Our New AI Is Faster! Compared to What?
Lots of prompts. Plenty of enthusiasm. What actually improved?
Imagine buying a new bathroom scale, stepping on it, and announcing that your diet is working.
But the scale has Bluetooth. So there’s that.
That is what an AI success meeting can sound like. People are logging in. Prompts are flying. The dashboard looks terrific. Then somebody asks what actually improved, and the room develops a sudden interest in the coffee.
AI success is hard to measure when the organization never measured the work in the first place.
88% of organizations now regularly use AI. 38% track employee-productivity changes. 41% of AI investments get ROI measured.
Those are different measures. They are not a calculation of how many AI projects failed. They do show why “everybody is using it” is a poor answer to “what are we getting?”
Banks already understand this when a loan is involved. An application comes in. Documents get collected. Somebody underwrites it. Eventually, it gets booked—or it doesn’t. Nobody would call the lending operation a success because employees opened the loan system a lot.
Why give AI that pass?
Pick one piece of real work. Before introducing AI, record how long it takes, what it costs, and how often somebody has to fix it. Afterward, measure the same work again. Then follow it downstream.
Did the loan move through faster? Did the next team inherit more mistakes? Did the customer get an answer sooner, or did we just move the waiting room?
This doesn’t require a grand measurement initiative with a steering committee and commemorative mugs. It requires an owner and a before number.
Ask your next AI project lead a plain question: “What will we compare this against, and where will the improvement show up?”
If nobody can answer, the invoice may be the most accurately measured thing in the project.
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