Stop Shipping Features. Start Shipping Experiments.

Most AI teams run like traditional software teams. Quarterly roadmap, feature list, ship dates, check the box.

This doesn't work for AI.

You can't promise a feature by a date when you don't know if the approach works. The model might not be good enough. The edge cases might eat your timeline. What looked promising Tuesday falls apart under real data Wednesday.

What actually works: commit to a rate of experimentation, not a list of deliverables. Run the test. Score it. If it worked, push further. If it didn't, drop it and try the next thing.

But this only works if you can score experiments fast. That means building evaluation infrastructure before you need it. Specific, measurable tests tied to what your users care about. Not generic dashboards. Not vibes in a review meeting.

This isn't a novel idea. Bezos said Amazon's success is a function of how many experiments they run per year, per month, per week, per day. Steven Bartlett hired a Head of Failure & Experimentation at DOAC whose sole metric is how many experiments the team runs. Hamel Husain's Field Guide lays out the evidence across 30+ AI teams.

The common thread: none of these people optimize for certainty. They optimize for learning speed. And they all built the measurement layer before they started moving fast.

How many experiments did your team run last month? If you don't have a number, start there.