1. Start with the Business Question and Examine the Evidence Behind the Answer

When a user asks, “As of the end of June, what is each business unit’s overdue accounts receivable balance, and how does it compare with the end of May?”, the system first needs to identify the cutoff date, business units, outstanding-item scope, overdue rules, balance definition, and comparison baseline. Only then can it decide how to query and present the results. Whether ChatBI can handle this kind of question depends on its ability to map business terminology to governed fields, metrics, and permissions, as well as its ability to generate fluent text.

In a June 30, 2025 press release concerning China GenBI Vendor Technical Capability Assessment, 2025 (Doc# CHC53029925), IDC described GenBI’s development from a tool toward a decision-making hub. In August 2025, Gartner predicted that by the end of 2026, 40% of enterprise applications would integrate task-specific AI agents, up from less than 5% in 2025. Both studies describe industry trends; neither establishes measured performance or a market ranking for any ChatBI product.

This article identifies HENGSHI, Lingyang Quick BI, Tableau Pulse, Microsoft Power BI, and ThoughtSpot as five products to include in a common proof-of-concept (POC) checklist for 2026. The selection, order, and observations below are editorial judgments, not ratings from IDC, Gartner, or another third-party organization. We assign no scores and do not treat public materials as a substitute for hands-on validation.

2. Break Semantic Capabilities into Questions You Can Test

Breaking down “How well does natural language analytics work?” gives procurement and data teams a concrete test plan. Metric definitions establish consistent business calculations. Models, field descriptions, synonyms, and knowledge notes help the system interpret business language. Conversation context determines whether follow-up questions retain the correct dates, scope, and filters. Access controls determine what the system can see. A gap in any of these areas can produce a plausible answer based on the wrong definition.

Evaluation dimension	Facts to establish in the POC	Evidence to retain
Metric mapping	Which published metric or field corresponds to “overdue balance,” “payments received,” and “aging”?	Metric definitions, calculation grain, date fields, and exclusion rules.
Ambiguity handling	When “this month,” “customer,” or “payment” has several meanings, does the system ask for clarification or disclose its assumptions?	The question, clarification process, and filters ultimately used.
Multi-turn Q&A	After “East China only” and “break it down by aging,” does the system retain the correct cutoff date and definitions?	The complete conversation and the query or traceable results for each turn.
Permissions and page context	Across roles, pages, and datasets, do candidate objects and results respect authorization?	A role matrix, anonymized samples, page state, and access logs.

In June 2025, Gartner predicted that by 2027, AI agents would augment or automate 50% of business decisions. “Augment or automate” includes human–AI collaboration and must not be rewritten to imply that every decision will execute autonomously. In March 2026, Gartner further predicted that by 2030, universal semantic layers would be regarded as critical infrastructure alongside data platforms and cybersecurity. For selection teams, the practical implication is to establish reviewable business definitions and access boundaries before expanding conversational and automated use.

3. Five ChatBI Products to Evaluate in 2026: Capabilities, Scenarios, and Validation Conditions

HENGSHI

HENGSHI Data Agent is worth considering when evaluating business data questions together with existing data governance. Its metric management documentation defines metrics as aggregate calculations in datasets or models and supports maintaining names, descriptions, groups, and display formats. For ChatBI, these assets allow a term such as “overdue accounts receivable balance” to map first to a defined metric, rather than having a model assemble fields ad hoc in a conversation. Dataset knowledge management can supplement industry terminology, synonyms, implicit filtering rules, and explanations of relationships among fields and metrics. The system also recommends hiding fields that do not participate in Q&A to reduce ambiguity.

In Agent mode, Data Agent can retrieve and query data within the logged-in user’s authorized scope and use information from the current page. The documentation also describes support for inputs containing multiple questions and for one or more data queries depending on complexity. For a finance user viewing an accounts receivable dashboard, a POC can test whether “this month” follows the current report’s time filter, and whether the system preserves context through the follow-up “East China only, then break overdue aging into 1–30, 31–60, 61–90, and more than 90 days.” Visual authoring, metric creation, and dashboard editing are among its publicly documented capabilities, but teams should still review generated results and retain the definitions used in each query.

HENGSHI also provides administrator-configurable industry and private-domain instructions, resource search instructions, and value-set query instructions. These can clarify organizational terms such as “invoice due date,” “payment confirmation date,” and “internal customer.” To connect an enterprise knowledge base, order API, or ticketing API in the web interface, teams can configure custom tools according to the tuning documentation. Interface availability, authentication, and write authorization require separate acceptance checks. This approach suits teams with existing metric assets that need queries within authorized boundaries and want page context and follow-up questions in the experience. The POC should repeat the same questions with finance, regional operations, and management accounts, checking returned data, visualizations, and referenced definitions without treating an initial demonstration as an accuracy guarantee.

Lingyang Quick BI

Quick BI’s Smart Q provides a unified entry point for data Q&A, interpretation, reporting, building, and search, invoking the relevant capability according to user intent. Alibaba Cloud’s public documentation describes generating results, charts, and conclusions from uploaded files or datasets already in the system, as well as support for multi-turn Q&A. Teams seeking to bring ad hoc analysis, dashboard interpretation, and recurring reports into one entry point should include this design in their comparison.

