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?

There are two aspects here causal inference and explainability.
From a causal inference perspective, domain expertise should guide the process of building relevant factors on a given purpose, targets, that are really linked and not just correlations explored or discovered by data scientists.  Inference and Intervention: Causal Models for Business, by Ryall-Bramson (2013), routledge provides set of case studies on how domain knowledge can guide building causal models.
In purely machine learning projects, bottom line is model's performance. So called feature discovery and extraction is usually performed without domain experts input, and best performing model is generated by data scientist. Domain experts involvement in this approach appears to be in ensuring to set to right business target in machine learning models. If it is a regulated environment or a requirement is there, then the machine learning model explainability work might require intervention from domain expert, see Interpretable Machine Learning.
