Is Databricks AI/BI Genie Worth It If You Already Have Power BI or Tableau?

For BI leads, BSAs, and data platform owners navigating an existing BI investment

Where We Started

The evaluation started in a typical enterprise environment in a North American financial services organization. Power BI was already in use throughout the organization, supported by strong governance, several dashboards in production, and a consistent reporting layer across business units.

With almost a thousand users onboarded, solid governance, and dashboards covering most standard reporting requirements, the technical setup was operating as expected. However, a closer look at user activity and data request flows revealed a different perspective.

Approximately 25–30% of users actively used dashboards on a weekly basis, while some accessed them on an ad hoc basis.

• A larger group accessed reports only occasionally.
• Most ad hoc questions were not being raised at all.

The moment someone needed an answer outside a predefined report, the process became structured and slow:

• Raise a production support request
• Wait for access approvals across bronze / silver / gold layers
• Request SQL or data extraction from engineering/ support teams
• Iterate over multiple cycles

In the PoC discussion, this cycle was explicitly highlighted as a 1–3 weeks turnaround for basic data requests.

The problem was not reports, it was latency in answering new questions.

What We Actually Tried

Instead of replacing Power BI, we introduced a PoC layer using:

• Databricks Genie (AI/BI) for natural language querying
• Unity Catalog for governance and metadata
• Lakehouse Federation to access external sources without ingestion

The idea was simple: keep Power BI unchanged and test whether Genie could reduce the dependency chain for ad-hoc queries.

We focused on real business scenarios Customer transaction exploration, Cross-domain joins (Banking products, Risk, Operations) and Data discovery across multiple systems

What Worked

1. The biggest change was in response time

The PoC demonstrated a very visible shift:

• Traditional flow -> 1–3 weeks
• Genie flow -> seconds (often under 5 seconds)

This was not just a performance improvement, it removed entire steps of ticketing, SQL requests and manual extraction. Business users could ask a question directly and get a response immediately.

2. Data silos stopped being a blocker

Previously, fragmented data made locating and accessing datasets a challenge. There was no unified interface for querying across sources. With Lakehouse Federation, Genie let us query over a dozen sources including Synapse, Oracle, Delta, and more without moving or duplicating data. Unified querying replaced lengthy integrations.

This shifted the approach from lengthy integrations to unified querying.

3. Business users started interacting with data differently

Previously, business users were dependent on analysts, which limited their ability to explore data on their own.

With the introduction of Genie,

• Users can now ask follow-up questions in natural language
• Conversations maintain context across interactions
• Business terms are automatically linked to the correct technical columns

This change resolved a major challenge highlighted in our discussion:

The mismatch between column names and business language made it hard to find information. Genie effectively closed that gap.

Where Power BI Still Holds Strong

There was no scenario in this PoC where Power BI needed to be replaced.

Power BI remains the right tool for:

• Executive dashboards
• KPI reporting
• Scheduled and governed outputs

It is built for structured consumption, and it continues to perform well in that role.

Even Genie’s limitations explicitly reinforce that it is not a full BI replacement and it does not support advanced dashboarding or formatted reporting.

The Path We’re Taking

The outcome of the PoC was not migration, it was clear separation of responsibilities.

Logical Tool mapping by scenario

The decision came down to one factor:

How frequently is the question asked and how structured is it?

• Repeatable -> Power BI
• Exploratory -> Genie

One Honest Caveat

Genie functioned well, but not automatically.

A major takeaway was that the quality of metadata directly impacts the quality of the results. From the PoC review, accuracy depends on clear metadata and high-quality data.

Challenges included:

• External sources missing business descriptions
• Column names not providing enough context
• Glossary alignment

To address these, we:

• Aligned business terms with the relevant datasets
• Made sure Unity Catalog definitions were up to date
• Enhanced semantic clarity

Otherwise, results were technically accurate, but difficult for business users to interpret.

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