Apart from X-ray crystallography there are other methods for structure determination such as nuclear magnetic resonance (NMR) or cryo-electron microscopy (cryo-EM). The latter has seen a dramatic improvement in resolution over the last decade.
Another idea is these may come into play for anti-verification, so if you are drug screening against a known structure. You could potentially use these more flawed structures of proteins you don't want to target but may be similar, and try to reduce the drug's efficacy at binding them. Or something to that effect. All of that is fun ideas that are currently being explored in that space but we'll see where it takes us.
Every couple years there is a massive competition called CASP where labs submit previously unresolved protein structures derived from experimental EM, x-ray crystallography, or NMR studies and other labs attempt to predict these structures using their software. AlphaFold2 absolutely destroyed the other labs in the main contest (regular monomeric targets, predominantly globular) for structure resolution two years ago, in CASP 14.
https://predictioncenter.org/casp14/zscores_final.cgi
The latest contest, CASP15, is currently underway and expected to end this year. As with all ML, the usual caveats apply to the models Google generated -- the dangers of overfitting to existing structures, artifacts based on the way the problem was modelled, etc