Building an Explainable AI Research Agent With Databricks Genie

The team partnered with Databricks to create a conversational research agent, built on Genie, that gives thousands of retail leaders fast, trustworthy answers grounded in governed data. Leaders can now ask why sales dipped, why a store overperformed, or why a model missed, and get narrative responses that align with business logic. The approach reflects a broader shift in how AI is transforming data analytics, moving the value of forecasting beyond prediction and into actionable explanation.

Impact shows up in fewer tickets, fewer sprawling dashboards, and a faster path from question to insight. Data scientists reclaimed time to improve models, while stakeholders gained confidence because answers arrive quickly enough to influence planning.

Shift the problem: from making predictions to explaining outcomes

The bottleneck was not forecast generation. It was the flood of ad hoc follow-ups that required analysts to test hypotheses, join many tables, and compose explanations by hand. Dashboards helped only partially, since each stakeholder wanted a different cut and complexity grew until navigation required the original developer.

Reframing the need around explanation led to a conversational agent that answers in plain language, supported by analysis under the hood. Leaders did not need more charts. They needed clear, defensible reasons tied to the data they trust.

Governed data and encoded context make GenAI useful

Accuracy depends on curation. The team registered relevant tables in Unity Catalog and used the AI-powered metadata builder to generate draft column descriptions that domain experts validated. Comments, join keys, and business definitions became teaching material for the agent.

Beyond metadata, the team configured a dedicated retail Genie space. They documented table joins, defined SQL expressions for business concepts like sales per square foot and sales per staff member, and curated example queries over months. Configuration mirrored onboarding a new analyst, transferring vocabulary and institutional knowledge so answers are correct in context.

What the agent enables for business users

The agent is embedded directly in dashboards and available as a standalone space for multi-step root cause analysis. Users can click a visualization to ask why a trend changed, or open the agent to explore across datasets in natural language. The agent remembers conversational context, shows its reasoning, and produces editable charts and tables that can be downloaded or added to dashboards.

In live demos on synthetic retail data, the agent identified issues like incomplete months, compared stores to chain averages, and explored drivers such as promotion exposure and store attributes. Users can adjust visualizations, filter to relevant features, and iterate on questions in minutes rather than days.

A new operating model for analytics

Moving from a push model to a pull model changed how analytics is delivered. One hundred dashboards for one hundred users became one governed foundation that supports a hundred perspectives. Ad hoc turnaround times dropped from days to minutes, which built trust across partners like finance and merchandising.

Freeing data scientists from repetitive query work created space to invest in model development, contributing to improved forecast accuracy. The agent does not replace analysts. It produces high quality analysis at scale, so experts can focus on advancing methods and strategy.

Treat explanation as a product, and give AI the context to deliver it

Effective conversational analytics starts with curated data and explicit business logic. Encoding joins, definitions, and measures in a governed environment let the agent answer like a trusted analyst. The forecasting model mattered, but the breakthrough came from productizing the explanation layer so every stakeholder can ask why and act fast.