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AI can help answer questions about structured database and business-intelligence data, but it is not a dependable authority by default. A generated query can run successfully and still answer the wrong business question. Treat AI as an assistant: check what data and context it can access, inspect how it reached its answer, and verify the result against the question you meant to ask.
What does “answer questions about your data” mean?
For the tools discussed here, it means turning natural-language questions into queries against structured databases or business-intelligence models. It does not establish that an assistant can reliably answer questions across every private file, document store, or type of data.
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These tools are real capabilities, not just a promised future. Microsoft documents Copilot in SQL Server Management Studio (SSMS) for database help and natural-language assistance generating T-SQL. Power BI Q&A accepts natural-language questions about semantic models. Google Cloud documents conversational data agents for selected sources. But they differ in what they can see, which configurations they support, and what kinds of questions they can answer.
Can you trust an AI-generated answer?
Not without checking it. Microsoft’s Copilot in SSMS transparency note warns: “The queries and responses generated might not be completely accurate, and when queries are executed they might not provide the results the user expected or intended to receive.” Google Cloud likewise recommends validating generated output. These are practical warnings: fluent wording and a successful query execution do not prove that the result is correct or relevant.
There are two separate questions to verify:
- Did the query calculate what it says it calculated? Check the query, source tables or model, filters, joins, date ranges, and aggregation.
- Does that calculation answer the business question? Confirm that the tool interpreted the terms, comparison, and time period as you intended.
A query may be syntactically valid and return plausible numbers while using the wrong definition of “active customer,” omitting a relevant source, or choosing an unintended date boundary. Technical validity and business correctness are not the same thing.
What current tools can—and cannot—see
“AI data assistant” is not one uniform product capability. The following examples have different scopes; the limits listed are the ones documented for those specific products, not universal limits for AI analytics.
| Tool | Documented scope | Important boundary |
|---|---|---|
| Microsoft Copilot in SSMS | Database help and natural-language assistance generating T-SQL. | Microsoft says Copilot has database-object schema context but not access to the underlying data. The documentation says English input is supported. |
| Power BI Q&A | Natural-language questions over semantic models. | Supported configurations vary; Microsoft documents unsupported model configurations and filter statements. Check the current documentation for the model in use. |
| Google Cloud Conversational Analytics API | Conversational data agents for selected data sources and query types. | Documented limits include source-specific row and processing caps, an output-token cap, and a warning that large result sets may reduce reasoning accuracy. |
For the Google Cloud API specifically, its documentation lists a maximum of 5,000 returned rows for Looker data-source queries; 1,000 rows for AlloyDB, Cloud SQL for MySQL, Cloud SQL for PostgreSQL, and Spanner; 500 GB processed for BigQuery data queries; and an output limit of 8,192 tokens. Google also cautions that large result sets may reduce reasoning accuracy. These figures apply to that API’s documented query paths, not to other products or AI data tools generally.
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Why a correct-looking query can still be wrong
Natural language leaves room for interpretation. A request such as “show revenue growth last quarter” may depend on which revenue measure counts, what “last quarter” means for the organization, whether refunds are included, and which comparison period is intended. If the schema does not encode those definitions clearly—or the assistant does not have access to the relevant context—it may make assumptions rather than ask.
Google Cloud’s engineering guidance recommends grounding generation in relevant schema, examples, and business rules; asking for clarification when a request is ambiguous; checking whether the question is answerable from the available schema and data; and complementing generation with query parsing or dry runs. Comparing multiple generated candidates for consistency is another described technique. These methods can improve a workflow, but none guarantees the intended answer.
Evaluation also has limits. Apple Machine Learning Research identifies weaknesses in text-to-SQL benchmark practices: test questions may not capture the ambiguity and probabilistic nature of natural language, while approximate SQL-equivalence matching can introduce bias. A benchmark score therefore cannot establish that a tool will understand every organization’s terminology or answer every production question correctly.
How to test an AI assistant on your own data
Before relying on answers for operational or business decisions, build a test set from the questions real users ask and have data owners agree on the intended answers. Score whether each response answers the intended question—not just whether its query runs.
- Choose representative questions. Include common requests from the actual audience, phrased as users would ask them rather than as SQL exercises.
- Write down the expected interpretation. For each question, specify the business metric, relevant data, filters, comparison, and time period. Record acceptable answers or a trusted reference result.
- Probe ambiguity and boundaries. Test business terms with multiple meanings, date boundaries, joins, aggregation, missing data, and questions that the available schema cannot answer.
- Check permissions and scope. Verify what sources the assistant can query, whether it honors the connected user’s access, and what happens when a question asks for data outside those permissions.
- Inspect the path to the answer. Where available, review the generated query and result. Confirm that the selected fields, tables, filters, joins, and calculations match the expected interpretation.
- Track failure modes, not just a single score. Record wrong answers, unsupported questions, unclear responses, and cases where the assistant should have asked for clarification or abstained.
- Repeat after changes. Re-test when the schema, semantic model, business definitions, permissions, or assistant configuration changes.
Compare candidate systems on their supported sources and query types, business-context grounding, clarification and abstention behavior, answer traceability, permission enforcement, output and scale limits, and results on your own test questions. This is a practical evaluation framework, not a universally mandated protocol.
What published studies do—and do not—establish
A 2026 paper in the ACM HILDA workshop proceedings, recorded by Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), reports a study with 80 participants who produced 887 natural-language queries while answering broad questions with a database. The paper also notes that benchmark questions created by paraphrasing SQL may remain more SQL-like than questions from non-expert users. That makes realistic user phrasing important when evaluating an assistant for a particular organization.
Those findings do not establish a universal accuracy rate or prove that AI is ready—or unready—for every data source and organization. Vendor documentation describes product capabilities and cautions; benchmark and user-question studies address evaluation and question design, not reliability across all production deployments. The evidence supports a narrower conclusion: AI can assist with scoped structured-data questions, but trust depends on the data, schema, business context, permissions, question, and verification process.
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