Which comes first - domain expertise or an experimental approach? In my organisation, we are embarking on an AI initiative where we try to identify business use cases and solve them using traditional ML algorithms.
However, our business users say that before they even take part in brainstorming, selecting, and reducing the feature space, they are asking the data folks to do a detailed scan and experiments and find out what are the most important and looks like important features through experiments...
Example: Let's say my data has 200 features and 30K rows. Our business team says that they will not be able to guide what the most relevant features to look at are, because they think this might bias the results. So, they want the data folks to find the important features through experiments.
Later, take these features and go to business team to check its relevance. Basically, no domain expert input until they get some confidence in what the algorithm outputs (for relevant features which has influence on the target variable).
Is this how it normally works in real-world AI projects? Is this a better approach to start with an AI project? Is there anything that we should be aware of?

This will probably be closed quickly as opinion-based, but here is a point you may want to consider.
200 features is a lot, and 30k rows is less than it sounds like. A "fishing expedition" to find relevant features is quite likely to overfit and select spurious features. The danger is that when you go to your domain experts with these features you "found" to be relevant, they may not push back. Instead, it's a very common human reaction to start telling stories about how these features are indeed useful, because we humans are very good at explaining stuff, even stuff that is simply noise.
Talking to your domain experts first will not completely avoid this problem, but it may reduce the number of wild goose chases.
You may be interested in my answer to "How to know that your machine learning problem is hopeless?".
