Viewpoint: From Generative BI to Agentic Analytics
September 2, 2026
Generative BI began with a straightforward premise: allow business users to ask questions of enterprise data in natural language and receive useful answers without writing SQL or navigating a complex dashboard.
Many organizations connected a large language model to data, added text-to-SQL, or introduced a chatbot over existing reports. These approaches can make information easier to access, but they do not automatically solve the harder questions: Which metric should the system use? Which source is authoritative? How should the AI interpret business terminology? When should it return a certified answer, and when is exploratory analysis appropriate?
The issue becomes more consequential as generative BI evolves toward agentic analytics.
To explore this shift, TDWI spoke with Josh Klahr, product manager at Snowflake. Klahr described a market moving beyond isolated natural-language queries toward AI systems that can complete analytical workflows.
Download this special Viewpoint today to learn about the architectural changes required when the primary consumer of semantic models, metadata, and business definitions is increasingly an AI agent rather than a human analyst.