The biggest mistake we made building an AI sales product was optimizing for output

When we started building Komo, we assumed sales teams wanted AI to produce more work: more research, more emails, more follow-ups.

We were partly wrong.

Producing more was easy. Deciding what was worth doing was the real problem.

A beautifully written email is still spam when it is sent to the wrong company at the wrong time. We eventually spent more time on account signals, evidence, and approval controls than on writing.

The principle we now use is simple:

AI prepares the work.
Evidence explains the recommendation.
A person makes the consequential decision.

It slowed down the flashy parts of the product, but made the output far more trustworthy.

For other founders building AI products: what assumption did you start with that turned out to be completely wrong?