From dashboards you read to a system you talk to.
The dashboard you open and interpret is giving way to a system you ask in plain language, one that drafts the views you need, watches the numbers, and repairs its own pipelines. It builds, explains, and heals itself. We call it Generative BI.
From dashboards to dialogue.
For twenty years the dashboard was the destination: someone gathered requirements, a team built the report, everyone else learned to read it. It worked, but it was slow, it aged, and it answered only the questions someone thought to ask in advance.
The emerging model is a dialogue. You ask the way you'd ask a colleague, and the answer comes back grounded in governed definitions, with the work shown. Behind it sits an agentic spine: cooperating agents that read the same definitions, draft the views, run the checks, and fix routine breakages before anyone is paged. The interface gets simpler precisely because the system underneath does more.
This isn't a single feature. It's a direction of travel, one that touches everyone who consumes insight, everyone who builds the data, and the foundations that make the answers worth trusting.
Three ways the ground is moving.
Generative BI changes the day-to-day for the people who consume insight and the people who build the data, and rests on a few foundations that make the whole thing trustworthy.
For end users
For the people who consume insight, BI stops being a dashboard you read and becomes a system you talk to.
Chat with your data · Reports that design themselves · Insights that surface themselves · Alerts that find the right person
For data teams
For the people who build and run the data, the toil of pipelines and tickets gives way to agents that draft, test, and heal.
Self-healing pipelines · A data-quality copilot · Natural-language writeback · Business users in the warehouse, safely · Add a source by asking
Building blocks
What makes any of this trustworthy: the semantic layer, the company memory, and the governance that lets an agent say "I do not know".
The semantic layer · A company-wide memory · Trust and governance
For end users
the people who consume insightFor data teams
the people who build and run the dataThree things changed at once.
None of this would have been credible a few years ago. Three shifts arrived close together and reinforced each other.
Natural language became the primary entry point. Large language models are now good enough that asking in plain words is a realistic front door to analytics, not a demo. People don't want to learn a query language or hunt through a folder of reports; they want to ask.
Self-healing pipelines moved toward baseline. Agents that detect schema drift, retry transient faults, and diagnose routine failures are turning data engineering from constant firefighting into exception handling. The novel problems still need people; the repetitive ones increasingly don't.
The semantic layer made answers trustworthy. The single biggest lever for accuracy is grounding an AI in shared, governed definitions of your metrics and entities. Pointed at raw schema, a model guesses; grounded in a semantic layer, it answers from definitions your business already agreed on. That difference turns a clever assistant into something you can act on.
Let's talk about the next move.
If dashboards-to-dialogue is the direction you want to take, we should compare notes. No pitch, just a conversation about where your BI is going.