What Worked, What Didn’t: Virgin Atlantic’s Flight to Trusted Generative BI

The session lays out how they closed that gap by productizing trust. The core moves, treating metrics as governed products, grounding conversational answers in authoritative Unity Catalog metrics views, and making explainability visible to both analysts and leaders, mirror the thinking in the complete guide to business intelligence and analytics in the AI era. Organizations navigating this same shift can also find practical grounding in how leading companies are delivering trusted AI-powered self-service analytics, which traces the governance and productization patterns that make conversational BI decision grade.

When fast answers outpace confidence

Early prototypes proved the technology. Natural language questions returned results in seconds and adoption spiked. Confidence fell anyway, because business meaning, the subtle filters and shared definitions analysts rely on, did not surface in the AI's answers.

Governance, quality checks, and lineage were already strong, yet they did not translate into trust users could consume. The lesson was direct. Trust has to be made legible to AI and to decision makers in the moment of use, not just documented somewhere a specialist can find.

Turning metrics into governed products

Virgin Atlantic reframed metrics from ad hoc outputs to products with owners, lifecycles, and a single implementation. Each metric now carries one authoritative definition, drafted collaboratively, signed off by a senior business stakeholder and a contextual owner such as an analytics lead, then countersigned by the central analytics team. Definitions live as version controlled markdown in Git, and each one maps to exactly one Unity Catalog metrics view.

That change shifted debate. Conversations moved from "which number is right" to "why does a view differ from the authoritative metric." Generative BI could then ground to the same metrics layer analysts use, eliminating competing versions of the truth and reducing the need for verification loops.

Designing workflows for analysts and leaders that share one truth

Two personas, two experiences, one foundation. Analysts need to explore, validate, and refine. They land in a Databricks workspace with a start here notebook that functions like a 101 guide for each domain, certified metrics, join patterns, examples, and where to get answers. Leaders need to ask, understand, and act. They use certified Genie that queries the same governed metrics.

The shared base, governed definitions, visible lineage, and explainable answers, lets analysts dive deep and decision makers move quickly without drifting off the same numbers. Explainability is non negotiable. An answer no one can defend is not decision grade BI.

Operationalizing trust by design

Work backward from decisions. Define the metrics that drive them, then package the assets, Genie, Unity Catalog metrics views, and supporting tables, as an analytics product rather than a scatter of dashboards and tables. Treat updates as product lifecycle work, not one off edits.

Because governance patterns and definitions sit in version control, teams can cookie cutter new analytics products and scale faster. Analysts can be paired with agentic tooling such as Agent Bricks to generate much of the trusted scaffolding automatically, while the governance-by-design pattern preserves confidence as speed increases.

Make AI answerable, then make it fast

Trusted generative BI does not come from wiring AI to data. It comes from wiring AI to analytics products that carry definitions, ownership, lineage, and explainability with them. Organizations that scale AI driven decisions will productize trust before they productize speed.