Run a pilot and measure results
Start with a limited scope and acceptance criteria set before testing. Compare with the baseline and also record errors, review cost, exceptions and effects on people. Time saved at one step may be lost at another.
Choose a representative set of cases, including difficult situations. Decide in advance when to continue, correct or stop. A successful demonstration does not prove production readiness, financial return or reliability at scale.
In a classification pilot, measure errors by category and the time needed to correct them. Compare the whole process with the previous one, not just the speed of the model’s first answer.
Define success and stopping conditions before you see the results.
Try it yourself
Create a pilot brief: problem, current process, authorised data, owner, metric, test cases, budget and stopping condition. Include a non-AI alternative and a plan for returning to the previous process.