Data analytics with AI is reshaping traditional BI around semantic understanding
A lot of AI-BI tools are starting to push toward semantic understanding rather than just dashboards.
Platforms like ThoughtSpot, Looker, Power BI (Copilot), Qlik, QuickSight, and Sigma all seem to be moving in that direction. On the other side, newer tools like Julius AI and Lumenn AI feel built around this idea from the start, using dataset context, metadata awareness, and LLM reasoning to explore data without heavy manual querying.
It makes me wonder what’s enabling this shift under the hood. Are these tools increasingly relying on metadata-aware data layers (like dbt Semantic Layer, Cube, AtScale, Omni) and LLM capabilities to understand datasets and generate insights? If so, where do the bottlenecks show up, inconsistent metrics, weak metadata, hallucinated joins, governance issues, or trust in AI-generated answers?
If this matures, the shift in BI could be pretty big, moving from manually building dashboards to AI-driven exploration, with analysts focusing more on validation, metric design, and decision support.
Curious how others are seeing this, are these tools actually improving trust in analytics, or just moving the bottleneck from SQL to metadata quality?