AI chat tools are getting faster: → but are they actually useful?

AI chat tools are getting faster: → but are they actually useful?

A growing number of data-first vendors are layering chat interfaces on top of their proprietary datasets. Open/closed claims, EMR, lab, sales — ask a question, get a curated answer. No SQL, and customized dashboards.

But here’s the question that keeps coming up: Where does this fit into how we already do analytics?

We already run primary research. We acquire and build a robust patient universe, across claims, EMR, dispensing and lab feeds (and more). We rely on secondary data assets to track market shifts and triangulate across sources to answer complex business questions.

So when an AI layer offers answers through a licensed interface, disconnected from downstream systems — it risks becoming just another triangulation point, rather than something we can build into how we structure insight.

Not saying these tools aren’t promising. They’re fast, intuitive, and improving quickly. But until they map into our real workflows, they stay useful, but isolated.

Curious how others are thinking about this.