Hi, I'm the author of the blog post. What problem we are solving at IKEA exactly is something I plan to write next about, once we get some numbers in from AB testing etc. We are not there yet to share concrete results.
KGs in the general e-commerce space solve search disambiguation and bridging the gap of industry language with customer language; common sense recommendation, especially when more and more customers do not opt-in to sharing their data; and embeddable info box content throughout the customers browsing experience.
KG is not a technology but a paradigm shift from an application-centric data modeling (classic data engineering) to a business/purpose/people-centric data modeling. It means that on top of the data layer, the KG enables to access the data via business concepts. It enables e.g. simple questions like which products are suitable for customers that live in small apartments and automatically solves the problem of combining different data sources to answer the question.
Knowledge Graphs are more and more adapted in practise. There are some nice in-use papers about it, for example one with Pinterest (https://arxiv.org/pdf/1907.02106.pdf).
My experience is that programmers (if you can generalise them like this) breath a sigh of relief when I as an ontologist (someone who builds knowledge graphs and makes sure that they are logically sound) tell them that I will work with domain experts to get their knowledge into a structured form that the programmers then may consume programmatically.
[BILLY bookcase in white 80x28x202 cm] rdf:type :Product . Product rdf:type :Class .
[BILLY bookcase in white 80x28x202 cm] :categorisedAs :bookcase . :bookCase rdf:type :Category .
*[ ] = IRI of that particular product