real problem users care about? We score each on user needs and the gap between today and what AI makes possible.
AI Role Definition
For each use case, what does the AI do, and what does the user do? Not every task should be automated. We define the boundary, use case by use case.
Model-to-Experience Mapping
What the model can do vs. what the experience should promise. This gap is where trust breaks. We map capabilities and failure modes against what the user will actually see.
MVP Scope Definition
Which use cases, which capabilities, which quality bar define v1? We draw the line, what ships, what waits, what gets cut. Every decision is documented and defensible.
Not sure which AI capability to build first?
Start with a conversation. We’ll tell you within 24 hours whether use case prioritisation is the right next step.
Our Process
Inside the two weeks.
We take every use case from discovery, and any the team has added since, and score them across impact, data readiness, model feasibility, trust complexity, and delivery effort. No gut feel. No loudest-voice-wins.
For the top-ranked use cases, we define the division of work: what the AI does, what the user does, where the handoffs happen. This is the delegation model that feeds directly into design.
We work with your technical team to map what the model can actually deliver, confidence levels, failure modes, latency, against what the experience needs to feel like. Where there’s a gap, we flag it before it becomes a trust problem in production.
We define the MVP scope: which use cases ship first, which AI capabilities they include, and what quality bar they need to meet. Every cut is documented with reasoning your team and investors can stand behind.
Deliverables
A scored roadmap, a clear MVP scope, and reasoning your investors can stand behind.
Prioritised use case matrix
Every use case scored on impact, feasibility, data readiness, and trust complexity
AI role maps
What the AI does vs. what the user does, per use case
Model-to-experience mapping
Where the model’s capability meets the experience promise, and where it doesn’t
Build-order roadmap
Sequenced for maximum learning with minimum risk
Investor-ready rationale
Defensible reasoning for every prioritisation decision
Two types of founders come to us for discovery.
Your model can do a lot. Your runway can’t do all of it. You need clarity on which use case to nail first, and confidence that the AI can actually deliver the experience you’re promising users.
You’ve identified multiple AI opportunities. Leadership wants a roadmap, not a wish list. You need a framework that scores each opportunity against real constraints, not just excitement.
If users aren’t finishing the conversation, the design needs to change.
Start with a free trial. One use case, prototyped end to end —
failure states included. No commitment.
Your questions, answered.
AI Opportunity Mapping is for existing products that need to find where AI fits. Use Case Prioritisation is for AI-native products where AI is already the core, the question is which capabilities to build first, and how far to take them.
Maybe. Most teams we work with arrive with strong instincts, and 2-3 assumptions that don’t survive contact with model constraints or user trust requirements. This engagement validates, challenges, or redirects. If your instincts are right, you’ll know for sure. If they’re not, you’ll find out before engineering starts.
Yes. Model-to-experience mapping requires understanding what the AI can actually do, confidence levels, failure modes, data dependencies. We work alongside your technical team to ground every prioritisation decision in reality.
Standard MVP scoping asks “what’s the smallest thing we can ship?” AI use case prioritisation asks a harder question: “what’s the smallest thing we can ship where the AI is reliable enough to earn trust?” Trust complexity and model constraints change the math.
One to two weeks, depending on the number of use cases. Typically follows Design Discovery, or runs alongside it.
Your team has a clear, scored roadmap and an MVP scope document. If you continue with us, we move into AI Design System Build and the build phase. If you take it in-house, every decision is documented and defensible.
Connect with us
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