I've had a research-heavy career in computer science. A central problem with the academic research is it commonly ignores real-world constraints. Or less commonly, it imagines constraints that don't exist. As someone who went deep in a few domains, academic literature is usually disappointing[0] once you've read and understand the entirety of it.

The valuable thing about hands-on experience is that the real world doesn't let you ignore constraints. You get that feedback quickly if you are paying attention. This in turn allows you to build a more accurate mental model of the true nature of the problem you are solving and where the hidden limitations and leverage points are. A lot of academic literature tacitly works from a set of assumptions that don't map to any real-world environment.

Once you have that hands-on mental model, the flaws and limitations of much of what is in the academic literature becomes obvious from first principles. Most of the insights might be academically interesting in a theoretical sense but they often aren't reducible to useful practice in real systems. Non-academic careers require implementations that actually work well.

[0] The computer science literature from the 1970s and earlier is much better in this regard than what came later. Many early papers were written by people that clearly had concrete experience in the trenches with the problems they were writing about. Those papers are both more readable and more applicable. This awareness of constraints is lacking from a lot of modern computer science papers.