Short answer: The leading conversational BI tools in 2026 are Colrows, ThoughtSpot Spotter, Power BI Copilot, Tableau Next and Pulse, and Looker. The field also includes Snowflake Cortex Analyst, Databricks Genie, Amazon Quick, Qlik Answers, Omni and Sigma. Most are bound to one platform and govern at query time. Pick the native assistant if you run one BI suite or warehouse. For regulated or cross-warehouse use, require deterministic SQL with governance enforced before execution, which Colrows provides.
Platform-bound conversational BI vs governed conversational analytics
| Dimension | Typical conversational BI assistant | Governed conversational analytics (Colrows) |
|---|---|---|
| Reach | Bound to one BI suite or warehouse | 16+ engines, one governed graph |
| Governance | Applied at query time, platform-native | Compile-time; unauthorized plans cannot be generated |
| Determinism | Nondeterministic; answers can vary | Deterministic; same question, same answer |
| Audit | Partial query logs | Point-in-time reproducible audit trail |
What changed in conversational BI tools in 2026?
Five shifts reshaped the category between January and October 2026. None of them closed the governance gap.
- Chat moved from the report to the tenant. Power BI now offers a standalone Copilot, in preview, that answers across every report and semantic model you can open. Databricks folded its business-user assistant into Genie One. The assistant no longer waits inside one dashboard.
- Agent modes arrived. Databricks made Agent mode generally available in Genie on 2 July 2026. ThoughtSpot introduced Spotter 3 for multi-step analysis across structured and unstructured data. One question can now trigger a plan of several queries. Our agentic BI tools ladder shows how far each vendor actually goes.
- MCP servers became standard. ThoughtSpot, Qlik and Databricks now expose their analytics to outside assistants through Model Context Protocol servers. Your conversational BI tool may answer inside Claude, ChatGPT or Gemini instead of its own window. We compare them in MCP servers for analytics and BI.
- First-generation natural language query is retiring. Microsoft says Power BI Q&A goes away in February 2027. ThoughtSpot removed Sage in its June 2026 release. Copilot and Spotter replace them.
- The semantic model became the gate. Power BI warns users when a semantic model is not marked "approved for Copilot". Looker grounds every answer in LookML. ThoughtSpot launched Spotter Semantics in March 2026. The vendors now agree that an assistant is only as good as the definitions under it.
Each shift makes the assistant reach further. None of them makes the answer reproducible. A multi-step agent that runs five nondeterministic queries multiplies the variance. The scorecard below therefore still weights governance timing and determinism above features.
How we scored conversational analytics tools
We score each tool on the factors that decide enterprise fit. The scores are directional. They come from each vendor's documented architecture and release notes, not from a lab benchmark.
- Governance timing. Does the tool stop an unauthorized query before it is generated, or does it filter rows when the query runs? Compile-time beats run-time, because a plan that cannot be generated cannot leak. See data governance tools for AI agents.
- Determinism. Does the same question return the same SQL and the same number twice? A tool that lets a language model write SQL on every request scores Medium at best. See semantic layer vs text-to-SQL.
- Reach. How many engines and platforms can one question span without a copy of the data?
- Audit. Can you reproduce last quarter's answer as it was given? See point-in-time query reproducibility.
The scorecard covers the first three factors for every tool. Audit appears in the north star table above and in the trial tests further down.
The scorecard
Scored on the enterprise factors above. Directional, not lab numbers.
| Tool | Governance timing | Determinism | Reach | Best for |
|---|---|---|---|---|
| Colrows | Before execution | High | 16+ engines | Governed, cross-warehouse agents |
| ThoughtSpot Spotter | Platform | Medium | Multi-cloud | Search-driven self-service at scale |
| Power BI Copilot | At query time | Medium | Microsoft ecosystem | Microsoft-first shops |
| Tableau Next and Pulse | At query time | Medium | Tableau Cloud | Metric monitoring for Tableau users |
| Looker Conversational Analytics | At query time (LookML and Looker permissions) | Medium | Looker Explores, up to five per question | Looker and Google Cloud shops |
| Cortex Analyst | At execution (RBAC) | Medium | Snowflake only | Snowflake-native self-serve |
| Databricks Genie | At execution (Unity Catalog) | Medium | Databricks only, 50 tables per agent | Lakehouse-native BI |
| Amazon Quick | At execution (row- and column-level security) | Medium | AWS data sources | AWS-first teams |
| Qlik Answers | Platform (Qlik app security) | Medium | Qlik analytics apps | Existing Qlik estates |
| Omni | Platform (semantic layer permissions) | Medium | Warehouses Omni connects to | dbt-centric BI teams |
| Sigma | Warehouse-native | Medium | Snowflake/Databricks/BigQuery/Postgres | Governed spreadsheet exploration |
Fix the Context, Not the Model. Every tool here uses a capable model. What separates them is the context and governance around the model, not the model itself.
