Why AI Pilots Fail to Reach Production

Nobody measured the baseline

A pilot that cannot say what the task cost before it started cannot demonstrate that it is cheaper now. This is the single most common reason a promising pilot dies quietly at budget time.

The running cost was never counted

Inference is the visible cost and usually the smaller one. On the largest use case in our published example, the model accounted for 35% of the cost per task and the humans around it — review, exception handling, QA sampling — accounted for 57%. A pilot costed on tokens alone will look profitable and will not be.

Security arrived at week ten

Data residency, client consent, audit trails and access review are not obstacles that appear at the end. They are constraints that should shape which use case is chosen in the first place.

The accuracy target was set by ambition

Higher accuracy lowers review cost and raises engineering cost, and those two lines cross. In our example, net saving peaked at 78% straight-through. Pushing to 93% turned a use case earning ₹8.6 lakh a month into one losing ₹4.2 lakh. Every vendor slide will show accuracy improving. None of them will show you the point at which it stops paying.