The news isn’t just that Databricks has appeared in Gartner’s Magic Quadrant for analytics and business intelligence platforms. The interesting takeaway is that, according to the chart dated May 2026, it is entering for the first time and directly positioned in the Visionaries quadrant, alongside names that have been competing in enterprise BI for many years.
This move confirms something that has been developing for some time: the boundary between data platform, lakehouse, BI, data governance, and conversational AI is blurring. BI is no longer just about creating dashboards, defining metrics, and publishing reports. Increasingly, companies expect the system to understand questions in natural language, respect the semantic model, generate visualizations, explain results, and enable action on data without forcing business users to write SQL.
In the Magic Quadrant image, the leaders are Microsoft, Amazon Web Services, Salesforce (Tableau), Google, Qlik, and ThoughtSpot. Databricks joins the Visionaries, close to players like SAP, Oracle, Strategy, SAS, IBM, GoodData, and Pyramid Analytics. Snowflake is not within the quadrant but is listed as Honorable Mention, a detail worth noting.
Databricks no longer wants to be just the data teams’ platform
For years, Databricks was mainly perceived as a platform for data engineering, data science, machine learning, and lakehouse. Its natural position was closer to the technical team than to the business user. That image has shifted.
With AI/BI, dashboards on the lakehouse, Genie, and the evolution of its Data Intelligence Platform, Databricks is trying to enter the traditional BI space without behaving like a classic BI tool. Its approach starts from governed data, the lakehouse, and AI within a common enterprise context, not from the presentation layer.
Databricks already gained recognition in other Gartner markets. In 2024, it was named a leader for the fourth consecutive year in the Magic Quadrant for Cloud Database Management Systems, highlighting releases related to AI/BI, dashboards, Genie spaces, AI functions, assisted SQL, and data governance within its platform.
This history helps explain why its entry into analytics and BI matters. It arrives not as a vendor that built reporting tools from scratch but as a data platform aiming to bring BI directly to where the data, models, permissions, tables, catalogs, and AI workflows already live.
Gartner Peer Insights describes Databricks Data Intelligence Platform as a solution that unify data loads, analytics, and AI, with capabilities for data warehousing, lakehouse integration, automated workflows, and governance. This combination supports Databricks’ thesis: if a company already governs its data and models on a single platform, then BI with AI should originate there, not as a separate layer.
Genie, dashboards, and the new conversational BI
The product best symbolizing this transition is Genie. The idea isn’t just “ask a dashboard,” but to offer an experience where users can converse with data, leveraging semantics, permissions, and enterprise context. Throughout 2026, Databricks has added capabilities to AI/BI and Genie One, including improved content search in dashboards, chart faceting, hierarchies in pivot tables, and advances in agent mode.
This shifts the role of dashboards. For years, dashboards were the endpoint of BI: designed by analysts, published, and consumed by others. In the new model, dashboards can become just another interface within a conversational analytical experience. Users can look at a chart, inquire about a deviation, request a breakdown, generate a new visualization, or explore a metric without waiting for the data team to publish a new version.
The challenge lies in reliability. BI with AI cannot invent metrics or give answers with a pretty but erroneous interpretation. It needs a solid semantic model, permission controls, traceability, human review in certain cases, and a clear way to differentiate data, inference, and explanations. The value isn’t only in natural language questions but in governed and justifiable answers.
Here, Databricks could have an advantage if its semantic layer, Unity Catalog, lakehouse, and AI/BI work as a cohesive experience. But there’s also an obvious obstacle: enterprise BI is full of habits, legacy reports, users accustomed to Power BI, Tableau, Qlik, or Looker, and teams that don’t migrate processes just because a tool is more modern.
Gartner shouldn’t be read as an absolute verdict. The legal note Databricks reproduces in communications reminds that Gartner doesn’t recommend choosing providers solely based on their position in a Magic Quadrant, and that their reports reflect research opinions, not absolute facts.
