Here’s a number worth sitting with: 86% of enterprises run two or more BI platforms. Not because they planned it that way. But because Finance needed Power BI, Operations trusted Tableau, and Sales already had something else.
The result?
We built sprawl trying to democratize data. And now the sprawl is what’s slowing decisions down.
Business users have more dashboards than ever. What they don’t have is the ability to move from “something looks off” to “here’s what we’re doing about it” without filing out a BI ticket.
A supply chain planner spots a MAPE spike in demand forecasts. To understand whether it’s a seasonality shift, a data quality issue, or a model degradation problem, they need three different reports, two teams, and then to wait. By the time the answer arrives, the window to act has narrowed or closed.
This isn’t a data problem. It’s a decision velocity problem.
What actually needs to change?
The instinct is to add a chatbot on top of existing reports. That’s the wrong fix.
The reason AI-assisted analytics fails in most enterprises isn’t the model. It’s that there’s no shared definition of revenue, margin, forecast accuracy, or churn underneath it. When different dashboards calculate the same KPI differently, a natural language interface just returns confident, wrong answers faster.
The fix starts at the semantic layer, not the interface layer.
Databricks Metric Views let you define KPIs once at the data layer and make them consistently available across dashboards, AI models, and conversational analytics. Not per-report calculated fields that drift over time, but rather a single, governed, reusable definition that every downstream consumer inherits.
Pair that with Unity Catalog for lineage, access control, and metadata governance, and you have a foundation where AI-generated answers are actually trustworthy because the data they’re grounded in is governed and consistent.
Genie: Conversational analytics that actually works at enterprise scale
Once the semantic foundation is solid, Databricks Genie changes the interaction model entirely.
Instead of navigating to a dashboard and hoping the right filter exists, a business user asks: “Which customer segments drove the increase in churn last week, and how does that compare to the prior quarter?” Genie generates the answer grounded in Metric Views, not by hallucinating SQL from scratch, but by working against a governed semantic context you’ve defined.
This matters for two reasons:
- It bypasses the BI queue for exploratory questions. Analysts stop being report-builders and start being decision architects.
- It’s not open-ended. Genie Spaces are scoped to specific domains — demand forecasting, supply chain health, customer analytics — so users get focused, accurate answers rather than an open SQL interface to the entire lakehouse.
Not all dashboards should become Genie Spaces. That’s where next-gen rationalization comes in
This is one of the most overlooked problems in BI modernization: organizations treat migration as a binary lift-and-shift everything to a new tool, or rewrite everything from scratch. Both are wrong.
The real question is: for each dashboard in your estate, should it stay a structured dashboard, or should it become a Genie Space where users ask questions in natural language? The answer depends on how the dashboard is actually used.
Our Next-Gen Rationalization engine inside EzConvertBI makes this decision systematically using six classification strategies applied against actual usage telemetry:
The output is a migration recommendation per asset: keep as AI/BI Dashboard, convert to Genie Space, consolidate with a cluster, or retire. No guessing. No manual spreadsheets. A system-driven decision backed by actual usage data from your BI environment.
The KPI inconsistency problem doesn’t solve itself during migration
Most migrations move dashboards. Almost none deal with the hardest problem underneath them: the same KPI defined 5 different ways across 5 different reports.
EZConvertBI surfaces and resolves this through a built-in KPI Dispute Resolution workflow flagging conflicts like:
- Same metric name, different formula
- Same formula, different aggregation grain,
- Same formula, different source table
- Letting business owners choose the canonical definition, adopt an AI-recommended industry standard, or author a custom one.
The resolved definition goes directly into a Databricks Metric View, so it becomes the single source of truth for every downstream consumer: dashboards, Genie, and AI agents alike.
Without this step, you migrate the dashboards but inherit the inconsistency. AI answers stay untrustworthy. Teams keep arguing about numbers.
AI/BI dashboards: Structured monitoring + self-service in one layer
Genie handles the exploratory layer. Databricks AI/BI Dashboards handle the structured monitoring layer, and the two work together natively.
A logistics manager tracking fulfillment performance can monitor supplier delays and stockout risk on an AI/BI Dashboard, then pivot into a Genie Space to ask “which suppliers are most at risk given current lead-time signals?” without switching tools, exporting data, or raising a ticket.
Structured views for monitoring. Conversational exploration for investigation. One governed data layer underneath both.
Most enterprises don’t start from a clean slate. They start with 150–400 dashboards across Tableau and Power BI, metric definitions that vary by team, and no automated way to understand which reports are actually used. EzConvertBI takes the BI migration from a manual, high-risk project to a structured, AI-assisted process (assessment, migration, diagnostics) with human-in-the-loop controls at every decision point.
The bottom line
The competitive advantage in 2026 isn’t who has the most dashboards, the most AI features, or even the most data. It’s decision velocity, how fast teams can move from signal to action while the context is still relevant.
That starts with getting the semantic foundation right: Metric Views for consistent KPIs, Unity Catalog for governance, Genie for exploration, AI/BI Dashboards for monitoring. And it requires retiring not just migrating the dashboards that are creating noise, not signal. With Next-Gen Rationalization, that decision is systematic and data-driven, not a guess.
More BI doesn’t democratize decisions. The right BI governed, semantic, rationalized, and AI-ready does.
09 Jun 2026
6 min read
