I think the reason is because it depends what impact metrics you want to measure. "Usefulness" is in the eye of the beholder. You have to decide what metric you consider "useful".
If it's company profit for example, maybe the data shows it's not yet useful and not having impact on profit.
If it's the level of concentration needed by engineers to code, then you probably can see that metric having improved as less mental effort is needed to accomplish the same thing. If that's the impact you care about, you can consider it "useful".
I think the reason is because it depends what impact metrics you want to measure. "Usefulness" is in the eye of the beholder. You have to decide what metric you consider "useful".
If it's company profit for example, maybe the data shows it's not yet useful and not having impact on profit.
If it's the level of concentration needed by engineers to code, then you probably can see that metric having improved as less mental effort is needed to accomplish the same thing. If that's the impact you care about, you can consider it "useful".
Etc.