>The 2017-2019 corporate version of "invest in AI" meant to build an in-house team to do ML experiments on internal data, and then usually evolved a bit to get

>The 2017-2019 corporate version of "invest in AI" meant to build an in-house team to do ML experiments on internal data, and then usually evolved a bit to get some "ml-ops" thrown in so they could "deploy" the models they built. You nailed it, although very few models actually ever got deployed to Prod at Fortune 500 non-tech companies and the few that did delivered little value. I'm a consultant and most internal AI/ML/DS teams that I interacted with were just running experiments on internal data as you said, and the results would get pasted into Powerpoint, a narrative created, and then presented to executives, who did little or nothing with the "insights". Reminded me of the "Big Data" boom a few years earlier where every company created a Big Data Team who then promptly stood up a Hadoop cluster on prem, ingested every log file they could find, and then..................did nothing with it.