Business Analytics

TL;DR for operators AI deployment is no longer mainly a question of whether a model can produce something plausible. That problem has been solved often enough to become boring, which is usually when businesses start wasting money at scale. The live problem is control. Which model should be trusted on this workload? When should a system query another model, pay more, or stop? When an LLM produces an analytical “insight”, is it finding the pattern you care about, or merely discovering an aggregate confound wearing a nice blazer? ...

Surveys look simple because the final artifact is simple. A customer clicks “agree.” An employee rates burnout from one to five. A manager reads a dashboard that says trust, anxiety, satisfaction, or readiness has moved by 7%. Everyone behaves as if the hard part was collecting responses. That is the polite fiction. ...