Bridging the AI Gap
Why Conversational Analytics Struggles with Structured Data (and how to fix it)
September 30, 2025
Every data leader feels the pressure to deliver insights faster, enable self-serve, and still maintain trust. Business users want instant answers, but even the best dashboards and semantic layers can’t keep pace with demand.
That’s why many organizations turned to AI Conversational Analytics, solutions like Snowflake Cortex Analyst and Databricks Genie, hoping to clear the backlog of data requests.
New Research Report
Our new benchmark study shows that results have not matched the promise of GenAI Conversational Analytics platforms:
- Accuracy across platforms ranged from 54% to 92%
- Inconsistencies reached 12% from some platforms when the same question was asked multiple times
These gaps erode trust and ultimately slow down decision-making, the opposite of what was hoped for.
Bridging Speed and Trust
We’ve been on both sides of this challenge - waiting for answers and trying to deliver them - so we dug in to identify the gaps and test potential solutions.
We discovered, through testing, that when we added a governance and intelligence layer supported by structured workflows, accuracy and consistency reached 100%, bridging the gap between speed and trust. This layer can be used on it's own in runQL or as a layer on top of Snowflake Cortex Analyst or Databricks Genie.
Inside the Report
The full “Bridging the AI Gap in Business Intelligence” report covers:
- Where Conversational Analytics tools are falling short
- What’s required to make these tools trustworthy and repeatable
- A buyer’s guide to help executives evaluate GenAI data solutions
The findings reveal what’s actually happening with these platforms and what’s required to restore confidence with analysts and business users. You can read the full report for our methodology, benchmarks, and detailed results here:
— The runQL Team