Proof of Concept (PoC)

A proof of concept is a stage where you first test, on a small scale, whether a new approach actually works. It's similar to opening a pop-up shop to gauge customer reaction before launching a full-scale restaurant. Just as you open the real restaurant if the pop-up succeeds, a company sets target metrics and first runs an AI feature on a limited scope before applying it company-wide.

For example, in the article, Mobileye had AI handle repetitive support tasks, but first set concrete targets—95% accuracy and response times under 2 minutes—and checked whether the system met them. Only after passing this test did the company formally roll the approach out across its actual operations.

The reason for setting target numbers in advance and validating on a small scale is that if you launch something big and it doesn't work, it's hard to recover the time and money spent. A proof of concept is essentially a safety mechanism for cheaply checking, up front, whether an idea really works.

The article reports that Mobileye "went through a proof of concept (PoC) targeting 95% accuracy and response times under 2 minutes, then moved to full deployment." A common misunderstanding here is that a proof of concept is not the finished product—it's a small-scale trial stage used to make a judgment. Only after passing the test does it move on to full deployment across actual operations.

If you want to introduce a new AI feature to your company or team, you can design a small test using the following steps.

1. Write down the problem you're trying to solve in one sentence. (e.g., reduce response time for repetitive inquiries)
2. Set numerical success criteria. (e.g., 90%+ accuracy, processing time under 3 minutes)
3. Apply it first to just a part of the workflow or a single team, not the whole organization.
4. Measure whether the target numbers were actually achieved, and expand the scope if it passes.