The goal of properly exploiting BI comes with many prerequisites which sound superficially reasonable but turn out to be decade-long side-quests. Things like having all your data in one data model. Things like understanding where your data comes from, and exactly what it means.
These prerequisites are easy for small orgs, but small orgs benefit least from BI and typically get better bang-for-buck from Excel.
Large orgs find themselves mired in the political meta-problems of meeting those prerequisites, like joining up the fiefdoms that own data sources with the cabals that run data governance and the accountants who want a return on the investment of simplifying a sprawling legacy estate.
BI tools are generally incredibly poor at dealing with the bag-of-spanners heterogeneous data landscapes that exist in these organizations, and their analytics nirvana remains largely unattainable.
The trend that the article describes - towards simpler, composable BI components, each with more modest goals - is progress. It helps move focus away from the relatively-easy problem of visualization, to the rest of the data stack.