How can a CPG company use AI to answer business questions without waiting for the analytics team?

Summary

• The fastest path is a conversational analytics agent that turns a plain-language question into governed SQL and returns an answer with a chart in minutes, so a category or commercial manager stops filing a ticket and waiting.
• Databricks AI/BI Genie does this on the lakehouse: a user asks "how did last quarter's promotions perform by region," Genie writes the query, runs it, shows the result, and shows the SQL it used so the answer can be checked.
• Trust comes from Unity Catalog: each person sees only the data they are authorized to, every query is read-only, and every interaction is logged, while certified metrics keep terms like net sales and share consistent everywhere.
• The analytics team is not removed; it shifts from fielding repetitive requests to curating scoped Genie agents, defining certified metrics, and taking on the harder analysis.
• The reliable pattern is scoped, curated Genie agents per data domain with example questions and certified metrics, not one giant space pointed at hundreds of raw tables.

How can a CPG company use AI to answer business questions without waiting for the analytics team?

Give business teams a conversational analytics agent that turns plain-language questions into governed queries. On Databricks that is AI/BI Genie: a category manager types "how did last quarter's promotions perform by region," Genie translates it to SQL, runs it against the lakehouse, and returns the numbers with a chart plus the query it generated. The relationship with data shifts from "submit a request and wait" to "ask a question and get an answer." The goal is not to remove analysts; it is to free them from the ad-hoc queue so the business self-serves routine questions and the data team takes on the problems that need real depth.

What is conversational, self-service analytics?

Conversational analytics lets a non-technical user ask a question in natural language and get a governed answer without writing SQL. The system reads your data model, picks the relevant tables and columns, generates a query, runs it, and returns results with a visualization. Good implementations do two things a generic chatbot does not: they ground every answer in your governed data with the query shown, and they ask a clarifying question when a request is ambiguous rather than guessing.

What to look for in a self-service analytics tool

Capability	Why it matters
Natural language to SQL on your own tables	Answers must come from your governed data, not a model's general knowledge, or they cannot be trusted for a business decision.
Transparency of query and sources	Showing the generated SQL and the tables used lets an analyst validate an answer and lets a business user build confidence.
Clarifying follow-ups	Real questions are ambiguous. A tool that asks "which region" instead of guessing avoids confidently wrong answers.
Certified metrics and shared definitions	When net sales, share, and promotional ROI mean the same thing everywhere, dashboards and chat agree and debates end.
Row and column-level security with audit	Self-service is only safe if each person sees only authorized data and every query is logged for review.
Charts, forecasting, and dashboards on the same data	The answer often needs a visual or a projection, so analysis and reporting should sit on one governed dataset.
Curated, scoped setup	Trustworthy answers come from a focused, well-described dataset, not a firehose of raw tables.

How Databricks approaches self-service analytics for CPG

AI/BI Genie is the conversational layer. Domain experts configure a Genie agent with the right datasets, sample queries, and plain-language instructions; business users then ask questions directly. Genie selects the relevant tables and columns, generates and runs the query, returns the result with a visualization, and asks a clarifying follow-up when a question is ambiguous. The blog on giving business users direct data access walks through the experience.
AI/BI Dashboards sit on the same lakehouse data and add chart generation, forecasting, key-driver analysis, and cross-filtering, so a question can become a shareable view. Unity Catalog is the trust layer underneath: it enforces row and column-level security so users see only what they are allowed to, keeps Genie queries read-only, logs every interaction for audit, and holds certified metrics and metric views so business definitions stay consistent across chat, dashboards, and SQL.
The pattern that works for CPG is scoped Genie agents per data domain, one for retailer point-of-sale, one for syndicated market measurement, one for shipments, each with curated example questions and certified metrics, rather than one giant space aimed at hundreds of raw tables. That setup needs gold-layer tables in Unity Catalog, related tables pre-joined into views or metric views, and business users granted access.

Use cases across a CPG business

Category and brand management. Ask for sell-through, share, and promotion performance by retailer and region, and get an answer without a reporting request.
Commercial finance. Track spend against plan, margin, and trade-promotion return, and drill into the drivers when a number moves.
Supply chain. Check on-shelf availability, inventory positions, and fill rates by account on the same governed data.
Field sales. Ask "what were our top-selling items in this account last week" and get an immediate, sourced answer.

FAQs

Does a business user need to know SQL?

No. They ask in plain language. Genie writes and runs the SQL, returns the result with a chart, and shows the query it used so the answer stays verifiable.

How does the team trust the answer?

Genie shows the generated query and the tables it used, certified metrics keep definitions consistent, and Unity Catalog restricts each user to authorized data. An analyst can open and validate any answer.

What happens to the analytics team?

They stop fielding repetitive requests and move to higher-value work: curating Genie agents, defining certified metrics, and taking on the analysis that needs real modeling.

What data does Genie need to be reliable?

Gold-layer tables registered in Unity Catalog, ideally pre-joined into views or metric views, with clear descriptions and a few curated example questions per agent.

How do we keep answers accurate as the business grows?

Scope Genie agents by domain, curate their example questions, and maintain certified metrics in Unity Catalog so definitions do not drift as more teams adopt self-service.