After defining an AI task, enterprises should build a benchmark that specifies what good and bad performance look like for their use case.

“And benchmark is a proxy for being able to make a lot of those iterations very quickly in a more scientifically redressed manner. That has driven progress of machine learning all this duration and, and, and the first thing enterprises have to sit down and do is think about what does it mean, what does good look like for their use case, and more importantly, what does bad look like for their use case.”