I’ve been working in the “real world business processes that companies are trying to AI-ify” realm for quite a while now. Pharma, cyber security, oil and gas production, etc. This article doesn’t mention a really, really straightforward factor for why AI hasn’t invaded these domains despite billions of dollars being dumped into them. An automated process only has to be wrong once to compel human operators to double o…
In lots of business contexts, probably most, reducing variance is much more valuable than reducing mean expenses. Variance can halt downstream production, so the loss can be some huge amount of opportunity. And variance propagates through a supply chain, so your customers will hate variance in your output, as it may mean they have to ship the variance forward, which their customers hate. Plus if you allow your supply chain to get away with inconsistency, they can start to rob you with lower average quality and it will take you time to notice.
If a company has been bothering to do some process manually and they haven't outsourced it to the cheapest humans possible, then they care more about low variance than low cost. Pitching these businesses a solution that lowers cost at the expense of unknown high variance is very unattractive. Instead, you want to tell them "I can reduce your variance even further! Here's what that would cost".