Pilot projects are usually a small sample that demonstrated that a protocol can be achieved, not a meaningful set of data to test a hypothesis. So no, it is probably a waste of time.
But this explains the value of scientific meetings and poster sessions where I can just tell this random tidbit to interested people without the burden of peer review.
An interesting recent development I've observed once was a tweet describing the fact that a dye (Texas red) labels brain vasculature even when injected subcutaneously. This random fact is not in the literature but is quite helpful from a procedural perspective. I think that science twitter has a potentially super valuable role to play in reporting unexpected or otherwise difficult to publish findings.
Instead, they demonstrate that doing something this specific way is too much of a hassle, too unreliable, or too expensive to answer a particular question in a particular context: skills, budget, timeline, resources, alternative ideas, etc.
I don't see how you could write pilot results up ("Trust me, I'm usually pretty good at these sorts of things but this didn't work well"?). Meanwhile, disentangling these factors in a rigorous, generalizable way would turn it into an entirely different project.
On the other hand, data sharing protocols mean the data is going to be made available, so I think that probably addresses that issue.
The problem with pilot data is that lots of things can change in the course of running the pilot; tweaks of the experimental protocol, bug fixes to code, etc.