Stop scoring agentic BI by how many dashboards it creates

A conversational analytics rollout can look busy while leaving the old operating model intact. Databricks argues that dashboard counts and license usage say little about whether people are getting decisions from governed data; its proposed alternative measures the path from a business question to a trusted answer. Databricks

Start the clock at the question

The first signal is time from question to trusted answer. That wording matters: latency to the first generated chart is not enough if a user still waits for an analyst to reconcile the result with finance. Databricks recommends timing common operational questions end to end; if the interval is still measured in days, the report queue remains the bottleneck. Databricks

For an evaluation, select recurring questions before rollout, record their current completion time, and rerun the same measurement after users receive natural-language access. Keep “trusted” explicit: the answer should use an approved definition and be usable without a second reconciliation step.

Count competing definitions, not generated assets

The second signal is the number of competing definitions for the organization’s most-used KPIs. Databricks suggests selecting the top 10 metrics and counting variants across BI tools and spreadsheets. The practical warning is that adding a natural-language interface above inconsistent definitions can make the inconsistency faster rather than remove it. Databricks

That makes metric consolidation a rollout dependency, not cleanup work for later. A team should know which definition the agent will use, who owns it and where exceptions are documented before judging answer quality.

Watch the queue and the analysts together

Databricks’ third and fourth signals are the share of questions answered without an analyst ticket and the portion of analyst time spent on routine requests. Rising self-service use alongside falling ad-hoc tickets is the desired combination; moving questions into chat while analysts continue repairing answers is not. Databricks

Track both measures in the same review. Otherwise, a decrease in tickets can hide support work that moved into informal messages, answer certification or incident cleanup.

Audit what decisions actually cite

The fifth signal asks whether important decisions cite governed metrics or personal exports. Databricks presents this as the clearest test of whether trust changed: people may use the agent frequently and still fall back to spreadsheets when the decision matters. Databricks

The resulting scorecard is small: trusted-answer time, KPI-definition count, ticket-free answer share, analyst routine-work share and governed-data citation. It does not prove that every generated answer is correct. It does, however, force an agentic BI program to show that it reduced delay without creating another layer of competing numbers.