A good AI MVP validates a specific assumption — usually 'will people trust and use an AI-driven output for this task' — with the smallest system that can honestly test that assumption. It is not a demo of everything the technology could theoretically do.
The AI MVPs that work well share a few traits: a narrow, well-defined use case; a clear way to measure whether the AI's output was actually useful (not just whether it ran); and a fallback path for when the model gets it wrong, because in an MVP, it will.
A common failure mode is over-investing in model sophistication before validating that users want the output at all. It's usually faster and cheaper to start with a simpler model — or even a well-designed rules-based system with an LLM assist — and prove demand before optimizing accuracy.
The second common failure mode is under-investing in the surrounding product: an impressive model wrapped in a confusing interface, with no clear feedback loop, will still fail user testing.
Our approach: scope the MVP around the riskiest assumption, build the smallest version that tests it honestly, instrument it so you can measure real usage, and treat the first version as something you'll very likely rebuild once you know more.