Unlocking semantics for AI: How Mercedes-Benz Korea built trusted “Talk to Data” at scale

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Summary

One KPI layer: Mercedes-Benz Korea built on its established Lakehouse and Power BI stack by making 500+ KPI definitions available in an open, AI-ready semantic layer on Unity Catalog metric views, using an automated DAX-to-Metric-View transpiler from Databricks to accelerate the transition.

Governed semantics for BI and AI: With Unity Catalog metric views, Mercedes-Benz Korea extended its governed semantic layer for enterprise KPIs. This layer supports both existing BI reports and new “Talk to Data” experiences, with Genie and Agent Bricks providing answers consistent with the existing KPI definitions.

Scaling “Talk to Data” across markets: Building on Unity Catalog metric views, Genie, and Agent Bricks, Mercedes-Benz Korea is shaping a playbook for persona-based AI agents on top of a shared KPI layer, which can serve as a reference for other Mercedes-Benz sales markets in enabling self-service analytics for sales, product, finance, and marketing teams.

“Talk to Data” is rapidly becoming an important capability across industries, and delivering it at enterprise quality requires a strong semantic foundation. Answer reliability is highest when AI can draw on clearly governed business logic rather than inferring it from complex schemas, report-specific KPI logic, or disconnected dashboards. Consistent KPI definitions, aligned business logic, and well-defined joins and aggregations are what give executives the explainable answers they need. Mercedes-Benz Korea and Databricks approached this together. Rather than treating “Talk to Data” as a chatbot project, Mercedes-Benz Korea extended its existing analytics foundation with a governed semantic layer for enterprise AI. To enable semantics that can power both BI and AI, Mercedes-Benz Korea made KPI logic available in Unity Catalog Business Semantics in addition to Power BI. Drawing on Metric Views , Genie , and Agent Bricks on the Databricks Data Intelligence Platform , Mercedes-Benz Korea piloted a unified architecture for data, semantics, and agentic AI. Learnings from the Korea pilot can serve as a reference for other Mercedes-Benz markets. Mercedes-Benz Korea’s vision in “Talk to Data”: unified semantics for BI and AI Mercedes-Benz is a market leader in the high-end luxury automotive segment, operating a global sales network in which data-driven, market-specific decision-making is a continuous priority. “Talk to Data” self-service analytics is one capability being explored to further support this priority. Mercedes-Benz Korea has a mature data foundation. Over time, Mercedes-Benz Korea established gold-layer reporting data, a master KPI catalog, and shared definitions in the Lakehouse and Unity Catalog on Databricks. This foundation serves as the single source of truth for BI reporting, automation, and other data products, covering more than 500 KPIs across business domains such as sales, product, marketing, customer service, and finance. Given this foundation, Mercedes-Benz Korea was selected to pilot the “Talk to Data” approach. At the same time, a significant share of the business semantics at Mercedes-Benz Korea was defined in Power BI. As part of preparing for AI use cases, these definitions were complemented by an open, AI-ready semantic layer in the Lakehouse. The broader vision of Mercedes-Benz Korea for “Talk to Data” was to establish a unified, AI-ready, and governed semantic foundation for enterprise decision-making that can support reporting, self-service analytics, and AI experiences on a consistent set of business definitions. In line with this vision, Mercedes-Benz Korea did not approach “Talk to Data” as a migration away from Power BI, but pursued three key objectives: Consistent context for AI : The business logic and KPIs were already defined across two layers: in Power BI’s DAX language for reporting, and in curated silver and gold tables in the Lakehouse that were ready to be consumed by AI. The next step is to move the semantic context from BI reports to Unity Catalog to enrich the existing data products in the Lakehouse. This allows Genie and other AI agents to access all KPI definitions in one place, so that the same question, for example, “What’s our total retail sales MTD by vehicle class?”, yields consistent answers across AI experiences. Architecture evolving toward agentic AI : Mercedes-Benz Korea has a mature BI stack combining Databricks for data engineering and warehousing with Power BI for semantic modeling and reporting. The next step in this evolution is to extend it with a unified, AI-ready semantic layer based on the business logic from the BI reports, so that downstream BI tools and AI agents can operate on the same governed KPIs. From reporting users to persona-based agents : Governance for end users at the table and report level was already in place as part of the existing data infrastructure. The next step is to extend that governance with persona-based access control in Unity Catalog and orchestration rules for persona agents, so that roles like the CFO or Sales VP can have an agent experience tailored to their domains without changing the underlying business semantics.

Following these principles supports the goal that future AI and BI tools can consume the same validated business logic. This contributes to consistency, explainability, and the answer quality required for enterprise use. A unified architecture for data, semantics, and AI

To enable AI-ready semantics, Mercedes-Benz Korea implemented “Talk to Data” with a unified architecture on the Databricks Data Intelligence Platform , powering trusted AI at scale. The solution rests on different Databricks capabilities working together: Lakeflow and Lakehouse ingest enterprise data from various source systems and prepare it for BI and AI workloads. Unity Catalog business semantics serve as the single source of truth for KPIs, translating Power BI DAX measures into metric views: sources, joins, measures, dimensions, comments, and synonyms all live alongside the data, governed by the same permissions as the underlying tables. Genie spaces let business teams “talk” to their data. Genie spaces are organized by business domain, each backed by a curated set of metric views. As KPIs in metric views are defined directly on top of the gold-layer data, Genie doesn’t need guesswork or complex joins to...