Most failed AI projects didn’t fail at the model. They failed at the problem statement — a use case that was too vague, too broad, or chosen because it was interesting rather than because it mattered. Getting the problem right is the highest-leverage decision you make, and it happens before a single line of code.
Start with the workflow, not the model
A good problem statement describes a job, not a technology. “Use an LLM to improve operations” is not a problem statement; it is a wish. “Find and qualify relevant tenders so the BD team gets a ranked shortlist instead of a search” is — it names the work, the actor, and the outcome.
A test for a good problem statement
- Specific job: can you name the exact task and who does it today?
- Measurable outcome: what number moves — time, cost, error rate, coverage?
- Frequent and painful: does it happen often enough that automating it compounds?
- Action, not answer: does the agent do the task, or just describe it?
- Bounded: is the first version narrow enough to ship in weeks?
Common missteps
- Boiling the ocean — a problem so broad no version of it can ship.
- Solving for the demo — impressive once, useless on the hundredth messy input.
- Automating a step nobody cares about while the real bottleneck sits untouched.
- Skipping the measurement, so you can never prove it worked.
Narrow, real, then widen
The teams that get value fastest pick one workflow that is narrow, real, and live in weeks — then widen from a thing that already works. The problem statement is what keeps that first version honest.
“Pick the problem that is small enough to ship and painful enough to matter. Everything good follows from that.”
KnackLabs
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