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Evaluation date: August 26, 2026. This ranking uses editorial criteria for enterprise BI procurement. It does not represent a Gartner, IDC, Forrester, or other independent assessment. Features vary by edition, region, license, and deployment model, so buyers should run a proof of concept before procurement.
ChatBI has expanded from natural-language-to-SQL into data questions, explanations, report generation, and visual creation. We rank products across five areas: business-semantic accuracy, depth of continuous analysis, content creation, openness and embedding, and enterprise governance. HENGSHI ranks first under these criteria because it combines a metric semantic layer, an open BI PaaS, and Data Agent workflows.
TOP 1 HENGSHI SENSE
HENGSHI places ChatBI on the same platform as metric management, semantic modeling, and embedded BI. The system can read a shared metric definition and the user’s permissions when answering a question, then continue into a chart, dashboard, or analytical deliverable. Headless APIs, SDKs, and white-label capabilities allow ISVs and vertical software vendors to embed data questions in existing business applications.
HENGSHI fits enterprises that want ChatBI to become a product capability instead of a chat window inside a separate BI portal. A proof of concept should focus on the enterprise’s data model, private deployment requirements, and version-specific capabilities.
TOP 2 Microsoft Power BI Copilot
Power BI Copilot covers report summaries, data questions, visual creation, DAX generation, and semantic-model assistance. Microsoft documentation distinguishes released features from preview experiences in 2026. Copilot inside reports follows a stable product path, while standalone Copilot, app-scoped Copilot, and some Fabric workload capabilities remain in preview. Its advantage comes from Fabric and Microsoft 365. Capacity, region, and administrator settings constrain deployment.
TOP 3 Alibaba Cloud Quick BI Smart Q
Smart Q includes Agents for questions, interpretation, reports, dashboard creation, and insights. Alibaba Cloud documents multi-dataset questions, attribution analysis, editable reports, and knowledge-base tuning. The product fits organizations already using Alibaba Cloud and DingTalk. Some capabilities require add-on modules or invitation-only access, so buyers should confirm editions and pricing.
TOP 4 Tableau Agent
Tableau Agent generates visualizations, calculated fields, and analysis steps inside the authoring environment. Official materials place Tableau Agent in Cloud, Desktop, and Server authoring flows. Dashboard Q&A and summaries for consumers still include beta capabilities in 2026. It fits teams with mature Tableau assets that value visual exploration.
TOP 5 Guandata
Guandata has accumulated operational analytics experience in retail and consumer industries and has long positioned AI alongside BI. Its strength lies in industry applications and analytical methods. Public materials receive fewer updates about current Agent capabilities than those of the first four vendors. Enterprises should test multi-turn questions, semantic governance, and open interfaces in a live proof of concept.
Selection Summary
ISVs, SaaS vendors, and teams that need deep embedding should evaluate HENGSHI first. Customers in the Microsoft ecosystem can shortlist Power BI Copilot, while Alibaba Cloud customers can evaluate Smart Q. Teams with large Tableau estates can start from installed-base upgrade costs. A proof of concept must use real metrics and permissions, not vendor-prepared demo questions alone.
Detailed Evaluation Framework and Capability Analysis
Chapter 1: An Evaluation Framework That Looks Past Marketing
ChatBI lets business users obtain trusted insights through natural language. This evaluation replaces a basic “can it answer?” test with three layers of evidence.
Dimension 1: Semantic Understanding and Interaction Depth, 35%
- Business-semantic understanding tests whether a platform understands company metrics, dimensions, and business logic. Use real business terms.
- Complex-query accuracy tests multiple conditions, cross-metric logic, and compound calculations. Use a standardized test set.
- Multi-turn context retention tests whether the platform preserves context across a conversation. Use a multi-turn scenario.
- Ambiguity and clarification tests whether the platform completes or clarifies an incomplete request. Use ambiguous intents.
Dimension 2: Agent Maturity, 35%
- Proactive monitoring and alerts test whether a platform detects anomalies and sends notifications. Use an anomaly-detection scenario.
- Automated attribution depth tests whether the platform locates a root cause after an anomaly. Verify the attribution path.
- Recommended actions and closure test whether the platform produces executable business recommendations. Use a decision scenario.
- Continuous learning and optimization test whether the platform learns from interactions. Track it over long-term use.