Snowflake isn’t in the quadrant, but its move could be very serious
Snowflake not being within the quadrant and being listed as Honorable Mention can be interpreted in two ways. A superficial reading might think it arrives late to BI. A more interesting interpretation is that Snowflake is building another entry point: not just a closed BI platform, but a foundation for creating AI-driven analytical applications on governed data.
The combination of Streamlit, Cortex Analyst, Cortex Agents, and shared semantic models can be very powerful for companies seeking more than dashboards—a suite of AI-powered business apps. Cortex Analyst enables creating applications that answer business questions in natural language using Snowflake data, generating and executing SQL within Snowflake’s engine.
Snowflake emphasizes the role of the semantic model. It’s not enough to deploy an LLM against a table schema. The system needs business concepts, metrics, synonyms, and relationships to translate user questions into reliable queries. Snowflake states that Cortex Analyst uses Semantic Views to bridge the gap between how the business thinks and how data is stored.
Furthermore, Cortex Analyst’s API-first approach allows integration with Streamlit, Slack, Teams, or custom interfaces. This is significant because AI BI may not resemble a single licensed tool but rather many small embedded applications within specific processes: sales, finance, operations, support, risk, marketing, or procurement.
The difference between Databricks and Snowflake may lie in how they reach the business user. Databricks seems to promote a more integrated AI/BI experience within its lakehouse platform. Snowflake appears to be developing a model where data teams and developers build conversational apps using Cortex, Streamlit, and shared semantics. Neither approach is closed.
| Vendor | Main move in AI-powered BI | Obvious risk |
|---|---|---|
| Databricks | Bringing dashboards, Genie, and conversational analytics to the governed lakehouse | Convincing business users accustomed to traditional BI tools |
| Snowflake | Building analytical applications with Cortex, Streamlit, and semantic models | Transforming a powerful foundation into a clear, mass-market BI experience |
| Microsoft | Integrating Power BI, Fabric, and Copilot into a large installed base | Managing complexity and avoiding excessive ecosystem dependence |
| Tableau / Salesforce | Connecting visual BI with CRM, enterprise data, and AI | Modernizing the experience without losing their established customer base |
| Google / Looker | Exploiting semantic layer, cloud, and Gemini | Gaining traction outside the native cloud clients |
The race has just begun because the market hasn’t yet decided what “BI with AI” will look like in five years. It could be an evolution of dashboards, a conversational layer over governed metrics, an agent that creates analysis, alerts, recommends, and executes actions, or a combination of all these.
What’s clear is that the traditional model—users waiting for static reports or requesting changes from data teams—is starting to fall short. Companies want faster answers but aren’t willing to sacrifice governance, security, and consistency. The provider who can resolve this tension will gain far more than just a position in a Magic Quadrant.
Databricks has entered the BI conversation from the AI and lakehouse side. Snowflake, though not yet in the main quadrant, has technical pieces to build a highly competitive proposal. Microsoft maintains a distribution advantage. Tableau retains a large installed base. Google, Qlik, and ThoughtSpot have been pushing self-service analytics for years.
Next year’s question won’t just be who rises or falls in Gartner. It will be which platform manages to earn the trust of CFOs, operations managers, or sales teams for answers generated by AI as much as they trust a reviewed report today. That’s where the new stage of BI will be decided.
Frequently Asked Questions
Why is it relevant that Databricks appears as a Visionary?
Because it shows Gartner now considers it part of the analytics and BI market, not just a data engineering, lakehouse, or machine learning platform.
Can Databricks reach the Leaders quadrant?
Potentially, but it will depend on its ability to demonstrate enterprise adoption, commercial execution, business user experience, governance, and maturity compared to established BI providers.
Is Snowflake out of the AI BI race?
No. Although it appears as Honorable Mention, its combination of Cortex Analyst, Cortex Agents, Streamlit, and semantic models could form a very competitive pathway for AI-driven analytic applications.
What differentiates AI BI from traditional BI?
Traditional BI relies on reports, dashboards, and prepared queries. AI BI adds natural language, assisted SQL generation, explanation of results, agents, and governed enterprise semantics.
via: LinkedIN
