For some use cases, and when everyone has access to all the systems, and data volumes allow reasonable perf, I could see this working well.

For things like customer-facing analytics, there needs to be a deployment layer for AI-native artifacts that supports ongoing operations & maintenance. If you want a conversational data agent to answer questions and reduce custom analytics requests, that's more to manage and maintain.

If you watched the Claude Dashboards announcement, they said legacy bi will be the deployment layer for AI artifacts. I don't buy it. Legacy BI was terrible at deployment in the first place.

We've built a performant, browser-native alternative to deploy and scale AI artifacts at Ridge AI (ridgedata.ai).

Fascinating to watch all of this evolve in real time.