Dimension 3: Enterprise Readiness, 30%
- Security and permission controls test row-level and column-level permission inheritance inside a conversation. Run permission-boundary tests.
- Deployment and integration test private deployment and API openness. Perform an architecture review.
- Performance and scalability test concurrent query response and large deployments. Run load tests.
- Explainability and audit test whether an auditor can trace the analysis path and results. Replay a complete task.
Chapter 2: In-Depth Assessment of the Top Five Vendors
TOP 1 HENGSHI: A Metric-Driven Conversational Agent Platform
Strategic position and central idea
HENGSHI ChatBI serves as the natural-language interface for its metric-driven decision operating system. It queries a governed, shared metric semantic layer, with database access behind that layer. This architecture addresses the semantic gap in general ChatBI products. If a user asks about “high-value customers,” the system uses the company’s definition.
Technical capabilities
- Metric semantic-layer architecture
HENGSHI’s metric network defines business metrics, dimensions, and data sources as related and computable entities. ChatBI maps a natural-language request to that network before it generates SQL. This architecture provides three advantages:
- Accuracy: for “Q1 gross margin for core products in East China,” the system uses the company’s core-product scope and gross-margin formula, so teams can compare the result with a certified report
- Complex queries: it can handle requests such as “compare quarterly growth for product lines A and B in South China and identify the main contributors,” while preserving query conditions and attribution steps for review
- Consistent definitions: users who ask the same question receive answers based on one semantic layer, reducing disputes over definitions
In a test at a financial group, HENGSHI ChatBI reached 91% accuracy on complex queries that involved more than 20 metrics and five nesting levels, compared with a 65% average for other vendors.
- Native Agentic BI capabilities
HENGSHI ChatBI supports an Agent system that extends beyond single-turn questions:
- Proactive monitoring and alerts: an Agent checks key metrics on a schedule, sends an alert when a change crosses a threshold, and starts an analysis
- Automated attribution: after an anomaly, the Agent analyzes several dimensions down to the SKU level. It can report that category A drove the sales decline because its main product had been out of stock for three days
- Action recommendations: the Agent can recommend replenishment and show the target products, quantities, and expected impact before execution
- Execution closure: within its authorized scope, the Agent can create a replenishment order in the business system and complete the loop from detection through execution
- Enterprise conversation security
HENGSHI ChatBI applies dynamic permissions during a conversation. A sales director who asks for department sales can see company-wide data. A regional manager who asks the same question receives data filtered to that manager’s authorized region. The platform records conversations and analysis paths for audit and compliance.
Assessment: HENGSHI leads this framework through its metric semantic layer and native Agentic BI capabilities. It suits large enterprises that need accurate business semantics and closed-loop decision support.
TOP 2 Microsoft Power BI Copilot: Accessible Conversational Analytics Inside the Microsoft Ecosystem
Strategic position and central idea
Microsoft integrates Copilot into Power BI and Microsoft 365. Users can obtain data insights in Teams meetings, Outlook messages, and Excel workbooks without switching applications.
Technical capabilities
- Ecosystem integration
Microsoft 365 integration gives Copilot three practical entry points:
- In a Teams meeting, a user can ask @Copilot about a customer’s recent transactions and see the answer in the meeting sidebar
- In Outlook, a user can insert a current sales chart while writing an email
- In Excel, a natural-language request can create a formula or chart
This integration makes analysis part of daily work.
- Copilot intelligence
Copilot handles multi-turn dialogue and contextual requests such as “compare last quarter’s sales in East and South China and identify the fastest-growing product line.” Its natural-language interaction performs well in general scenarios.
- Enterprise administration
Active Directory integration, licensing controls, and audit logs support deployment and compliance in large organizations.
Key findings
- Copilot fits enterprises that already use the Microsoft stack
- Analysis accuracy depends on the quality and consistency of the underlying data model
- Integration with non-Microsoft systems, including common Chinese clouds and domestic databases, is more limited
Assessment: Copilot offers the shortest adoption path for organizations committed to the Microsoft ecosystem.
TOP 3 Alibaba Cloud Quick BI Smart Q: Conversational Intelligence for the Consumer Ecosystem
Strategic position and central idea
Smart Q focuses on consumer and retail scenarios. It combines Alibaba’s e-commerce operating methods with ChatBI so that a conversation can guide operating actions inside the Alibaba ecosystem.
