How to Run an AI Proof of Concept Without Wasting Months
A focused approach to testing whether AI can solve your problem, with clear success criteria and a decision at the end.
Many AI initiatives stall in endless experimentation. A well-run proof of concept answers one question quickly: can AI solve this specific problem well enough to be worth building properly?
Pick one valuable problem
Choose a task that is frequent, time-consuming, and has a clear definition of a good result. Summarising support tickets, extracting data from documents, or answering internal policy questions are good candidates.
Define success before you start
- What accuracy or quality level is acceptable?
- What time or cost saving would justify building it?
- What would make you stop?
Writing these down prevents a proof of concept from drifting into an open-ended research project.
Use real data
Test with real, representative examples, including messy and difficult ones. Demos built on hand-picked inputs give a false sense of quality.
Build an evaluation set
Collect inputs with expected outputs and measure results objectively. This also becomes the foundation for testing the production version later.
Keep it time-boxed
A focused proof of concept is usually measured in weeks, not months. At the end, make a clear decision: build it properly, adjust the approach, or stop.
Think about production from the start
- Where will the data come from in production?
- What privacy and security requirements apply?
- How will people review or correct the output?
- What will it cost to run at full volume?
A proof of concept that ends with a clear "no" is a success. It saved you from a costly mistake.
MOKRIN runs focused AI proofs of concept with defined success criteria and real data, then gives you an honest recommendation and a written plan if it is worth building.
