There can be multiple reasons for this[0], including but not limited to:
* 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.
