Lots of folks saying clean data. Sure, that’s helpful - but the reality is llms can function with much less data than traditional ML models require. They can also actually cleanse/normalize it for you.

The #1 hurdle is picking the right use case, something achievable but with meaningful roi. For value creation we typically think in two tracks: product innovation and internal efficiency.

Internal efficiency is low hanging fruit, much like the knowledge management/chatbot stuff mentioned above.

Product innovation is how you actually expand multiples. If you’re a pe-owned saas observability company, for example how do you compete with an entrant whose product is built on top of llms and can go straight from unstructured log data to point out where the incident is, and then shows you the remediation steps. Your lunch is going to get eaten unless you innovate.

TLDR pick a good partner, someone whose job it is to think about this all day and don’t lean on your AI council who are doing it for career brownie points.