The issue is the following: In economics, one is interested in an underlying parameter of a complex equilibrium system (or, if you wish, a non-equilibrium complex system of multi-agentic behavior). This may be, for example, some pricing parameter for a given firm - say - how your sold units react to setting a price.
Economics faces two basic issues:
First, any predictive model (like a NN or simple regression) that takes price as an input, will not correctly estimate the sensitivity of revenue to price. It is actually usually the case, that the inference is reversed.
A model where price is input, and sold units or revenue is output (or vice-versa) will predict (you can check that using pretty much any dataset of prices and outputs) that higher prices lead to higher outputs, because that is the association in the data. Of course we know that in truth, prices and outputs are co-determined. They are simultaneous phenomena, and regressing one on the other is not sufficient to "causally identify" the correct effect.
This is independent of how sophisticated your model is otherwise. Fitting a better non-linear representation does not help.
The solution is of course to reduce down these "endogenous" phenomena to their basic ingredients. Say you have cost data, and some demand parameters. Then, using a regression model (or NN) to predict the vector of endogenous outcome variables will work, and roughly give you the right inference.
Then, as a firm, you are able to use these (more) exogenous predictive variables to find your correct pricing.
This is not new, pops up everywhere in social science, is the basis of a gigantic literature called econometrics, and really has nothing to do with how you do the prediction.
The only thing that NN add are better predictions (better fitting) and the ability to deal with more data. As this inferential problem shows, using more (and more fine-grained) data is indeed crucial to predicting what a firm should do.
BUT, it is crucial to understand and reason about the underlying causality FIRST, because otherwise even the most sophisticated statistical approach will simply give you wrong results.
Secondly, the counterfactual data for economic issues is usually very scarce. The approach taken by machine learning is problematic, not only because of potentially wrong inference, but also because two points in time may simply not be based on comparable data-generating processes.
In fact, this is exactly the blindness that led to people missing the financial crisis. Of course, with enough data, and long enough samples, one should expect to be become pretty good at predicting economic outcomes. But experience has shown that in economics, these data are simply too scarce. The unobserved variation between two quarters, two years, two countries, two firms (etc.) is simply very large and has fat tails. This leads to spontaneous breakdowns of such predicitive models.
Taking these two issues together, we see that better non-linear function approximation is not the solution to our problems. Instead, it is a methodological improvement that must be used in conjunction with what we have learned about causality.
Indeed the literature moves into a different direction. Good economic science nowadays means to identify effects via natural experiments and other exogenous shifts that can plausibly show causality.
Of course such experiments are more rare, and more difficult, the larger the scale becomes. Which is why Macroeconomics is arguably the "worst science" in economics, while things like auctions and microstructure of markets are actually surprisingly good science (nowadays).
Doors are wide open for ML techniques, but really only to the point that they are useful in operationalizing more and better data.
Anyone trying to understand economic phenomena needs to be keenly aware of how inference can be done, which requires an understanding (or an approach to) - that is, a theory - of the underlying mechanisms.