The tools, by job to be done
1. Colrows - governed, deterministic, cross-warehouse
Colrows compiles questions into deterministic SQL across 16+ engines with governance enforced before execution. Best when answers must reproduce and span more than one platform, especially in regulated settings.
The compile-then-execute pipeline resolves each question against a versioned semantic graph and attaches a join path proof. Compile-time governance (RBAC, ABAC and row- and column-level predicates) applies before any SQL exists. Every answer is point-in-time reproducible. Watch for: Colrows sits above your BI and warehouses, so you keep a dashboard tool for pixel-perfect reporting. See how Colrows works and the AI Data Analyst documentation.
2. ThoughtSpot Spotter - search-driven self-service
ThoughtSpot Spotter pairs a search-token architecture with an agentic semantic layer for thousands of business users across clouds.
ThoughtSpot's February 2026 release notes introduced Spotter 3, an agent for multi-step analysis across structured and unstructured data, first for selected customers. Spotter Semantics followed in March. The June 2026 release removed the older Sage experience and set a 180-day limit on Spotter conversation history. Watch for: confirm which Spotter agents your licence includes, and price them in ThoughtSpot pricing.
3. Power BI Copilot - Microsoft-first
Power BI Copilot is the natural pick inside Microsoft. Watch determinism; see why Copilot gives wrong answers.
The standalone Copilot experience, in preview, finds the right report or semantic model for a question across your whole tenant. When a report does not hold the answer, Microsoft's documentation says Copilot can create new DAX calculations. Models that authors have not marked "approved for Copilot" carry a warning that answer quality could be low. Watch for: generated DAX is new logic that no one reviewed, and Copilot needs a paid Fabric capacity. Compare the costs in Power BI Copilot pricing.
4. Tableau Next and Pulse - metric monitoring for Tableau
Tableau Pulse is a metric-insight feed inside Tableau Cloud. Great for following metrics, bounded to one source per metric.
Tableau's July 2026 release made Tableau Agent conversational analytics enhancements generally available in Tableau Next. Tableau Agent in Pulse, formerly Enhanced Q&A, became generally available in the Tableau Cloud+ Edition and the Tableau+ Bundle. Conversational analytics on dashboards was still in preview. Watch for: the newest features sit in the top editions. We set Pulse against Copilot in Power BI Copilot vs Tableau Pulse.
5. Looker Conversational Analytics - LookML-grounded
Google made Conversational Analytics generally available for all Looker platform users in November 2025. Looker grounds each answer in LookML, and one question can draw on up to five Looker Explores. A "How was this calculated?" control explains the underlying query in plain language. Analysts can share agents that fix common questions, filters and style rules for a team.
Watch for: reach ends at the Explores your LookML model defines, and each answer is still generated per question. See Looker alternatives and Looker pricing.
6. Snowflake Cortex Analyst - Snowflake-native
Cortex Analyst is fast and low-friction for Snowflake-only estates. Cortex now reads Snowflake Semantic Views in place of the older semantic models. Cortex Analyst also powers Snowflake Intelligence, which reached general availability in November 2025. Snowflake renamed it Snowflake CoWork at Summit 2026.
For budgeting, Snowflake is moving AI consumption to a separate AI Credit currency ($2.00 per credit global, $2.20 regional, roughly 67 credits per 1,000 messages). The transition was underway in April 2026, so check your contract for the date it applies to you. Watch for: definitions and answers stay inside Snowflake. See every Snowflake layer in Snowflake AI tools compared.
7. Databricks Genie - lakehouse-native
Databricks Genie inherits Unity Catalog governance. Databricks renamed its business-user assistant Genie One in June 2026, and Genie Spaces became Genie Agents in July. Agent mode, which plans a multi-step analysis and returns a cited report, became generally available on 2 July 2026. Since 10 September each Genie Agent accepts up to 50 tables, views or metric views, up from 30.
Its semantic substrate, Unity Catalog Metric Views, reached GA on 2 April 2026 and was open-sourced into Apache Spark. Databricks has extended free Genie One and Genie Agents usage through 31 January 2027. Watch for: the definitions stay Databricks-only, and the free period ends. Compare the full stack in Databricks AI and BI tools compared.
