From my side, I cannot pay for something that works worse than that as it will have no value for me
Understandable. Viewed from the other end besides a student or an enthusiast you most likely won‘t find anybody …
A step back, ML is a filter, which depends on the history of its data offered. More generalized there‘s a lot of noise, so your are in the business of designing robust products. This can be done systematically, including identifying and improving limited concepts.
Is there a way I can mitigate this such as offering a very low rate for trying but not achieving the results?
Consider your employer offers you just that … and you know the answer.
Is there any best practice/strategy on requesting difficult RnD(research and development) requirements while minimizing my risk?
As I lined out above, evaluating and improving robustness of your system offers this. It won‘t be a one step process. Here are some things that will and have to happen on this path:
- You have a goal in mind and a couple of ideas or concept to achieve it
- That‘s a good starting point
- These concepts will demonstrate shortcomings (either by expensive market introduction with redesigns, or quick with robust …)
- Which means: your beloved idea will/has to die to make room for the more capable one
- Which will require at least one to-the-point invention
- While your system will perform so good, you can‘t believe it … if you dare taking this route.
To indicate roughly 2 out of 3 products have a hidden performance, which you simply can use afterwards. Roughly 1 out of 3 products „fail“ optimization attempts … and those are your gold mine. Why? Because their concept imposes limitations. How to overcome it on-the-fly? By said invention(s). Literally in hours. So you want to torture your ML to fail quickly, to invent, improve and learn fast … beyond books, conferences, working groups.
To sum it up, if you don‘t invest into a guide on this route, you won‘t get returns. If you do, you are on a turbo-learning-link, with a small team. Consider investing in this kind of know-how and capability, not into exchangeable capacity. Then review your risk concerns.