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    How AI agents could make business intelligence more question-driven

    A viewer discussing a Rippling case study says its data team still manually inspects agent queries and builds rollup tables when patterns emerge.

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    1 Source, 4h ago, first seen 4h ago

    TLDR

    Reflecting on a Rippling case study video, a viewer argues that AI agents could shift business intelligence toward answering questions as they arrive, rather than relying on tables and dashboards built in anticipation. The viewer says Rippling’s data team still manually inspects agent queries and creates rollup tables when patterns emerge. They also speculate that queries leading to useful business actions may matter more to semantic-layer tools than query history alone.

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    1 Source, first seen 4h ago

    Combined views

    4.1K

    1 Source, first seen 4h ago

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    11 comments
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    1 Source

    @sh_reyaFound this video interesting (especially as someone who hasn't been a user of BI tools for 5+ years). Some interesting implications from this Rippling case study (all of these are my opinions): - For agents, choosing a tool / MCP and choosing what data to retrieve start to look like the same problem: both are pieces of context needed to answer a question. The old boundary between "business logic" and “data” gets blurrier. - Also interesting: Rippling’s data team still manually inspects agent queries and creates new rollup tables when patterns emerge. Where are the materialized view database fiends -- they would find this very fascinating!! - I could be wrong but: it feels like trad ELT is mostly push based: i.e., humans anticipate questions and build tables / dashboards. Agents make BI much more pull based than in the past: the question arrives first, then the system figures out what data, transformations, and tools it needs. - And perhaps one reason why existing semantic layer tools aren't great yet: not all past queries are equally useful signals. Perhaps we cares more about which queries led to useful business actions, not just query history in aggregate.4h

    1 Source

    @sh_reyaFound this video interesting (especially as someone who hasn't been a user of BI tools for 5+ years). Some interesting implications from this Rippling case study (all of these are my opinions): - For agents, choosing a tool / MCP and choosing what data to retrieve start to look like the same problem: both are pieces of context needed to answer a question. The old boundary between "business logic" and “data” gets blurrier. - Also interesting: Rippling’s data team still manually inspects agent queries and creates new rollup tables when patterns emerge. Where are the materialized view database fiends -- they would find this very fascinating!! - I could be wrong but: it feels like trad ELT is mostly push based: i.e., humans anticipate questions and build tables / dashboards. Agents make BI much more pull based than in the past: the question arrives first, then the system figures out what data, transformations, and tools it needs. - And perhaps one reason why existing semantic layer tools aren't great yet: not all past queries are equally useful signals. Perhaps we cares more about which queries led to useful business actions, not just query history in aggregate.4h