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?

The problem you are dealing with is a selection of variables problem, and so standard principles and methods apply.  In particular, if you have a large number of initial variables/features to select from, there is a danger of overfitting if you fail to adopt appropriate methods that account for multiple comparisons.  In the case where you have an exogenous source of information about the variables from subject matter experts, this can be used for prior information to guide your selection method --- e.g., to narrow down the model/comparisons you will make.
You are correct to have serious misgivings about the proposal made to you.  The idea of first using statistical methods to find relevant features, and then giving this information to the business team to inform their feedback sounds like a terrible idea to me.  By doing this you take a potential form of a priori exogenous information and you bias it heavily by feeding in your statistical results first.  It will certainly bias the feedback of the business team and so whatever information they give you after that is likely to be contaminated and useless.  If you then narrow your model/fitting to focus on the features/variables they "confirm", this will effectively over-weight the data in your original model fit (since they will largely just feed back the posterior results as prior information) and you will almost certainly overfit your model.  I recommend reading Gelman and Loken (2013) on the "garden of forking paths" that occurs when making research/modelling choices.
