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?

John Elder in 2005 gave a (now classic) presentation called: "Top 10 Data Mining Mistakes". Number 4 in that list is: Listen (only) to the data.
Specifically for business environments where it is almost certain that we act using incomplete information (e.g. client priorities, financial and physical resources, legal framework, etc.) which affects our outcome of interest, ignoring prior knowledge can be very detrimental. At best we will duplicate work and/or produce trivial results; at worst we will get nonsensical "data-driven" findings. To be fully bearish on this as Twyman's law states: "Any figure that looks interesting or different is usually wrong".
Some points specifically to your question:

*

*Finding "the important features through experiments" will entail a full experimental design. If they are will to invest the time and money to it, go for it. Also familiarise yourself with the "Analysis of Observational Data" (yes, it is "unsexy" to read about survey analysis but treating a biased sample as being random is catastrophic.)

*This is good point in an analysis project life to consider applications of Bayesian inference as a way to formalise modelling assumptions. Simply put, if we have 200 features some have to be more likely to affect our outcome than others; use priors to encapsulate that.

*Respect the physics of the environment you operate in (until you don't have too). Signal strength diminishes as distance increases; average client in Luxenbourg has more disposal income than one in Bulgaria. Use well-established hypotheses as priors/starting points of the analysis; update wisely.

*Read the literature. Really, this is not just an academic activity. Very often we can get reasonable answers by curated resources if we make formal searches. Similarly, if there is nothing available this is even more of a reason to revisit points 1 & 2 in greater detail.

To recap: absolutely talk to domain experts (or work hard to become a budding one yourself). It can save you a world of pain; if anything downstream you will have a better idea on how to present your findings and what were/are usual contention points.
