PhD: Find the problem to solve or use tool to solve a problem? So let's say I have a problem (X) that needs to be solved. Most people in research use several approaches around a sub-topic Y. Most of these algorithms have performance of 80-95% accuracy so there is a space for improvement. I understand that a proper PhD topic would be to find a better solution here. 

But what if I just use a new technique (K) to solve this problem? It hasn't been addressed yet in literature. I expect the performance of it to be lower or near the conventional methods. I justify using it because it's outperforming in imagery tasks and it would open up the literature to a new method.

If I use K to solve this problem and get lower performance than conventional solutions Y, does that count as a proper PhD thesis?

In other words, to define a PhD topic, should I find the problem to solve or use a tool and see how it solves a problem?
The following answer/story is more to address the titular question, and the last line of the OP (free of the context of the rest of the post)...
I once attended a talk by Peter Sarnak.  He had the following to say (which I recall from memory; these are not guaranteed to be his exact words) on the matter:

My student had just recently finished solving a problem for his thesis.  He was very excited to have found a new way to approach the problem that worked.  He said to me,
&quot;Okay, now that I have these new tools, what can I solve with them?&quot;
To which I responded:
&quot;No, no, no!  That's not how you do research.  You don't pick up tools and then take them to a problem to solve with them.  I mean, sometimes you can get lucky that way, so you can try it if you want.  But that's not the smart way.  What you do is, you pick up a problem and then you invent the tools you need to solve it.

As far as the more specific question of the OP...
Ultimately, this is something to discuss with your advisor.  Expecting worse results is not likely to sound very appealing as a project idea, unless you can make a convincing argument that it will make some sort of improvement—be it computation time, memory consumption, cost to implement (maybe it can be run on data obtained by cheaper technology that other methods basically can't), or a basic proof of concept that could lead to more meaningful breakthroughs after suitable adjustments and investigation, etc.  If you can do those now, your advisor may decide this could be a fruitful area of investigation.
Either way, talk it out and see what his opinion is, and if he knows if this actually has appeared in the literature before or not.