8. Amazon Quick - AWS-native
Amazon QuickSight grew into Amazon Quick, the AWS agentic workspace. Its Dataset Q&A feature became generally available on 4 May 2026 in every Region where Quick runs. An internal text-to-SQL agent writes engine- and dialect-aware SQL against AWS data sources. Answers respect row- and column-level security, and an Explain view steps through the reasoning so a user can check the SQL.
Watch for: the SQL is written fresh for each question, so the same question can take a different route. Our text-to-SQL accuracy analysis shows why that matters on real schemas.
9. Qlik Answers - for Qlik estates
Qlik made its agentic Qlik Answers experience and a Qlik MCP server generally available in Qlik Cloud in February 2026. Agents answer structured questions over Qlik analytic apps and unstructured questions over curated documents, with citations and an explanation of the reasoning. Qlik currently charges nothing extra for structured data questions.
Watch for: answers stay inside the Qlik apps you publish to it, and Qlik's documentation says structured answering requires cross-region data processing. See Qlik Sense alternatives.
10. Omni - semantic-layer-first BI
Omni is a BI and embedded analytics platform with a built-in semantic layer. Its AI chat, agent skills and Slack integration sit on that layer, with two-way dbt support to stop metric drift. Omni raised a $120M Series C in April 2026.
Watch for: answers reach only what your Omni model covers, and the query is still generated per question. We score the semantic layers themselves in best semantic layer tools.
11. Sigma - governed spreadsheet exploration
Sigma connects live to major warehouses with an "Ask Sigma" agent that respects warehouse roles and row-level security.
Watch for: Sigma inherits the governance of the warehouse under it, so it is only as consistent as the models in that warehouse. Teams that embed analytics in their own product should also read headless BI tools.
How do conversational analytics tools charge?
Four pricing shapes dominate the category. They behave differently once usage grows past a pilot.
| Pricing shape | Tools | What grows the bill |
|---|---|---|
| Capacity or edition licence | Power BI Copilot (Fabric capacity), Tableau Cloud+ and Tableau+, ThoughtSpot, Looker | Users, capacity tier and edition upgrades |
| Consumption credits | Snowflake Cortex Analyst (AI Credits) | Message volume |
| Pay-as-you-go compute, free for now | Databricks Genie (free through 31 January 2027) | DBUs once the promotion ends |
| Included for now | Qlik Answers structured questions | Future metering decisions |
| Metered on questions | Colrows | One committed annual figure, metered only on the questions people and agents ask, with unlimited users |
Promotional pricing hides the steady-state cost. Model the bill at full adoption, not at pilot scale. Our semantic layer pricing comparison shows how seat, capacity and credit models diverge, and the Colrows pricing page explains the question-based meter.
How does conversational BI differ from conversational analytics?
The two names get used for the same software, and the difference is worth stating, because it changes which tool fits.
Conversational BI tools put a chat box on an existing business intelligence stack. Power BI Copilot, Tableau Pulse, and ThoughtSpot Spotter are all this shape. The assistant inherits that platform's semantic model, its permissions, and its boundary. Ask about data the platform does not hold, and it cannot answer.
Conversational analytics tools is the broader term. It covers the same chat interface. It also covers warehouse-native assistants such as Snowflake Cortex Analyst and Databricks Genie, which answer over the warehouse rather than a BI semantic model. The interface defines the category, not the stack underneath.
That breadth is exactly why the term is a poor buying filter on its own. Two products can carry the same conversational analytics tools label and still differ on what decides enterprise fit. Does governance run before or after the query? Does the same question return the same number twice? Can the tool reach data outside its home platform? Those are the three axes the scorecard above scores, and they matter more than which label a vendor uses.
Five tests to run before you buy conversational BI
Vendor demos use clean questions on clean data. Run these five tests on your own data during the trial instead.
- Same question, twice. Ask one revenue question on Monday and again on Friday, with no data change in between. Compare the generated SQL and the number.
- Two phrasings. Ask for "Q3 revenue" and then for "revenue from July to September". A governed tool returns one number for both.
- The restricted user. Sign in as a user who cannot see one region, and ask about that region. Note whether the tool refuses before it writes a query or returns a silently filtered result.
- The cross-source join. Ask a question that needs the warehouse and a second system, such as CRM data. Note where the tool stops.
- Last quarter's replay. Ask the tool to reproduce an answer it gave 90 days ago, with the definitions that applied then.