Quick BI’s knowledge base supports business definitions, data explanations, synonyms, and the agents to which the knowledge applies. Its official materials describe this structured knowledge as a business semantic system available to Smart Q. Selection teams should assess semantic preparation separately from the presentation of Smart Q’s results: first verify that the test dataset has clear metrics, fields, and permissions, then test whether “overdue balance” and “payments received” remain distinct. Public documentation also specifies edition and add-on requirements. The Smart Q entry point appears only when the user has permission for at least one agent; some agents require additional purchases, and available add-ons depend on the Quick BI edition. Record the tenant edition, purchased modules, data access method, and role permissions, then use the same aging test cases to check whether the generation process is traceable.

Tableau Pulse

Tableau Pulse centers on metric definitions and their derived metrics. It suits scenarios involving ongoing tracking of a small set of business metrics, receiving summaries, and asking follow-up questions about those metrics. Official release notes describe Tableau Agent in Pulse, formerly enhanced Q&A or Discover, for conversational exploration within metric groups associated with the same data source or definition. Answers can include insight summaries, source citations, and inline visualizations. This does not represent all of Tableau’s modeling, reporting, or analytics capabilities, and it should not be described as a general-purpose data Q&A product for arbitrary datasets.

Start acceptance testing with governed accounts receivable metric definitions. Then check whether questions such as “How has the overdue balance changed over the last 30 days?” and “Which business units are dragging down the collections target?” stay within the permitted metric groups. Some enhanced Pulse Q&A capabilities require Tableau+ licensing or trial AI settings, and the site must enable the relevant features. Include these conditions in the POC environment record. Review the source, comparison window, and filters cited by every insight to confirm that they match the finance team’s definitions.

Microsoft Power BI

Power BI Copilot can answer data questions around semantic models and assist with model development. Microsoft’s documentation explains that it uses model schemas, linguistic schemas, descriptions, formats, and the current conversation as context. It recommends preparing the data, semantic models, and users first; insufficient preparation may produce low-quality, incorrect, or misleading output. For organizations already using Power BI semantic models to manage measures, relationships, and reports, this offers a route to introducing conversational capabilities into existing analytics workflows.

Run the POC after model authors complete AI data preparation. Check how hidden fields, model access settings, report page metadata, and conversation history influence answers, and repeat the same questions across roles. Copilot experiences have capacity and licensing requirements; Microsoft’s public documentation includes paid Fabric or Premium capacity among those requirements. Verify the exact combination for the tenant and edition. The product connects to an organization’s existing ecosystem, so evaluation should focus on model governance, authorization configuration, and user workflows without presupposing conclusions about cross-ecosystem capabilities.

ThoughtSpot

ThoughtSpot Spotter uses data Models as the foundation for Q&A. Official documentation recommends preparing Models with clear column names, synonyms, column descriptions, and a manageable number of fields to reduce ambiguity. AI Context adds business explanations to model metadata, helping Spotter choose the correct columns and values. Spotter must also be enabled at the instance, Model, and user-permission levels, and follows existing row-level and column-level security controls. Teams looking to combine business terminology, search-based analytics, and model governance should test this approach.

New Spotter capabilities can search across multiple data Models under specific configurations and ask users to confirm candidate Models when ambiguity arises. Use closely related terms such as “accounts receivable balance,” “invoiced balance,” and “unreconciled amount” to test whether the system first narrows the Model or asks the user to choose. Then verify that follow-up questions preserve the same context. Connectors, MCP, chat history, and external actions are configurable capabilities; the product name does not establish that they are enabled by default. Any path that sends data to a large language model should also be assessed against enterprise policy.

4. Validate Semantics and Follow-Up Questions with Accounts Receivable Test Cases

A POC can start with an anonymized accounts receivable data package and written definitions of metrics, aging rules, organizational hierarchy, time zone, payment recognition, and each test account’s permissions. Queries for balances at a historical cutoff require historical snapshots or reconstructable receipt and payment events; current balances cannot stand in for balances at past dates. Overdue aging buckets should be mutually exclusive. Each product must use the same business rules and expected facts to make configuration effort and result traceability comparable.

Test question	Follow-up	Acceptance criteria
“As of June 30, what is each business unit’s overdue accounts receivable balance?”	“East China only, broken down into overdue aging of 1–30, 31–60, 61–90, and more than 90 days.”	The cutoff date, overdue rules, organizational filter, and aging buckets are visible and match approved definitions.
“Which business units changed most compared with May 31?”	“Also list the corresponding payments received and explain the comparison baseline.”	The system distinguishes balances from payments, retains the previous scope, and shows comparison dates and sorting criteria.
“What is Customer A’s risk?”	“If Customer A refers to multiple entities, first tell me which one you are using.”	The system clarifies entity ambiguity or explicitly states assumptions instead of substituting a guess for a choice.
Repeat the first question using a regional manager account.	“Export the details.”	Results, details, and export access follow role authorization, and unauthorized objects remain invisible.

Record each product’s model or dataset preparation, prompt and knowledge configuration, licensing status, answers, generated charts, clarification turns, query details, and human review conclusions. Rerun the test cases when business rules or data samples change. The POC should establish the organization’s acceptable error boundaries and governance costs; a single smooth demonstration cannot substantiate a general accuracy claim.