How to roll out Genie One: A step-by-step enterprise playbook
Summary
Scaling Genie One from an initial demo to trusted enterprise adoption requires starting with a single, well-governed data domain. This step-by-step playbook outlines a phased plan to expand the data-smart AI coworker from a pilot team to org-wide adoption without losing control of your data.
Summary generated by brickster.ai. For the full article, follow the source link above.
More from Databricks Blog
How Databricks rolls out frontier models to 12,000 employees on Day 1
Databricks delivers Day 1 access to frontier AI models for all 12,000 employees to ensure cutting-edge capabilities are immediately available internally. See how the company prioritizes and operationalizes large-scale model rollouts across its entire workforce.
Lakebase Search: State-of-the-art full text and vector search for Postgres
Lakebase Postgres now includes a built-in search engine, generally available on AWS and Azure, that enables native semantic, keyword, and hybrid search directly alongside operational data without separate ETL pipelines. Featuring a serverless architecture that scales to zero and decouples storage from compute, it delivers twice the throughput at a quarter of the cost of cloud Postgres with pgvector on 100-million-vector benchmarks.
Manufacturing data and AI: Connecting the product value chain
Databricks connects fragmented manufacturing systems across functions and plants by unifying data or querying it in place to answer cross-stage value chain questions. Governed semantics, natural-language analytics, and agentic applications empower teams to move from finding data to taking action without becoming data engineers.
The 24 most commonly misunderstood marketing data terms
Aligning marketing and data engineering teams on 24 commonly misunderstood terms, including "customer," "audience," and "real time," prevents campaign rework before work begins. Establishing this shared business context around data sources, freshness, and ownership makes agreed definitions reusable across future campaigns, analytics, and AI workflows.
From Data to Dialogue: How S&P Global Energy Made Its Structured Data Estate Conversational with Databricks Genie Agents and MCP
S&P Global Energy made its structured data estate conversational by composing Databricks Genie Agents as managed MCP servers through a FastMCP proxy for cross-domain queries. This approach enables domain experts to curate code-free semantic layers, accelerating time-to-market for conversational data products while preserving Unity Catalog governance.
Running open-Jev in SQL on Databricks
Run models like SemIf-OpenJev directly on governed Databricks data using serverless GPUs and managed Model Serving. Query custom endpoints from SQL or Lakeflow jobs using ai_query to return structured decisions and probability scores at scale, with an importable notebook to deploy and adapt the workflow.