Do you build text classification models in house? Why do you think eng teams with 'obvious' use cases don't?
Some examples below: - LegalTech categorizing legal docs for lawyers - HR Tech tagging job descriptions by category - Insurance labeling docs/records for claims adjusters
* The people or industry have low tolerance or fear around risk of false positives
* The industry is centered around billable hours and has no incentive for automation
* The engineers or people perceive ML as this obscure/difficult thing
I'd say the incentives and risks have hindered lots of legal adoption (this is what I observed while working in legaltech for instance). Insurance sounds similar, but I'm less familiar and assume they are coming along more quickly.
[0] I agree with minimaxir's point, that it's a bad assumption to think few teams use basic ML functionality. This will become even more true as emergent tech such as zero shot classification with LLMs becomes more commoditized.
This is an extremely incorrect assumption.
If you have a Data / ML team, they will handle ofc.