Few tools pass all five. Colrows is built to pass them by design: deterministic compilation, compile-time governance, a join path proof for every query, and a point-in-time reproducible audit trail. See auditable SQL for conversational analytics for how a bank applies these tests.
How to choose
- Single BI suite or warehouse, want the native assistant: Copilot, Tableau Next and Pulse, Looker, Cortex, or Genie.
- AWS or Qlik estate: Amazon Quick or Qlik Answers, inside that estate.
- dbt-centric BI team: Omni or Sigma.
- Search-driven self-service for thousands: ThoughtSpot Spotter.
- Deterministic, governed answers across warehouses, especially regulated: evaluate Colrows.
- Betting on portable definitions: track Open Semantic Interchange, which entered the Apache Incubator as Apache Ossie in June 2026 - but treat cross-vendor portability as a 2027 question, not a 2026 one.
Before shortlisting, run the semantic layer evaluation checklist against each candidate, and price the ones you keep against Power BI Copilot pricing and ThoughtSpot pricing - per-seat and per-capacity models diverge sharply once usage scales.
The same field has since rebranded. Agentic BI tools places these vendors on a five step autonomy ladder, and most of them stop at level 2.
For the wider field, AI data analyst tools scores these vendors on what they can actually prove, and not one of them has submitted to a public benchmark.
Frequently asked questions
What is conversational BI?
Conversational BI lets business users ask questions of their data in plain language and get governed answers, charts, or insights, without writing SQL or building dashboards. It is the natural-language front end to analytics.
What is the main weakness of most conversational BI tools?
Most are bounded to one platform (their own BI suite or warehouse), govern data at query time rather than before generation, and are nondeterministic, so the same question can produce different SQL and different numbers. That is a problem for regulated or cross-warehouse use.
Which conversational BI approach is best for regulated enterprises?
A compile-time semantic execution layer that produces deterministic SQL, enforces governance before execution, and spans warehouses. Colrows is built for this; platform-native assistants like Cortex Analyst, Genie, Copilot, and Pulse are strong inside their own ecosystems.
What is the difference between conversational BI and conversational analytics tools?
Conversational BI tools add a chat interface to an existing business intelligence platform, so they inherit that platform's semantic model and its boundary. Conversational analytics tools is the wider term, and it also covers warehouse-native assistants that answer over the warehouse directly. The label does not tell you how the tool governs access or whether its answers are reproducible.
Which conversational BI tools changed most in 2026?
Databricks and Microsoft moved fastest. Databricks renamed Genie to Genie One, made Agent mode generally available in July 2026 and raised the limit to 50 tables per agent. Microsoft added a standalone Copilot that answers across reports and set February 2027 for retiring Power BI Q&A. ThoughtSpot, Qlik and Tableau shipped agents or MCP servers.
Is Power BI Q&A being retired?
Yes. Microsoft's documentation says Q&A experiences go away in February 2027 and recommends Copilot for Power BI instead. Copilot works best on semantic models that their authors mark as approved for Copilot. Models without that approval show a warning that the answer quality could be low.
How do I test whether a conversational analytics tool is deterministic?
Ask the same question twice with no data change, then ask it in two different phrasings. Compare the generated SQL and the returned number each time. A deterministic tool returns identical SQL and an identical number. Equivalent answers most of the time is not the same as identical answers every time.
A note on the claims
Vendor capabilities come from each vendor's own documentation, release notes and announcements:
- Microsoft Learn: standalone Copilot in Power BI (updated August 2026) and Power BI Q&A (updated September 2026)
- Databricks AI/BI and Genie One release notes for 2026
- Tableau's July 2026 feature release
- Google Cloud's Looker Conversational Analytics announcement (November 2025)
- The AWS Dataset Q&A announcement (4 May 2026)
- Qlik's agentic experience announcement (February 2026)
- ThoughtSpot's February and June 2026 release notes
- Omni's Series C announcement (April 2026)
Preview and beta status changes quickly in this category. Scores are our assessment against the factors on this page, not a vendor rating. Colrows sells a competing product, and we wrote this page with that disclosed.
Correction, 1 Nov 2026: An earlier version said Databricks Metric Views could not nest MEASURE() inside an aggregate. Databricks documents composable measures as of August 2026. The earlier version also capped Genie at 30 tables per space. The limit is 50 per agent since 10 September 2026. Finally, it dated Snowflake's AI Credit billing to 1 April 2026, a date that only third-party sources support.