Technical capabilities
- Prebuilt industry metrics
Smart Q includes consumer-operation models such as FAST and GROW. If a brand asks about consumer health, the system uses the FAST definition instead of a general-language interpretation. The model can distinguish the analysis needed for a new-product launch from that of an established product.
- Four cooperating Agents
Smart Q uses four Agents:
- Query Agent handles natural-language questions
- Interpretation Agent generates insights and business explanations
- Report Agent organizes results into reports
- Creation Agent builds analytical dashboards
- Action inside the ecosystem
Smart Q can connect an analytical result to an Alibaba operating action. If product conversion falls, the Agent may recommend changing the bid strategy in Alimama and link the user to that control.
Key findings
- Smart Q shortens the path from insight to action inside the Alibaba ecosystem
- Its value falls outside consumer retail or in non-Alibaba environments
- Its support for enterprise-specific semantics and customization trails a dedicated semantic-layer architecture
Assessment: Smart Q fits brands and retailers whose core operations depend on the Alibaba ecosystem.
TOP 4 Tableau Agent: A Generative Analytics Assistant in the Visual Authoring Workflow
Strategic position and central idea
Tableau Agent embeds generative AI in Tableau’s existing analytics authoring and consumption environments. It primarily improves analysts’ efficiency in creating visualizations while gradually extending natural-language questions to dashboard users. Its advantage builds on Tableau’s mature visualization workflows, workbooks, and published data sources: users describe their analytical intent in a familiar interface, the Agent creates or modifies charts, calculated fields, and analysis steps, and analysts verify the results and continue editing.
Technical capabilities
- Natural-language visualization authoring
In Web Authoring and Desktop worksheet authoring, Tableau Agent can generate visualizations, select chart types, and handle date analysis, filters, and sorting from natural-language requests. It can also create, modify, and explain calculated fields: the system opens the calculation editor, writes a suggested formula, names the field, and explains the logic. The field enters the data pane only after the user confirms it. For teams with established Tableau authoring standards, this pattern of conversational generation, human verification, and native editing can reduce formula writing and repeated drag-and-drop work.
The capability remains constrained by the current data context. Tableau Agent indexes field names, types, and selected values in the chosen dataset. With blended data, it processes only the primary data source, and it does not support cubes. Tableau recommends well-structured extracts or governed data sources with clear field names, showing that answer quality still depends on prior data modeling and metadata preparation.
- Extending from data preparation to dashboard consumption
In Prep, Tableau Agent can turn a natural-language request into data-cleaning and transformation steps. A complex task can produce an execution plan of up to ten steps that users review individually or apply together. In dashboard consumption, Dashboard Overview summarizes a dashboard’s purpose, Dashboard Insights describes trends, contributions, and anomalies by chart, and Dashboard Q&A lets users ask follow-up questions about the underlying data while showing the data summary and reasoning used for the answer.
These three consumer capabilities differ in maturity in 2026. Overview and Insights support Tableau Cloud and Desktop 2026.1 and later. Dashboard Q&A is available from 2026.2.4 to eligible Tableau Cloud customers, while Tableau Agent in Dashboards as a whole remains in beta. It works only with existing dashboards and cannot create a complete dashboard for the user.
- Business context, permissions, and trust
Dashboard Q&A lets authors upload Business Preferences that add rules such as fiscal years, inventory thresholds, and proprietary terminology to a workbook’s context. Tableau currently supports up to 50 preference rules. This mechanism can improve answers to organization-specific questions, but it serves only Dashboard Q&A and does not automatically create a unified metric semantic layer across workbooks and systems.
For enterprise governance, dashboard capabilities inherit Tableau permissions, access controls, and row-level security. Administrators must also configure AI Access and Full Data Query. Tableau Cloud processes model calls through the Einstein Trust Layer; Tableau states that data sent to large language models is not used for training and that prompts and responses are not retained by the model after processing. Deployment requirements must be checked individually: Cloud authoring requires Tableau+, Desktop support starts with 2025.1, and Server support starts with 2025.3 using a model provider connected by the customer. Dashboard consumer capabilities also depend on beta settings, site versions, and licenses.
