If you're doing science and you don't know the literature, then you're doing a bad job.
Understanding a paper won't work by reading AI summaries. Finding surprising new papers in your field should be very rare even without tech.
[edit] Let me re-phrase the very negative statement above. There are a couple of important tasks in working with literature.
One is finding cumulative truth, i.e. figuring out the overall state of research. This should be done in a true meta-analysis of the data, since references are sometimes citing articles incorrectly (i.e. claiming something that's not in the paper) and the linguistic complexity is enormous. Meta-analyes are a lot of manual work, even though you can extract some data from standardized tables.
Another one is figuring out what new publications there are on a specific subject. Search engines like google scholar are immensely useful here, because you can follow the reference tree in both directions (citing and cited). This tool would help, but perhaps incrementally so (not a panacea).
A third task would be to identify novel interesting papers that you didn't know about, perhaps from a different field. This tool might help, but only if an unknown interesting paper cites something you're looking at (i.e. if the networks are not disjoint).
Apart from that, offering structural analyses and visualizations of science is something I believe should be done with open source and open data, because science is about trust and transparency - now more than ever.