Measure the Baseline Before You Buy the AI

The most common reason an AI project fails to demonstrate value is not model quality. It is that no baseline was captured, so nobody can prove anything changed.

Before building, measure three things: how long the task currently takes, how often it is done, and the current error rate. Those three numbers convert directly into an expected return and a threshold for acceptable accuracy.

The accuracy threshold matters more than people expect. A system that is right 85 percent of the time can be excellent for drafting and unacceptable for anything that ships unreviewed. The same accuracy number is a success or a failure depending on the review step you design around it.

Then measure cost per task including review time. Automation that halves processing time but doubles review time has achieved nothing except moving the work.

Finally, agree what happens when the system is wrong. Every deployment needs a defined failure path. Without one, you have not automated a process, you have introduced an unmonitored one.