Most AI projects do not fail because the model is not powerful enough.
They fail before that.
They fail because the team cannot clearly answer who the user is, what the workflow is, what data matters, what output is useful, and how success will be measured.
The common failure pattern
A team starts with: “We should add AI.”
But the better starting point is:
- what is the user trying to do?
- where does the current workflow break?
- what information does the user need?
- what decision or action follows the AI output?
- what does a good answer look like?
- what happens if the AI is wrong?
The product standard
AI should make a workflow better, not just make a demo more exciting.
That means the MVP should be scoped around:
- one clear user
- one painful workflow
- one measurable outcome
- one demo path
- one fallback path
- one evaluation loop
The real work
The model matters. But the system around the model matters just as much: UX, data, retrieval, prompts, evaluation, handoff, and trust.
That is why I approach AI products as full systems, not isolated features.