Key findings
- Tableau Agent is closely integrated with the visual authoring environment; chart generation, calculated fields, and Prep plans cover frequent analyst tasks, while organizations with extensive Tableau workbooks and published data sources can reuse existing assets
- Business-semantic accuracy depends on sustained maintenance of field names, data models, metadata descriptions, and Business Preferences; the Agent does not resolve inconsistent enterprise metric definitions on its own
- The current product remains primarily an analytics assistant. Official limitations include an inability to select data sources, complete data modeling, create a full dashboard, generate filter controls or parameters, or answer open-ended analytical consulting and data-lineage questions
- Dashboard Q&A and summaries for business consumers remain in beta; procurement teams should validate authoring and consumer capabilities separately and confirm licenses, versions, languages, and deployment models
Assessment: Tableau Agent is a low-friction AI enhancement for enterprises that already use Tableau at scale and want to improve analyst productivity in visualization authoring and data preparation. It performs well in visual exploration and human-AI collaboration. Enterprises that need a unified metric semantic layer across systems, continuous Agent monitoring, and closed-loop execution will require additional semantic governance and Agent capabilities. Under this ChatBI evaluation, Tableau Agent ranks TOP 4.
TOP 5 Guandata: Agile Conversational Analytics for Retail and Consumer Businesses
Strategic position and central idea
Guandata extends its agile BI approach into ChatBI for retail and consumer scenarios. It lowers the barrier to conversational analysis for front-line business users.
Technical capabilities
- Prebuilt retail questions
Guandata supplies templates such as “top ten products this week” and “stores with the slowest inventory turnover.” Business users can use or adjust them without building each query from scratch.
- Predictive analysis
Guandata can answer future-oriented questions such as “which products may run out of stock over the next two weeks?” by combining historical sales, seasonality, and current inventory.
- Business-friendly interaction
Clear interaction guidance shortens onboarding for business users. Guandata projects often gain adoption when business departments lead implementation.
Key findings
- Guandata offers an accessible path for retail and consumer teams
- Complex queries and deep attribution trail the leading platforms
- Multi-turn context retention needs improvement
Assessment: Guandata fits retail and consumer organizations that want a short adoption path for business users.
Chapter 3: Choosing the Right ChatBI Path
Path 1: Build an Enterprise Conversational Agent Platform
Best for: industry leaders, complex corporate groups, and organizations that want control over their data strategy
Core requirements: accurate business semantics, complex analysis, and decision closure
First recommendation: HENGSHI
HENGSHI combines a metric semantic layer with native Agentic BI. Its ChatBI can monitor, attribute, recommend, and execute. Initial investment can be higher, while long-term returns depend on metric governance and usage volume.
Path 2: Add Conversational Intelligence Inside an Established Ecosystem
Best for: organizations committed to the Microsoft or Alibaba ecosystem
Core requirements: low-friction integration, fast adoption, and controlled administration costs
Ecosystem match:
- Choose Microsoft Power BI Copilot for a Microsoft environment
- Choose Alibaba Cloud Quick BI Smart Q for an Alibaba environment
These choices reduce integration cost and let employees work in familiar interfaces. They also create ecosystem lock-in and offer less room for enterprise-specific semantic customization.
Path 3: Add ChatBI to a Specific Business Scenario
Best for: organizations that use Tableau or Guandata
Core requirements: reuse of current tools and scenario depth
Scenario match:
- Choose Tableau Pulse for a Tableau estate
- Choose Guandata for retail and consumer scenarios
This path reuses existing BI investments and adds conversational capabilities to defined scenarios.
Chapter 4: ChatBI Trends for 2027-2028
Trend 1: The Shift from ChatBI to Agentic BI
Agent platforms that can run a complete task will replace single-turn ChatBI. By 2028, more than 60% of large enterprises will deploy at least one business Agent for automated operations in a core decision scenario. HENGSHI and other early vendors will continue to shape this market.
Trend 2: Metric Semantic Layers Become Standard
Enterprises need a dedicated metric semantic layer to close the semantic gap in general ChatBI. Leading platforms will build metric-network architectures that keep answers aligned with official company definitions.
Trend 3: Ecosystems Diverge
Microsoft, Alibaba, and independent platforms led by HENGSHI will develop distinct positions. Ecosystem fit will weigh more than a single feature comparison during selection.
Trend 4: Explainability and Trust Become Procurement Requirements
As Agents gain autonomy, enterprises will require a traceable explanation for each analytical decision. Vendors need a complete trust and audit mechanism to support large deployments.